@article {pmid41337804,
year = {2025},
author = {Tang, J and Feng, D and Guan, L and Xu, Z and Zou, Y},
title = {Dissecting the atomic effects of S305 phosphorylation on the aggregation of the core tau peptides of the first intermediate filament.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {259},
number = {},
pages = {115326},
doi = {10.1016/j.colsurfb.2025.115326},
pmid = {41337804},
issn = {1873-4367},
abstract = {Tauopathies, containing aging-related Alzheimer's disease (AD) and contact sports-related chronic traumatic encephalopathy (CTE), etc., are neuropathologically characteristic of the tau protein aggregates in the brain. Targeting the oligomeric species formed in the route of tau fibrilization has been considered a promising therapeutic approach to prevent or treat tauopathies. In vitro experiment reported that S305 phosphorylation (Pho-S305) exerted a protective role in tau aggregation. However, the atomic effect and molecular mechanisms of Pho-S305 on tau aggregation are largely elusive. In this study, we performed replica exchange molecular dynamics (REMD) and classical molecular dynamics (MD) simulations, with a total simulation time of 57 μs, on tau peptides without and with Pho-S305. The protein model was a novel tau fragment (G302-S316), constituting the ordered core of a first intermediate amyloid (FIA) filament of AD- and CTE-specific tau filament. The REMD results revealed that Pho-S305 suppressed the β-sheet formation and weakened the peptide-peptide interaction, thus inhibiting the aggregation of the peptides. Additional MD simulations indicated that the oligomerization dynamics of the peptides were disturbed by Pho-S305. Our findings excavate the mechanistic information underlying the phosphorylation-induced inhibitive effects on tau302-316 aggregation, which may provide potential useful clues for the development of therapeutic avenues for tauopathies.},
}

@article {pmid41337685,
year = {2026},
author = {Gasparini, F and Valletta, M and Vetrano, DL and Beridze, G and Rizzuto, D and Calderón-Larrañaga, A and Fredolini, C and Dale, M and Winblad, B and Fratiglioni, L and Grande, G},
title = {Kidney Function, Alzheimer Disease Blood Biomarkers, and Dementia Risk in Community-Dwelling Older Adults.},
journal = {Neurology},
volume = {106},
number = {1},
pages = {e214446},
doi = {10.1212/WNL.0000000000214446},
pmid = {41337685},
issn = {1526-632X},
mesh = {Humans ; Female ; Male ; Biomarkers/blood ; Aged ; *Alzheimer Disease/blood/epidemiology ; *Glomerular Filtration Rate/physiology ; tau Proteins/blood ; Amyloid beta-Peptides/blood ; Independent Living ; Aged, 80 and over ; Longitudinal Studies ; Sweden/epidemiology ; *Dementia/blood/epidemiology ; Neurofilament Proteins/blood ; Cross-Sectional Studies ; Glial Fibrillary Acidic Protein/blood ; Peptide Fragments/blood ; },
abstract = {BACKGROUND AND OBJECTIVES: Impaired kidney function has been linked to altered concentrations of blood biomarkers of Alzheimer disease (AD), but the underlying mechanisms and its potential role in dementia development remain poorly understood. We explored the associations between estimated glomerular filtration rate (eGFR), blood-based biomarkers of AD, and dementia development.

METHODS: Data were extracted from the Swedish National Study on Aging and Care in Kungsholmen, an ongoing longitudinal population-based study. Kidney function was assessed using eGFR based on serum creatinine. AD biomarkers (amyloid beta [Aβ42/40], phosphorylated tau [p-tau181 and p-tau217] and total tau [t-tau] proteins, neurofilament light chain [NfL], and glial fibrillary acidic protein [GFAP]) were measured from peripheral blood samples using the Simoa platform. Dementia was diagnosed according to DSM-IV criteria. Quantile regression models assessed the cross-sectional associations between eGFR and AD biomarkers; Cox regression models were used to examine the association of kidney function and biomarkers with incident dementia.

RESULTS: At baseline, 2,279 dementia-free participants with available blood samples were included (median age 72 (interquartile range, 61-81) years; 62% female). Lower eGFR was associated with higher median z-score levels of all examined AD blood biomarkers, except Aβ42/40, following a nonlinear relationship. At eGFR = 30 mL/min/1.73 m[2], estimated differences were as follows: p-tau181: β, 0.22 [95% CI 0.09-0.35]; p-tau217: β, 0.20 [95% CI 0.10-0.31]; t-tau: β, 0.24 [95% CI 0.05-0.42]; NfL: β, 0.88 [95% CI 0.80-0.95]; GFAP: β, 0.10 [95% CI 0.03-0.16]. During a mean follow-up period of 8.3 (SD, 4.3) years, 362 participants developed dementia. In multivariable-adjusted models, impaired kidney function (eGFR < 60 mL/min/1.73 m[2]) was not associated with an increased hazard of dementia compared with preserved kidney function (eGFR ≥ 60 mL/min/1.73 m[2]) (hazard ratio [HR], 0.93 [95% CI 0.72-1.21]). The relationship between increased (high vs low) NfL and dementia was stronger among individuals with impaired (vs preserved) kidney function (HR, 3.85 [95% CI 1.87-7.95] vs HR, 1.84 [95% CI 1.34-2.53], respectively).

DISCUSSION.: Impaired kidney function was associated with elevated circulating level of most AD blood biomarkers. However, the presence of impaired kidney function did not independently increase the risk of dementia but rather seemed to accelerate the clinical expression of underlying neurodegenerative pathology.},
}

Humans
Female
Male
Biomarkers/blood
Aged
*Alzheimer Disease/blood/epidemiology
*Glomerular Filtration Rate/physiology
tau Proteins/blood
Amyloid beta-Peptides/blood
Independent Living
Aged, 80 and over
Longitudinal Studies
Sweden/epidemiology
*Dementia/blood/epidemiology
Neurofilament Proteins/blood
Cross-Sectional Studies
Glial Fibrillary Acidic Protein/blood
Peptide Fragments/blood

@article {pmid41337541,
year = {2025},
author = {Weerakkody, TN and Sabelström, H and Andrews, SV and Chadarevian, JP and Chin, MY and Tatarakis, D and Propson, NE and Kim, DJ and Theolis, R and Parico, GCG and Misker, H and Kung, JE and Bandyopadhyay, A and Robles Colmenares, Y and Jackson, TN and Qerqez, AN and Balasundar, S and Davis, SS and Ha, C and Ghosh, R and Ravi, R and Rana, A and Germain, K and Tao, A and Xiong, K and Braun, D and Raju, K and Huang, KC and Zhan, L and Guo, JL and Safari Yazd, H and Sarrafha, L and Capocchi, JK and Hasselmann, J and Chadarevian, AL and Tu, C and Mansour, K and Eskandari-Sedighi, G and Tesi, N and van der Lee, S and Hulsman, M and Oshegov, G and Pijnenburg, Y and Calvert, M and Holstege, H and Suh, JH and Di Paolo, G and Davtyan, H and Lewcock, JW and Blurton-Jones, M and Monroe, KM},
title = {Loss of PILRA promotes microglial immunometabolism to reduce amyloid pathology in cell and mouse models of Alzheimer's disease.},
journal = {Science translational medicine},
volume = {17},
number = {827},
pages = {eadw7428},
doi = {10.1126/scitranslmed.adw7428},
pmid = {41337541},
issn = {1946-6242},
mesh = {*Alzheimer Disease/pathology/metabolism/immunology ; Animals ; *Microglia/metabolism ; Humans ; Disease Models, Animal ; Mice ; Mice, Knockout ; *Receptors, Immunologic/metabolism/deficiency/genetics ; *Amyloid/metabolism ; Mitochondria/metabolism ; Induced Pluripotent Stem Cells/metabolism ; Inflammation/pathology ; },
abstract = {The Alzheimer's disease (AD) genetic landscape identified microglia as a key disease-modifying cell type. Paired immunoglobulin-like type 2 receptor alpha (PILRA) is an immunoreceptor tyrosine-based inhibitory motif domain-containing inhibitory receptor, expressed by myeloid cells such as microglia. The known protective PILRA G78R gene variant reduces AD risk in apolipoprotein E4 (APOE4) carriers and is enriched in a cohort of healthy centenarians. However, mechanisms underlying protective effects in microglia are undefined. Here, we identified biological functions of PILRA in human induced pluripotent stem cell-derived microglia (iMG) and chimeric AD mice. PILRA knockout (KO) in iMG rescued ApoE4-mediated immunometabolic deficits and prevented lipotoxicity through increased lipid storage, improved mitochondrial bioenergetics, and antioxidant activity. PILRA KO also enhanced microglial chemotaxis and attenuated inflammation. With pharmacological inhibitor studies, we showed that peroxisome proliferator-activated receptor and signal transducer and activator of transcription 1/3 mediated PILRA-dependent microglial functions. AD mice transplanted with human PILRA KO microglia exhibited reduced amyloid pathology and rescued synaptic markers. A high-affinity ligand blocking PILRA antibody phenocopied PILRA KO iMG. These findings suggest that PILRA is a pharmacologically tractable therapeutic target for AD.},
}

*Alzheimer Disease/pathology/metabolism/immunology
Animals
*Microglia/metabolism
Humans
Disease Models, Animal
Mice
Mice, Knockout
*Receptors, Immunologic/metabolism/deficiency/genetics
*Amyloid/metabolism
Mitochondria/metabolism
Induced Pluripotent Stem Cells/metabolism
Inflammation/pathology

@article {pmid41337426,
year = {2025},
author = {Li, J and Gao, Y and Yang, P and Guan, Z and Wang, T and Ma, G and Lei, B},
title = {Multi-Modal Feature Fusion Using Transformer for Early Alzheimer's Disease Diagnosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253151},
pmid = {41337426},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/diagnostic imaging ; Humans ; Deep Learning ; Algorithms ; Early Diagnosis ; },
abstract = {Alzheimer's disease (AD) is a common neurodegenerative disorder. Early and accurate diagnosis of AD is essential for effective treatment. However, due to the class imbalance problem, there is a significant data gap between different categories. Moreover,the data feature differences of AD are relatively small, which poses challenges for its application in the early diagnosis of AD. To tackle these problems, we propose an intelligent early AD diagnosis model based on Transformer. The deep learning diagnosis model utilizes Transformer to integrate image features and non-image features. Furthermore, it incorporates a class imbalance loss function to optimize the performance of early AD diagnosis, thereby enhancing the model's ability to recognize underrepresented classes. In order to alleviate the problem of class imbalance and test the model performance, we used stratified 5-fold cross validation to verify the model effect.Experimental results demonstrate that our model can significantly improve the accuracy of AD diagnosis, which is markedly better than traditional methods. Additionally, loss function we used more effectively mitigates the problem of class imbalance. We believe this work can effectively reduce the burden on medical staff to diagnose early AD.},
}

*Alzheimer Disease/diagnosis/diagnostic imaging
Humans
Deep Learning
Algorithms
Early Diagnosis

@article {pmid41337392,
year = {2025},
author = {Hamrick, WC and King, TJ and Olsen, CD and Nelson, AE and Mauchley, M and Curd, BC and Cliatt Brown, C and Frost, NA and Iversen, MM and George, JA},
title = {Improved Prediction of Activities of Daily Living from Wrist Electromyography Using Intermediate Gesture Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11252749},
pmid = {41337392},
issn = {2694-0604},
mesh = {Humans ; *Activities of Daily Living ; *Electromyography/methods ; *Wrist/physiology ; *Gestures ; Algorithms ; Male ; Female ; },
abstract = {The long-term goal of this research is to automatically, remotely, and noninvasively identify Activities of Daily Living (ADLs) using muscle activity at the wrist. Alzheimer's Dementia is a widespread condition affecting patients' memory and ability to care for themselves. A wrist-worn device capable of monitoring precise ADLs could increase patient independence and provide caretakers with detailed information regarding the patient's wellbeing. Here, we investigate the use of an intermediate hand gesture classifying algorithm to predict ADLs using wrist electromyography (EMG). We show that predicted gestures are well-represented within the span of ADLs, and that the repertoire of predicted gestures appears distinct among ADLs. Importantly, we show that a simple linear discriminant analysis of predicted gestures provides better and more efficient classification accuracy relative to a state-of-the-art neural network that predicts ADLs directly from EMG. Accurate and efficient classification of ADLs from a wrist-worn device can provide a foundation for remote monitoring of patients in a socially acceptable formfactor. More broadly, a better understanding of human behavior via ADL tracking can enable new assistive technologies that improve quality of life.Clinical Relevance-Monitoring activities of daily living with an electromyographic smart watch can provide insights into patient behavior and wellbeing.},
}

Humans
*Activities of Daily Living
*Electromyography/methods
*Wrist/physiology
*Gestures
Algorithms
Male
Female

@article {pmid41337360,
year = {2025},
author = {Lee, HP and Arginteanu, T and Kudela, P and Wyse-Sookoo, K and Anderson, WS and Salimpour, Y},
title = {Phase-Dependent Neuromodulation in a Computational Hippocampal Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254724},
pmid = {41337360},
issn = {2694-0604},
mesh = {*Hippocampus/physiology ; Humans ; *Models, Neurological ; Computer Simulation ; Theta Rhythm/physiology ; Neurons/physiology ; Animals ; },
abstract = {The critical role of phase-amplitude coupling (PAC) between oscillations of differing frequencies highlights the promise of phase-dependent neuromodulation as a therapeutic strategy for various neurological conditions. In the hippocampus, theta-gamma PAC is linked to key memory processes and information transfer. Computational models avoid technical challenges in in vivo and in vitro experiments and offer a practical alternative for exploring the mechanisms behind phase-dependent effects. In this study, we built on a published CA3 hippocampal computational model implemented in the NEURON-Python environment. We used a closed-loop autoregressive (AR) forward prediction model that sampled the network's local field potential (LFP) to achieve real-time calculation of stimulus time points locked to a target phase of the theta oscillation. Our approach enabled the delivery of current injections to all neuronal populations at either the peak or the trough of the theta rhythm. Analysis of the resulting network LFP showed distinct phase-dependent changes in the theta band during stimulation. The peak-phase stimulation significantly enhanced theta-gamma coupling. Further study on a large-scale human-based model is needed to better capture these phase-dependent effects. Overall, the results underscored the importance of closed-loop stimulation systems and the potential of phase-targeted neuromodulation to influence PAC. These findings offer new avenues for treating disorders marked by disrupted oscillations, including Alzheimer's disease and other memory disorders.Clinical Relevance- This study provides investigations of the origins of neuronal oscillations and the development of a brain stimulation technique for modulating the level of oscillations, possibly contributing to the development of novel treatment methods for neurological disorders associated with abnormal oscillations..},
}

*Hippocampus/physiology
Humans
*Models, Neurological
Computer Simulation
Theta Rhythm/physiology
Neurons/physiology
Animals

@article {pmid41337354,
year = {2025},
author = {Kang, MK and Elisabet, A and Hong, KS},
title = {Time-frequency deep metric learning of resting-state fNIRS signals for staging Alzheimer's disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254651},
pmid = {41337354},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/diagnosis/diagnostic imaging/physiopathology ; Spectroscopy, Near-Infrared/methods ; *Deep Learning ; Male ; Aged ; Female ; Cognitive Dysfunction ; Rest ; Signal Processing, Computer-Assisted ; },
abstract = {This study proposes a deep metric learning framework designed to embed resting-state functional near-infrared spectroscopy (fNIRS) signals into a feature space using a continuous wavelet transform layer in the time-frequency domain for classifying stages of Alzheimer's disease (AD). Early AD pathology, particularly working memory impairment, is closely linked to changes in frontal-lobe networks. Classifying AD patients based on the low-frequency components (0.008-0.15 Hz) of resting-state fNIRS signals presents a significant research challenge. We collected resting-state fNIRS data from individuals with AD, mild cognitive impairment (MCI), and healthy controls (HCs). Due to the limited size of the dataset and the challenges in quantifying individual signal characteristics, conventional data-intensive deep learning models show constrained performance and generalizability. To address these limitations, our proposed architecture generates robust signal embeddings and evaluates inter-sample similarity using a learned distance metric. Experimental results demonstrate high classification accuracy between AD and HCs in the time- frequency domain. Additionally, our findings indicate that i) frequency-domain metric learning effectively captures brain-signal complexity, and ii) differences in inter-regional activation play a crucial role in the progression of neurodegeneration.},
}

Humans
*Alzheimer Disease/diagnosis/diagnostic imaging/physiopathology
Spectroscopy, Near-Infrared/methods
*Deep Learning
Male
Aged
Female
Cognitive Dysfunction
Rest
Signal Processing, Computer-Assisted

@article {pmid41337319,
year = {2025},
author = {Walsh, A and Shanker, S and Valdes, AL},
title = {Classifying Neurodegenerative Diseases from Selected Temporal EEG Electrodes: Towards Ear-EEG Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254741},
pmid = {41337319},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/instrumentation/methods ; Electrodes ; *Alzheimer Disease/diagnosis/physiopathology ; Male ; Female ; Aged ; *Neurodegenerative Diseases/diagnosis/physiopathology/classification ; *Ear ; Signal Processing, Computer-Assisted ; Middle Aged ; Machine Learning ; Frontotemporal Dementia/diagnosis/physiopathology ; },
abstract = {Dementia is the seventh leading cause of death globally, with over 55 million people affected. As there is no cure to dementia, early detection is essential. Machine learning applied to EEG has been proposed for enabling early dementia detection, with multiple studies demonstrating successful binary classification of dementias. As a step towards the use of a more accessible tool, ear-EEG, this study investigates the extent to which two common forms of dementia, Alzheimer's Disease (AD) and Fronto-temporal Dementia (FTD), can be classified from temporal electrode EEG using a multiclass classification approach. EEG recordings from 88 participants (29 control, 36 AD and 23 FTD) at rest with eyes closed were used for classification. Features extracted from temporal electrodes (T3, T4, T5, T6, F7, F8) included sub-band power characteristics and band power ratios. Binary classification of healthy vs. AD achieved 81.8% accuracy, while multiclass classification of healthy, AD and FTD achieved 63.8% accuracy. The low accuracy in multiclass classification suggests challenges in differentiating between types of dementia using the proposed features.Clinical relevance- Our results demonstrate the potential of temporal EEG data in developing dementia classification models, highlighting the promise of more accessible tools such as ear-EEG for brain health monitoring. Further advancement of such tools and improvement of multiclass classification models could facilitate early detection of dementia, enabling timely interventions and improved patient outcomes.},
}

Humans
*Electroencephalography/instrumentation/methods
Electrodes
*Alzheimer Disease/diagnosis/physiopathology
Male
Female
Aged
*Neurodegenerative Diseases/diagnosis/physiopathology/classification
*Ear
Signal Processing, Computer-Assisted
Middle Aged
Machine Learning
Frontotemporal Dementia/diagnosis/physiopathology

@article {pmid41337291,
year = {2025},
author = {Wu, K and Sarrigiannis, PG and Blackburn, DJ and He, F},
title = {Enhanced Functional Connectivity for EEG Classification with a Modified Maximum Entropy Model: a Case Study of Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253453},
pmid = {41337291},
issn = {2694-0604},
mesh = {*Alzheimer Disease/physiopathology/diagnosis ; Humans ; *Electroencephalography/methods ; Entropy ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiopathology ; },
abstract = {Accurate characterization of functional connectivity (FC) in neural signals is critical for investigating a wide range of brain disorders and cognitive processes. Conventional approaches typically rely on correlation-based metrics to capture pairwise interactions; however, such measures may fail to capture more global patterns of connectivity. Pairwise Maximum Entropy Models (pMEM) a complementary global perspective by jointly modeling all pairwise interactions, but they can diverge significantly from traditional linear FC measures and are often prone to numerical instability. In this study, we propose a penalized pMEM framework that incorporates correlation-based FC into pMEM as a prior constraint, aiming to balance the global coupling captured by pMEM with the interpretability and simplicity of correlation-based approaches. By regulating the adherence of pMEM parameters to traditional FC, our method achieves more stable learning and better preserves key linear relationships. We evaluate the proposed framework using a public available EEG dataset comprising individuals with Alzheimer's disease and healthy controls. The proposed hybrid modelling of FC achieves a classification accuracy of 83.13%, outperforming both standalone pMEM and correlation-based classifiers. Furthermore, the penalized pMEM reveals more pronounced group difference in network small-worldness, offering improved interpretability alongside enhanced classification performance.},
}

*Alzheimer Disease/physiopathology/diagnosis
Humans
*Electroencephalography/methods
Entropy
Signal Processing, Computer-Assisted
Algorithms
Brain/physiopathology

@article {pmid41337282,
year = {2025},
author = {Uhm, J and Kim, HY and Choe, B and Agrawal, H and Yoon, S and Yun, JH},
title = {Smartphone Keystroke-based Cognitive Impairment Diagnostic Methodology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253467},
pmid = {41337282},
issn = {2694-0604},
mesh = {Humans ; *Smartphone ; *Cognitive Dysfunction/diagnosis ; Male ; Aged ; Female ; },
abstract = {With the global population rapidly aging, dementia has emerged as a major public health concern. Significant efforts are being made to address the health and other problems associated with dementia. Although several advances have been made in the diagnosis and treatment of dementia, many challenges remain unresolved. Existing dementia diagnostic methods are often not performed in a timely manner due to poor accessibility, leading to delays in initiation of treatment. In this study, we propose a smartphone keystroke-based diagnostic method as a solution to overcome the limitations associated with the early diagnosis and treatment of dementia. As compared to other existing methods, the proposed diagnostic method is easier to develop and maintain, promoting commercialization and widespread use. The diagnostic method has been designed to extract language-agnostic keystroke data features from smartphone keyboard input logs. Rather than simply extracting features assessing motor skills from keystroke data or features demonstrating language usage patterns from text, this study focused on features that can assess cognitive abilities without using linguistic characteristics. Clinical trials were conducted in patients with mild cognitive impairment and early Alzheimer's dementia were conducted, and a series of experiments and validation tests were performed using the trial data. The results demonstrated that the proposed smartphone keystroke-based diagnostic method is effective in diagnosing cognitive impairment. The proposed method does not require the use of any special equipment except smartphones, which facilitates low-cost commercialization. This study presents a diagnostic method that addresses the problem of people who avoid tests for the diagnosis of dementia due to economic and psychological burdens.Clinical RelevanceThis study provides an approach for early detection of dementia using ordinary smartphone keystroke logs. The proposed method has the potential to improve the quality of life of patients with dementia. Following large-scale clinical research and the integration of more digital biomarkers, the methodology proposed in this study can potentially facilitate the development of an early diagnostic platform for dementia.},
}

Humans
*Smartphone
*Cognitive Dysfunction/diagnosis
Male
Aged
Female

@article {pmid41337161,
year = {2025},
author = {Wang, Y and Butala, AA and Thebaud, T and Villalba, J and Moro-Velazquez, L},
title = {Sampling Rate Guidelines for Eye-Tracking in Neurological Disorders: A Task-Specific Multimetric Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253777},
pmid = {41337161},
issn = {2694-0604},
mesh = {Humans ; *Eye-Tracking Technology ; *Eye Movements ; *Nervous System Diseases/physiopathology/diagnosis ; Saccades ; Male ; Reproducibility of Results ; Female ; },
abstract = {Variations in eye-tracking sampling rates across devices pose challenges for research characterizing Neurodegenerative Disorders (ND) where precise measurement of eye movements is essential for detection of disease-specific features. This study proposes three metrics-TOST Test Equivalence (TTE), Mean Absolute Error Percentage (MAE%), and Kullback-Leibler Divergence (KLD)-to systematically evaluate how downsampling impacts data quality and feature integrity. Applied to three common tasks (prosaccade, text reading, picture description), results revealed task-specific sampling rate requirements: ⩾400Hz preserved saccadic dynamics in prosaccade, while cognitively complex reading and picture description tasks demanded ⩾600Hz to maintain fidelity. Downsampling degraded key features that are salient to differentiating neurological disorders. ⩾200Hz is required to maintain the discriminative power of key saccade and fixation features. We propose task- and feature-specific recommendations for sampling selection to balance analytical rigor with practical constraints. These findings provide actionable guidelines for standardizing eye-tracking protocols in behavioral research, enhancing reproducibility and robust cross-study comparisons and biomarker validation.Clinical relevanceThis study provides guidelines for selecting eye-tracking sampling rates to ensure reliable detection of disease-specific eye movement features in Alzheimer's, Parkinson's, and related disorders.},
}

Humans
*Eye-Tracking Technology
*Eye Movements
*Nervous System Diseases/physiopathology/diagnosis
Saccades
Male
Reproducibility of Results
Female

@article {pmid41337160,
year = {2025},
author = {G, SP and Gupta, A},
title = {Computational Modeling of NMDAR-Mediated Calcium Dysregulation in Hippocampal Dendritic Spines in Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253866},
pmid = {41337160},
issn = {2694-0604},
mesh = {*Alzheimer Disease/metabolism/pathology/physiopathology ; *Receptors, N-Methyl-D-Aspartate/metabolism ; *Hippocampus/metabolism/pathology ; Humans ; *Dendritic Spines/metabolism/pathology ; *Calcium/metabolism ; Computer Simulation ; Calcium Signaling ; Animals ; *Models, Neurological ; },
abstract = {Disruptions in calcium (Ca[2][+]) signaling play a central role in neurodegenerative processes, particularly in Alzheimer's Disease (AD), where Aβ pathology alters synaptic function. This study presents a computational framework integrating an NMDAR model into an existing astrocyte calcium signaling model adapted for hippocampal dendritic spine to examine the effects of Aβ-induced Ca[2][+] dysregulation. The model incorporates NMDAR-mediated Ca[2][+] influx, ER calcium dynamics (IP3Rs, RyRs, SERCA pumps, buffering), to simulate pathological Ca[2][+] signaling. Results indicate that Aβ increases NMDAR burst probability and conductance, leading to sustained cytosolic Ca[2][+] elevation, further influenced by ER-mediated Ca[2][+] release, which contributes to calcium imbalance. Elevated glutamate levels are also observed to exacerbate these effects. The study further examines possible modulatory mechanisms, suggesting that changes in NMDAR kinetics and calcium-buffering capacity may influence intracellular Ca[2][+] stabilization. Findings highlight the complex interplay between glutamate signaling, ER stress, and NMDAR dysfunction in synaptic calcium dysregulation. The model is constrained by physiological values and the range of values for the model outputs are validated where possible with experimental studies. The model provides a framework for investigating potential mechanisms underlying disruptions in calcium homeostasis in AD and could be extended to study CaMKII activation pathways, synaptic remodeling dynamics, and calcium-binding proteins to enhance its predictive capabilities and broader applicability in understanding AD pathology.Clinical Relevance- Aβ-driven calcium dysregulation is implicated in early synaptic dysfunction in AD, making it a potential target for therapeutic strategies. Insights from this model may help in exploring how modulating NMDAR activity, ER calcium regulation, and calcium-buffering proteins could influence synaptic stability offering directions for future research into neuroprotective strategies for AD.},
}

*Alzheimer Disease/metabolism/pathology/physiopathology
*Receptors, N-Methyl-D-Aspartate/metabolism
*Hippocampus/metabolism/pathology
Humans
*Dendritic Spines/metabolism/pathology
*Calcium/metabolism
Computer Simulation
Calcium Signaling
Animals
*Models, Neurological

@article {pmid41337145,
year = {2025},
author = {He, S and Yang, B and Chen, Y and Zhou, L},
title = {A Novel Graph Neural Network Framework for Brain Age Prediction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254083},
pmid = {41337145},
issn = {2694-0604},
mesh = {Humans ; *Brain/diagnostic imaging/physiopathology ; Magnetic Resonance Imaging ; *Alzheimer Disease/diagnostic imaging/physiopathology ; *Neural Networks, Computer ; Aged ; *Aging ; Male ; Female ; Algorithms ; Machine Learning ; Neuroimaging ; Graph Neural Networks ; },
abstract = {Alzheimer's disease (AD) is a neurodegenerative disorder that causes cognitive decline, and early detection remains a challenge. Resting-state functional MRI (rs-fMRI) has shown potential for identifying early AD signs by analyzing brain connectivity. In this study, we propose a Hierarchical GCN-Transformer Network (HGTNet) for brain age prediction using rs-fMRI data. Through experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we demonstrate that, compared to traditional machine learning and deep learning methods, the combination of Graph Convolutional Networks (GCN) and Transformer architecture enhances the model's ability to capture complex brain interactions. Our model's more accurate brain age predictions provide a valuable step in identifying early neurodegenerative changes, aiding in the better intervention and management of Alzheimer's disease.},
}

Humans
*Brain/diagnostic imaging/physiopathology
Magnetic Resonance Imaging
*Alzheimer Disease/diagnostic imaging/physiopathology
*Neural Networks, Computer
Aged
*Aging
Male
Female
Algorithms
Machine Learning
Neuroimaging
Graph Neural Networks

@article {pmid41337121,
year = {2025},
author = {Sharan, RV and Xiong, H and Tseng, YJ},
title = {Fusion of Traditional and Large Language Models for Linguistic Analysis in Alzheimer's Disease Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254116},
pmid = {41337121},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis ; Humans ; *Linguistics/methods ; *Language ; Algorithms ; Speech ; Female ; Male ; Large Language Models ; },
abstract = {Language-based markers hold potential for the detection of Alzheimer's disease (AD), offering a non-invasive and accessible alternative to traditional methods. Previous studies have leveraged traditional language models (TLM) or large language models (LLM) for AD detection, but their effectiveness is limited by the lack of annotated datasets for training. To address this limitation, this work proposes a fusion of TLM and LLM for classification. Transcripts of speech from 156 AD and non-AD subjects are preprocessed and used to extract linguistic features for TLM classifiers, including random forest (RF), which achieves a test accuracy of 89.6%. In parallel, fine-tuning a pre-trained BERT model on the same dataset results in a test accuracy of 87.5%. The outputs from RF and BERT are then fused using a multilayer perceptron, which achieves a test accuracy of 91.7%. Additionally, word cloud analysis highlights distinct patterns in filler word usage, such as increased reliance on "uh" in AD speech versus "um" in non-AD speech, reflecting linguistic differences linked to cognitive decline. The proposed fusion of traditional and large language models improves AD detection and presents a scalable framework for language-based diagnostic systems.},
}

*Alzheimer Disease/diagnosis
Humans
*Linguistics/methods
*Language
Algorithms
Speech
Female
Male
Large Language Models

@article {pmid41337029,
year = {2025},
author = {Guo, Y and Wang, Y and Moro-Velazquez, L and Dehak, N},
title = {Leveraging Eye-Tracking Signals for Neurodegenerative Disease Classification with Deep Learning Models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254272},
pmid = {41337029},
issn = {2694-0604},
mesh = {Humans ; *Deep Learning ; *Neurodegenerative Diseases/diagnosis/physiopathology/classification ; Male ; Female ; Aged ; *Eye-Tracking Technology ; Parkinson Disease/diagnosis/physiopathology ; Alzheimer Disease/diagnosis/physiopathology ; *Eye Movements ; Middle Aged ; },
abstract = {Neurodegenerative diseases (NDs) can induce subtle changes in eye movements, which can be used for diagnosis or evaluation. This study employs deep learning models to classify NDs using raw eye-tracking data from 133 participants: 51 healthy controls (CTL), 25 with Alzheimer's Disease (AD), 39 with Parkinson's Disease (PD), and 18 with Parkinson's Disease Mimics (PDM)-PD-like diseases often misdiagnosed as PD. Eye movements were recorded during smooth pursuit, text reading, and picture description tasks. Results suggest the text reading task, being structured and cognitively demanding, best distinguished CTL from PD and AD, while the motor-oriented smooth pursuit task differentiated PD from PDM. Ablation studies suggest pupil size improved classification accuracy, especially for CTL vs. PD, and binocular data was crucial for distinguishing PD from PDM. This study shows deep learning models can classify NDs using raw eye-tracking data, becoming a potential tool for objective evaluation in neurology and primary care.Clinical relevance-This project illustrates the efficacy of deep learning methods in leveraging patients' eye movement to accurately predict the presence of neurodegenerative diseases.},
}

Humans
*Deep Learning
*Neurodegenerative Diseases/diagnosis/physiopathology/classification
Male
Female
Aged
*Eye-Tracking Technology
Parkinson Disease/diagnosis/physiopathology
Alzheimer Disease/diagnosis/physiopathology
*Eye Movements
Middle Aged

@article {pmid41336985,
year = {2025},
author = {Salinas-Medina, A and Gonzalez-Mitjans, A and Toussaint, PJ and Liu, X and Evans, A},
title = {HISRON: AI-Driven GPU-Accelerated Framework for Scalable High-Resolution Neuroimaging Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253703},
pmid = {41336985},
issn = {2694-0604},
mesh = {Humans ; *Neuroimaging/methods ; *Image Processing, Computer-Assisted/methods ; *Computer Graphics ; Algorithms ; *Artificial Intelligence ; Brain/diagnostic imaging ; },
abstract = {Ultra-high-resolution imaging, particularly cellular neuroimaging, poses challenges from terabyte-scale data and computational complexity. We present HISRON (High-Resolution Scalable Neuroimaging) a GPU-accelerated framework enabling scalable multidimensional analysis, combining unsupervised learning for adaptive feature extraction, benchmarked anisotropic diffusion for noise reduction, and promptable segmentation models with a zero-shot generalization feature. Dynamic overlap-aware tiling maximizes parallelization while preserving spatial context, enabling real-time processing of complex structures. Built on NVIDIA CUDA and CuPy, the framework achieves transformative efficiency: 10x faster noise reduction and detection of 200,000 neuron centroids in 30 seconds (40% pipeline improvement). This advances integration with AI-driven segmentation/classification pipelines, overcoming bottlenecks in high-dimensional computer vision. By emphasizing scalability, our method accelerates analysis of biomedical imaging data, directly supporting translational healthcare innovations in neuroscience. The tool's adaptability underscores its potential for clinical research, enhancing precision in neuroanatomical studies and fostering discoveries in brain function and pathology.Clinical relevance-The proposed framework directly addresses critical challenges in modern clinical neuroimaging, where the analysis of high-resolution data is essential for diagnosing and monitoring neurological disorders such as Alzheimer's, Parkinson's diseases, and epilepsy. By enabling real-time processing of terabyte-scale datasets, this technology reduces delays in image interpretation, facilitating faster decision-making in time-sensitive scenarios, such as intraoperative imaging during neurosurgery or stroke assessment. The zero-shot segmentation model's adaptability ensures robust performance across heterogeneous imaging protocols, which is vital for multicenter clinical studies and personalized treatment planning. Additionally, the framework's efficiency in detecting neuron populations at scale supports large-scale neuroanatomical studies, enhancing our understanding of brain connectivity abnormalities in psychiatric and neurodegenerative conditions. By lowering computational barriers, this tool democratizes access to advanced imaging analytics, empowering clinics with limited resources to adopt precision medicine approaches. These advancements align with the growing demand for AI-driven scalable solutions to improve diagnostic accuracy, accelerate therapeutic discovery, and optimize patient outcomes in neurology and neurorehabilitation.},
}

Humans
*Neuroimaging/methods
*Image Processing, Computer-Assisted/methods
*Computer Graphics
Algorithms
*Artificial Intelligence
Brain/diagnostic imaging

@article {pmid41336968,
year = {2025},
author = {Wardynski, M and Iacopini, I and Petri, G and Latora, V and Crimi, A},
title = {Modeling the Spread of Misfolded Proteins in Alzheimer's Disease using Higher-Order Simplicial Complex Contagion.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253716},
pmid = {41336968},
issn = {2694-0604},
mesh = {*Alzheimer Disease/metabolism ; Humans ; *Protein Folding ; Amyloid beta-Peptides/metabolism/chemistry ; Brain/metabolism ; },
abstract = {Neurodegenerative diseases are characterized by complex proteins misfolded that propagate within the brain. For instance, current findings highlight the role of 2 specific misfolded proteins in Alzheimer which are believed to spread using brain fibers as highways. Previous studies investigated such spreading by simulation models or machine learning-based predictors which adopt the brain connectome as the underlying spreading network. However, the structural connectome by construction only describes pairwise connections between nodes in a graph. High-order interaction complex networks offer significant advantages over normal graphs because they can capture interactions that go beyond simple pairwise relationships. Protein misfolding and aggregation often involve cooperative behaviors or group dynamics that normal graphs, with their focus on individual edges, cannot adequately represent. The non-linear and multiscale nature of protein misfolding might be better suited to a richer representation of higher-order models. In this study we investigate whether higher-order networks can provide improved fits and explanatory power in this context. More specifically, we employ a simplicial complex contagion model for amyloid beta to predict protein misfolding spread. The simplicial contagion complex produced a mean reconstruction error of 0.030 for Alzheimer's patients regarding the predicted protein deposition across all brain regions in a 2-year horizon and other results, outperforming previous studies, especially for cases in which the misfolded proteins were non-increasing steadily. Despite the limited time span, this study highlights the potential of combining advanced network analysis to capture the intricate dynamics of protein aggregation across neural networks.Clinical relevance- This study highlights the potential of high-order networks to improve predictions of misfolded protein spread in Alzheimer's, offering better insight into protein aggregation dynamics.},
}

*Alzheimer Disease/metabolism
Humans
*Protein Folding
Amyloid beta-Peptides/metabolism/chemistry
Brain/metabolism

@article {pmid41336848,
year = {2025},
author = {Zhang, S and Chattopadhyay, T and Thomopoulos, SI and Ambite, JL and Thompson, PM and Ver Steeg, G},
title = {Diffusion Bridge Models for 3D Medical Image Translation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11252839},
pmid = {41336848},
issn = {2694-0604},
mesh = {Humans ; *Imaging, Three-Dimensional/methods ; *Diffusion Tensor Imaging/methods ; *Brain/pathology ; Male ; Female ; Alzheimer Disease/pathology ; Algorithms ; },
abstract = {Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-weighted (T1w) magnetic resonance imaging (MRI). To address this challenge, we propose a diffusion bridge model for 3D brain image translation between T1w MRI and DTI modalities. Our model learns to generate high-quality DTI fractional anisotropy (FA) images from T1w images and vice versa, enabling cross-modality data augmentation and reducing the need for extensive DTI acquisition. We evaluate our approach using perceptual similarity, pixel-level agreement, and distributional consistency metrics, demonstrating strong performance in capturing anatomical structures and preserving information on white matter integrity. The practical utility of the synthetic data is validated through sex classification and Alzheimer's disease classification tasks, where the generated images achieve comparable performance to real data. Our diffusion bridge model offers a promising solution for improving neuroimaging datasets and supporting clinical decision-making, with the potential to significantly impact neuroimaging research and clinical practice.},
}

Humans
*Imaging, Three-Dimensional/methods
*Diffusion Tensor Imaging/methods
*Brain/pathology
Male
Female
Alzheimer Disease/pathology
Algorithms

@article {pmid41336827,
year = {2025},
author = {LaGrow, TJ and Itkyal, V and Watters, H and Jensen, KM and Ballem, R and Pan, WJ and Iraji, A and Calhoun, VD and Keilholz, S},
title = {Widespread Spatiotemporal Patterns of Functional Brain Networks in Longitudinal Progression of Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251603},
pmid = {41336827},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/physiopathology ; Disease Progression ; Magnetic Resonance Imaging/methods ; *Brain/physiopathology ; Male ; *Nerve Net/physiopathology ; Female ; Aged ; Longitudinal Studies ; },
abstract = {Alzheimer's Disease (AD) is characterized by progressive functional network disruptions that precede cognitive decline, yet traditional functional connectivity analyses often fail to capture transient network instabilities critical for early diagnosis. This study investigates the role of Quasi-Periodic Patterns (QPPs) in identifying disease-related connectivity changes across longitudinal stable disease stages (sNC, sMCI, sDAT) and transitioning (uNC, pMCI) AD cohorts using resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative. By integrating QPP occurrences with intrinsic connectivity networks (ICNs), we assessed network integrity across disease stages, with statistical significance evaluated using the Kruskal-Wallis test and Dunn's test for post-hoc analysis. Results revealed a progressive decline in functional connectivity integrity, with early impairments in subcortical and executive function networks in stable groups, followed by widespread disconnection in higher cognition, sensorimotor, and visual networks at later stages. Transitioning AD groups exhibited earlier disruptions in visual and cerebellar networks, suggesting their potential as early biomarkers for disease onset. The occurrence of QPPs decreased significantly with disease progression, indicating an increase in functional disconnection. These findings highlight the synergy between QPPs and ICNs as a dynamic and sensitive biomarker framework for AD progression. Future research should further explore this integration within multimodal imaging and clinical diagnostic frameworks to enhance early detection and intervention strategies.},
}

Humans
*Alzheimer Disease/physiopathology
Disease Progression
Magnetic Resonance Imaging/methods
*Brain/physiopathology
Male
*Nerve Net/physiopathology
Female
Aged
Longitudinal Studies

@article {pmid41336754,
year = {2025},
author = {Zhang, M and Liu, H and Hao, X and Liu, H and Cong, F and Tommi, K},
title = {Time-Frequency Analysis of Frontal EEG Channels for Alzheimer's Disease Detection Using Deep Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254406},
pmid = {41336754},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/physiopathology ; Humans ; *Electroencephalography/methods ; *Deep Learning ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; Algorithms ; },
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. Electroencephalography (EEG) offers a non-invasive and cost-effective approach for real-time monitoring of brain activity in AD patients. This study proposes a novel EEG-based framework for AD detection, in which frontal EEG signals are transformed into time-frequency representations using continuous wavelet transform (CWT) and encoded as red-green-blue (RGB) spectrograms. A ResNet18 neural network, enhanced with a parallel convolutional block attention module (CBAM), is employed for feature extraction and classification. The proposed method achieves an accuracy of 89.23% using leave-one-subject-out cross-validation (LOSOCV) on a publicly available dataset, outperforming previously reported methods under the same validation protocol. To evaluate the generalization ability of the model, we further test it on a related neurodegenerative condition-frontotemporal dementia (FTD)-and observe promising classification performance. These results suggest that the proposed approach not only provides accurate AD detection but also holds potential for broader applications in neurodegenerative disorder analysis.Clinical Relevance: This study highlights the practicality and effectiveness of utilizing non-invasive EEG signals from frontal brain regions for AD detection. The proposed framework has the potential to assist clinicians in developing timely intervention strategies, thereby improving patient outcomes and quality of life.},
}

*Alzheimer Disease/diagnosis/physiopathology
Humans
*Electroencephalography/methods
*Deep Learning
Signal Processing, Computer-Assisted
Wavelet Analysis
Algorithms

@article {pmid41336726,
year = {2025},
author = {Sanmartin, J and Leon-Guijarro, JL and Romero-Martin, JA and Castellote-Huguet, P and Lopez, B and Santabarbara, JM and Valles-Lluch, A and Maceira, AM and Lloret, A and Moratal, D},
title = {Development of Neuroimaging-based Biomarkers for Early Detection of Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254349},
pmid = {41336726},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis ; Humans ; *Biomarkers/metabolism ; *Neuroimaging/methods ; Early Diagnosis ; *Magnetic Resonance Imaging/methods ; Cognitive Dysfunction/diagnosis ; Male ; Female ; Aged ; Machine Learning ; },
abstract = {Alzheimer's Disease (AD), the leading cause of dementia, is characterized by a progressive cognitive decline and structural brain degeneration. This study is aimed to identify MRI-based biomarkers for early diagnosis of AD, by analyzing data from three clinical groups: controls, mild cognitive impairment (MCI) and AD patients. A total of 589 parameters were extracted: 536 from volumetry, 35 from diffusion, and 12 from perfusion measurements. Statistical analysis focused on two classification tasks: a binary classification to differentiate between controls and unhealthy patients, and a multiclass classification to distinguish between controls, MCI and AD patients. Key volumetric parameters included gray and white matter, hippocampus, limbic system, ventricles and the subventricular zone (SVZ). All the regions analyzed for diffusion and perfusion provided at least one significant parameter. Thirty key biomarkers were selected for each classification task and used to train machine learning models. These were KNN, SVM and Ensemble methods, trained with 5, 10, 15, 20, 25 and 30 parameters as well as a PCA 95%. Ensemble models achieved the highest accuracy, with 85% for binary classification and 75% for multiclass classification. These findings demonstrate the promise of MRI biomarkers combined with machine learning techniques for enhancing early diagnosis of AD.Clinical Relevance- This project provides innovative MRI-based biomarkers for Alzheimer's diagnosis. Early identification of neurodegeneration is crucial for improving patient outcomes. These findings could also influence on diagnostic protocols to enable a more accurate differentiation between healthy individuals, those with mild cognitive impairment and Alzheimer's Disease patients.},
}

*Alzheimer Disease/diagnosis
Humans
*Biomarkers/metabolism
*Neuroimaging/methods
Early Diagnosis
*Magnetic Resonance Imaging/methods
Cognitive Dysfunction/diagnosis
Male
Female
Aged
Machine Learning

@article {pmid41336569,
year = {2025},
author = {Gao, Y and Calhoun, VD and Miller, RL},
title = {Generative Forecasting of Brain Activity Enhances Alzheimer's Classification and Interpretation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253394},
pmid = {41336569},
issn = {2694-0604},
mesh = {*Alzheimer Disease/physiopathology/diagnostic imaging/classification/diagnosis ; Humans ; *Brain/physiopathology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; Forecasting ; },
abstract = {Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, particularly for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.},
}

*Alzheimer Disease/physiopathology/diagnostic imaging/classification/diagnosis
Humans
*Brain/physiopathology/diagnostic imaging
*Magnetic Resonance Imaging/methods
Forecasting

@article {pmid41336503,
year = {2025},
author = {Dhinagar, NJ and Thomopoulos, SI and Thompson, PM},
title = {Leveraging a Vision-Language Model with Natural Text Supervision for MRI Retrieval, Captioning, Classification, and Visual Question Answering.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251809},
pmid = {41336503},
issn = {2694-0604},
mesh = {*Magnetic Resonance Imaging ; Humans ; *Natural Language Processing ; Algorithms ; Brain/diagnostic imaging ; Alzheimer Disease/diagnostic imaging ; Deep Learning ; *Information Storage and Retrieval/methods ; Image Processing, Computer-Assisted/methods ; },
abstract = {Large multimodal models are now extensively used worldwide, with the most powerful ones trained on massive, general-purpose datasets. Despite their rapid deployment, concerns persist regarding the quality and domain relevance of the training data, especially in radiology, medical research, and neuroscience. Additionally, healthcare data privacy is paramount when querying models trained on medical data, as is transparency regarding service hosting and data storage. So far, most deep learning algorithms in radiologic research are designed to perform a specific task (e.g., diagnostic classification) and cannot be prompted to perform multiple tasks using natural language. In this work, we introduce a framework based on vector retrieval and contrastive learning to efficiently learn visual brain MRI concepts via natural language supervision. We show how the method learns to identify factors that affect the brain in Alzheimer's disease (AD) via joint embedding and natural language supervision. First, we pretrain separate text and image encoders using self-supervised learning, and jointly fine-tune these encoders to develop a shared embedding space. We train our model to perform multiple tasks, including MRI retrieval, MRI captioning, and MRI classification. We show its versatility by developing a retrieval and re-ranking mechanism along with a transformer decoder for visual question answering. Clinical Relevance - By learning a cross-modal embedding of radiologic features and text, our approach can learn to perform diagnostic and prognostic assessments in AD research as well as to assist practicing clinicians. Integrating medical imaging with clinical descriptions and text prompts, we aim to provide a general, versatile tool for detecting radiologic features described by text, offering a new approach to radiologic research.},
}

*Magnetic Resonance Imaging
Humans
*Natural Language Processing
Algorithms
Brain/diagnostic imaging
Alzheimer Disease/diagnostic imaging
Deep Learning
*Information Storage and Retrieval/methods
Image Processing, Computer-Assisted/methods

@article {pmid41336445,
year = {2025},
author = {Newman, JL and Collins, J and Phillips, JS},
title = {Discriminating and Measuring Activities of Daily Living.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11251699},
pmid = {41336445},
issn = {2694-0604},
mesh = {*Activities of Daily Living ; Humans ; Wearable Electronic Devices ; Male ; Algorithms ; },
abstract = {Activities of Daily Living (ADLs) are used to assess the ability of individuals to look after themselves in the home, and can be used to aid the diagnosis of degenerative brain diseases, such as Alzheimer's Disease. The current methods for assessing ADLs require the subjective input of a human, and as such may not reflect the objective capabilities of the individual. Therefore, this work explores the automatic and objective quantification of ADLs using data obtained from a wearable medical device which records eye- and head-movements. We focus on detecting four basic activities including eating, brushing teeth, walking and inactivity. Using a subject-independent testing framework, in which the data from each individual being tested does not appear in the training data, our main contribution is to explore a range of contemporary approaches to time series classification and to compare them to our own previous work in this area. Using a MUSE classifier, we obtain a peak mean accuracy of 84.72%. We also present a system for classifying the duration of each activity, which we suggest may be a good parameter for determining how well an activity has been performed. In this task, we achieve perfect discrimination between three durations of 30 s, 60 s and 90 s.Clinical relevanceDetailed measurements of activities of daily living could aid the diagnosis, monitoring and care of individuals with degenerative brain conditions.},
}

*Activities of Daily Living
Humans
Wearable Electronic Devices
Male
Algorithms

@article {pmid41336423,
year = {2025},
author = {Ganon, SA and Friedman, A and Zigel, Y},
title = {Electroencephalogram Data-Based Analysis of Paroxysmal Slow Wave Events Patterns in Brain Pathologies.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254025},
pmid = {41336423},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Machine Learning ; *Epilepsy/physiopathology/diagnosis ; *Brain/physiopathology ; Male ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Slowing of brain activity observed in electroencephalography (EEG) recordings is normal under resting conditions such as sleep. However, a recent series of studies described a new pattern of cortical slowing in patients with epilepsy and Alzheimer's disease, consisting of transient paroxysmal slowing of the network. These paroxysmal slow wave events (PSWEs) were defined with median power frequency (MPF) less than 6 Hz and duration longer than 5 s. In this research, we are using clinical EEG recordings from the Temple University and Bonn University databases. We aim to: (1) Characterize the temporal and spatial characteristics of PSWEs in patients with epilepsy; (2) Identify PSWEs features that will assist in the diagnosis of epilepsy, specifically drug-resistant epilepsy; (3) Identify the sensitivity and specificity of selected combination of features that will help in differentiating between patients with epilepsy and other brain disorders (e.g. Alzheimer's disease, mood disorders). To this end, we trained machine learning models using the Temple University dataset, achieving a classification accuracy of 78.26% in distinguishing between epilepsy and non-epilepsy patients. Moreover, by training the models on the Bonn University database, we achieved an accuracy of 91.67% in classifying drug-resistant epilepsy versus seizure-free groups.Clinical Relevance- PSWEs serve as a potential biomarker for early epilepsy diagnosis and risk assessment, aiding in distinguishing isolated seizures from chronic epilepsy. Their association with neurodegenerative and cognitive disorders further highlights their clinical significance in neurological disease monitoring.},
}

Humans
*Electroencephalography/methods
Machine Learning
*Epilepsy/physiopathology/diagnosis
*Brain/physiopathology
Male
Female
Signal Processing, Computer-Assisted

@article {pmid41336418,
year = {2025},
author = {Nossum, B and Liverud, AE and Dapsance, F and Annavini, E and Modi, B and Hafting, T and Boccara, C and Escobedo-Cousin, E},
title = {A Lightweight FPGA-Based Platform for Low-Power Neural Recording in Freely Moving Animals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253988},
pmid = {41336418},
issn = {2694-0604},
mesh = {Animals ; *Signal Processing, Computer-Assisted/instrumentation ; Wireless Technology/instrumentation ; Sleep/physiology ; *Electroencephalography/instrumentation ; Equipment Design ; Brain/physiology ; },
abstract = {Understanding the neural mechanisms of sleep is critical for advancing treatments for neurodegenerative diseases. To investigate these mechanisms, long-term electrophysiological studies are conducted in animal models, where brain activity can be monitored continuously under controlled conditions. However, electrophysiological recordings from freely moving animals are typically performed using a tethered setup, which imposes constraints on natural behaviour. As a first step towards a miniaturised, wireless neural implant, we present a low-power system that enables direct control of the Intan RHD2132 electrophysiology chip using a custom FPGA-based stack. A light-weight yet robust implementation ensures efficient operation while maintaining the necessary flexibility for future integration into a compact, wireless system. The FPGA handles real-time signal processing and data transmission, with support for closed-loop stimulation and onboard data compression. Our system achieves continuous multi-channel recording at 20 kHz, with real-time wireless streaming and long-term data storage. The hardware design prioritises low power consumption, with the aim of enabling extended operation for uninterrupted sleep monitoring over 24 hoursClinical Relevance- This technology enables long-term, high-resolution sleep monitoring, which is essential for understanding the role of sleep in neurodegenerative disorders such as Alzheimer's and Parkinson's. By providing continuous, naturalistic brain recordings, this system may help identify early electrophysiological biomarkers of cognitive decline, with potential translational and therapeutics applications.},
}

Animals
*Signal Processing, Computer-Assisted/instrumentation
Wireless Technology/instrumentation
Sleep/physiology
*Electroencephalography/instrumentation
Equipment Design
Brain/physiology

@article {pmid41336397,
year = {2025},
author = {Seo, K and Hwang, J and Kim, K and Argrawal, H and Yun, JH and Yoon Kim, H},
title = {End-to-End Classification of Cognitive Impairment Using Daily-Life Gait Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254035},
pmid = {41336397},
issn = {2694-0604},
mesh = {Humans ; *Cognitive Dysfunction/diagnosis/physiopathology/classification ; *Gait ; Aged ; Male ; Female ; Middle Aged ; Smartphone ; Accelerometry ; *Activities of Daily Living ; Alzheimer Disease/diagnosis/physiopathology ; Wearable Electronic Devices ; },
abstract = {We investigated the feasibility of using commercial smart devices to prescreen cognitive impairments for timely intervention. A total of 125 individuals aged 50 and older were recruited from a local clinic and categorized into groups based on cognitive diagnosis: Mild Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Subjective Cognitive Decline (SCD).This study focused on distinguishing individuals requiring treatment (AD and MCI) from those without cognitive impairment (SCD) using gait-related data collected from daily life. This classification task was particularly challenging, as the SCD group shares cognitive symptoms with MCI and the data was collected in non-controlled, real-world conditions.Participants used smartphones and smartwatches for one month to collect accelerometer and gyroscope data during daily activities. We preprocessed walking segments and trained a deep learning classifier to differentiate between the two groups. The model achieved an area under the curve (AUC) of 0.70, demonstrating the potential of wearable-based gait analysis for cognitive impairment detection. A sensor ablation study revealed that wrist-worn gyroscope data alone achieved a comparable AUC of 0.70, suggesting a moderate association between arm motion and cognitive impairment. The results further indicated that integrating smartphone accelerometers and smartwatch gyroscopes enhanced classification performance.This study is part of a broader multimodal research initiative that integrates gait analysis with voice recordings, phone usage patterns, and other behavioral data to improve cognitive impairment classification. Future work will explore multi-modal fusion techniques to enhance accuracy and reliability, with the long-term goal of developing accessible, real-world screening tools for early detection.},
}

Humans
*Cognitive Dysfunction/diagnosis/physiopathology/classification
*Gait
Aged
Male
Female
Middle Aged
Smartphone
Accelerometry
*Activities of Daily Living
Alzheimer Disease/diagnosis/physiopathology
Wearable Electronic Devices

@article {pmid41336391,
year = {2025},
author = {Favaro, A and Novotny, K and He, Y and Leng, Y and Das, S and Mekyska, J and Moro-Velazquez, L and Dehak, N},
title = {Toward Inclusive Large-Scale Alzheimer's Disease Detection via Speech and Language Modeling.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254036},
pmid = {41336391},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/diagnosis/physiopathology ; *Speech ; *Language ; Aged ; Male ; Female ; Cognitive Dysfunction/diagnosis ; },
abstract = {Previous speech-based detection methods for Alzheimer's Disease and Related Dementias (ADRD) have been constrained by small sample sizes, reliance on single corpora, languages, tasks, and recording conditions, limiting their generalizability. Moreover, many studies have simplified cognitive decline progression through binary classification. To address these limitations, we developed a multimodal framework integrating language-agnostic, multilingual, and language-dependent models with demographic data to enhance adaptability across diverse cohorts. We applied this model to a three-class classification problem-cognitively normal controls (CNs), Mild Cognitive Impairment (MCI), and ADRD-using the PREPARE Challenge corpus, which includes 2058 speakers (1140 CNs, 268 MCI, and 650 ADRD). Our best-performing model achieved an F1 score of 0.71 and a log loss of 0.63 on the internal test set, with strong generalization to external test data. Bias mitigation strategies addressed demographic imbalances, including model fusion, data augmentation, and weighted cross-entropy loss. However, challenges remain for underrepresented subgroups. This study highlights the importance of integrating generalizable and language-specific features for scalable, accurate ADRD detection. Future work will expand the dataset to include more languages, improve task diversity, and refine fusion strategies to enhance robustness and scalability in clinical settings.Clinical relevance-The proposed framework provides clinicians with a scalable, inclusive system for early ADRD detection, leveraging multilingual and language-agnostic models to support timely interventions and personalized care, even in resource-constrained or underserved settings.},
}

Humans
*Alzheimer Disease/diagnosis/physiopathology
*Speech
*Language
Aged
Male
Female
Cognitive Dysfunction/diagnosis

@article {pmid41336382,
year = {2025},
author = {Roy, O and Moshfeghi, Y and Smith, J and Ibanez, A and Parra, MA and Smith, KM},
title = {A Hodge-FAST Framework for High-Resolution Dynamic Functional Connectivity Analysis of Higher Order Interactions in EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253015},
pmid = {41336382},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; Alzheimer Disease/physiopathology ; *Brain/physiopathology ; Cognitive Dysfunction/physiopathology ; },
abstract = {We introduce a novel framework that integrates Hodge decomposition with Filtered Average Short-Term (FAST) functional connectivity to analyze dynamic functional connectivity (DFC) in EEG signals. This method leverages graph-based topology and simplicial analysis to explore transient connectivity patterns at multiple scales, addressing noise, sparsity, and computational efficiency. The temporal EEG data are first sparsified by keeping only the most globally important connections, instantaneous connectivity at these connections is then filtered by global long-term stable correlations. This tensor is then decomposed into three orthogonal components to study signal flows over higher-order structures such as triangle and loop structures. Our analysis of Alzheimer-related MCI patients show significant temporal differences related to higher-order interactions that a pairwise analysis on its own does not implicate. This allows us to capture higher-dimensional interactions at high temporal resolution in noisy EEG signal recordings.},
}

*Electroencephalography/methods
Humans
*Signal Processing, Computer-Assisted
Algorithms
Alzheimer Disease/physiopathology
*Brain/physiopathology
Cognitive Dysfunction/physiopathology

@article {pmid41336322,
year = {2025},
author = {Lin, MC and Kuo, PC and Li, YT and Chen, CY},
title = {Interpretable Multi-Attention Fusion Mechanisms for Early Detection of Transitional Phases in Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254165},
pmid = {41336322},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/diagnostic imaging ; Humans ; Magnetic Resonance Imaging/methods ; Early Diagnosis ; Machine Learning ; Electronic Health Records ; *Attention ; Algorithms ; },
abstract = {Alzheimer's disease (AD), a chronic neurodegenerative disorder, is characterized by progressive cognitive and memory decline, ultimately leading to physical deterioration and death. Early detection is essential for slowing the progression of symptoms and improving patient outcomes. Recent advancements in technology have led to the increased application of machine learning for AD detection. In this study, we aim to identify individuals at risk for future AD onset. To this end, we propose a Multi-Attention Gated Multimodal Unit model that integrates Electronic Health Records (EHRs) and Magnetic Resonance Imaging (MRI) data. We also present an evaluation framework designed to measure the model's effectiveness in early-stage AD detection. Experimental results demonstrate that our approach outperforms baseline models in recognizing early signs of AD. Furthermore, to enhance interpretability, we employ Gradient-Weighted Class Activation Mapping (Grad-Cam) heatmaps and attention maps, offering insights into the model's decision-making process and its ability to detect crucial AD-related features at an early stage.},
}

*Alzheimer Disease/diagnosis/diagnostic imaging
Humans
Magnetic Resonance Imaging/methods
Early Diagnosis
Machine Learning
Electronic Health Records
*Attention
Algorithms

@article {pmid41336319,
year = {2025},
author = {Vlontzou, ME and Athanasiou, M and Davatzikos, C and Nikita, KS},
title = {Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254128},
pmid = {41336319},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnostic imaging/diagnosis ; Humans ; *Machine Learning ; *Magnetic Resonance Imaging/methods ; Male ; Female ; Aged ; Cognitive Dysfunction/diagnostic imaging ; Bias ; Aged, 80 and over ; },
abstract = {The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) from MRI-derived neuroimaging features. Biases associated with age, race, and gender in a multi-cohort dataset, as well as the influence of proxy features encoding these sensitive attributes, are investigated. The reliability of various fairness definitions and metrics in the identification of such biases is also assessed. Based on the most appropriate fairness measures, a comparative analysis of widely used pre-processing, in-processing, and post-processing bias mitigation strategies is performed. Moreover, a novel composite measure is introduced to quantify the trade-off between fairness and performance by considering the F1-score and the equalized odds ratio, making it appropriate for medical diagnostic applications. The obtained results reveal the existence of biases related to age and race, while no significant gender bias is observed. The deployed mitigation strategies yield varying improvements in terms of fairness across the different sensitive attributes and studied subproblems. For race and gender, Reject Option Classification improves equalized odds by 46% and 57%, respectively, and achieves harmonic mean scores of 0.75 and 0.80 in the MCI versus AD subproblem, whereas for age, in the same subproblem, adversarial debiasing yields the highest equalized odds improvement of 40% with a harmonic mean score of 0.69. Insights are provided into how variations in AD neuropathology and risk factors, associated with demographic characteristics, influence model fairness.},
}

*Alzheimer Disease/diagnostic imaging/diagnosis
Humans
*Machine Learning
*Magnetic Resonance Imaging/methods
Male
Female
Aged
Cognitive Dysfunction/diagnostic imaging
Bias
Aged, 80 and over

@article {pmid41336252,
year = {2025},
author = {Uudeberg, T and Paeske, L and Bachmann, M},
title = {Longitudinal Stability of Detrended Fluctuation Analysis in Monthly Recorded EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253319},
pmid = {41336252},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Longitudinal Studies ; Reproducibility of Results ; },
abstract = {Electroencephalography (EEG) is a cost-effective, noninvasive method with high temporal resolution that enables detailed assessment of neuronal activity. Detrended fluctuation analysis (DFA), a nonlinear technique for quantifying long-range temporal correlations in time-series data, has found application in EEG research across various contexts. This study investigates the temporal stability of DFA over the course of one year, during which 12 monthly EEG recordings were collected from each of nine healthy male participants. Our findings demonstrate excellent within-subject reliability, with intraclass correlation coefficients (ICCs) ranging from 0.985 to 0.997 across 30 EEG channels. These high ICCs indicate that interindividual variability exceeds intraindividual variability, supporting DFA's reliability for long-term neural monitoring. Despite considerable differences between individuals, DFA remained consistent within subjects, highlighting its potential as a subject-specific biomarker for neurological disorders such as epilepsy, depression, and Alzheimer's disease. These findings underscore the importance of accounting for individual variability in EEG measures when developing tools for early diagnosis and clinical monitoring.Clinical Relevance- Reliable biomarkers for neuronal activity must demonstrate consistent temporal stability. This study shows that DFA offers excellent stability within individuals, supported by ICC analyses, suggesting its potential as a subject-specific biomarker for early detection of disorders characterized by altered EEG dynamics.},
}

Humans
*Electroencephalography/methods
Male
*Signal Processing, Computer-Assisted
Adult
Longitudinal Studies
Reproducibility of Results

@article {pmid41336136,
year = {2025},
author = {Favaro, A and Guo, Y and Thebaud, T and Villalba, J and Moro-Velazquez, L},
title = {Advancing Alzheimer's Disease Detection via Multimodal Fusion of Speech and Eye Movement Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253038},
pmid = {41336136},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/diagnosis/physiopathology ; *Eye Movements ; *Speech ; Male ; Female ; Aged ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Alzheimer's disease (AD) is a neurodegenerative disorder that impacts multiple cognitive domains. Early-stage changes in eye movement patterns and language abilities are often detectable and become more pronounced as cognitive decline progresses. This study investigates whether integrating speech and extraocular movement (EOM) data improves AD detection compared to using each modality alone, assessing its effectiveness across various modeling frameworks and language tasks. Our contributions include: (1) developing task-agnostic methods that leverage raw speech and EOM signals to improve the scalability and robustness of AD classification; (2) introducing SpeechEyeNet, a transformer-based cross-modal network that integrates speech and EOM data for effective multimodal fusion; (3) evaluating a pretrained time-series model for encoding raw EOM data, using its zero-shot capabilities for eye movement analysis without task-specific fine-tuning; and (4) conducting a comparative evaluation of interpretable and non-interpretable multimodal frameworks, highlighting their trade-offs and complementary strengths. Our findings indicate that multimodal fusion consistently outperforms unimodal baselines, with interpretable methods typically yielding superior performance, likely due to sample size limitations. This study underscores the complementary role of speech and EOM data in AD detection. Future work will consider different neurological disorders, explore new fusion techniques, and incorporate diverse datasets to improve model generalizability.Clinical relevance-This study highlights the potential of speech and EOM signal integration for improving early and accurate AD detection. Leveraging complementary modalities, our approach enhances diagnostic robustness and supports the development of non-invasive screening tools for clinical and real-world applications.},
}

Humans
*Alzheimer Disease/diagnosis/physiopathology
*Eye Movements
*Speech
Male
Female
Aged
Signal Processing, Computer-Assisted
Algorithms

@article {pmid41336106,
year = {2025},
author = {Shanmugasundaram, S and K, GG and Sinha, N},
title = {EEG Complexity Measures for Alzheimer's and Frontotemporal Dementia Classification Using Explainable Machine Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253046},
pmid = {41336106},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/diagnosis/physiopathology/classification ; *Electroencephalography/methods ; *Frontotemporal Dementia/diagnosis/physiopathology/classification ; *Machine Learning ; Male ; Female ; Aged ; Signal Processing, Computer-Assisted ; Middle Aged ; },
abstract = {This study aimed to classify patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD) from healthy controls (CN) using non-linear EEG features. We explored various classification tasks, including binary classifications (CN vs. AD, CN vs. FTD, FTD vs. AD) and multi-class classifications, employing machine learning models such as XGBoost, multi-layer perceptron, k-nearest neighbors, and support vector machines. To understand the model's decision-making process, we employed explainable AI (XAI) using SHAP (SHapley Additive exPlanations) analysis. An EEG dataset of 88 subjects: 36 with AD, 29 controls, and 23 with FTD, was used. The occipital electrode O2 played a crucial role in differentiating AD from controls. In both FTD vs. AD and CN vs. FTD classifications, features from the frontal and temporal electrodes exhibited the highest importance. The results showed that XGB and MLP perform best across all classification tasks, with 100% accuracy achieved in CN vs. AD classification and area under the curve values of 0.99 for most classifiers. Distinguishing unhealthy patients (AD and FTD) from healthy controls yielded lower performance, potentially due to the differential EEG signal alterations in these conditions. The multi-class classification of AD, FTD, and controls achieved accuracy of 82%, lower than the binary classification tasks. The study proposed a novel methodology combining non-linear EEG features and machine learning models, offering the potential for improved disease detection.Clinical relevance- This study offers the potential to provide a non-invasive, efficient method for early detection and differentiation of AD and FTD from healthy controls. The ability to classify these neurodegenerative diseases using EEG, a widely accessible and cost-effective tool, could significantly aid in the timely diagnosis and monitoring of disease progression. This could lead to more personalized treatment plans and improved patient care.},
}

Humans
*Alzheimer Disease/diagnosis/physiopathology/classification
*Electroencephalography/methods
*Frontotemporal Dementia/diagnosis/physiopathology/classification
*Machine Learning
Male
Female
Aged
Signal Processing, Computer-Assisted
Middle Aged

@article {pmid41336065,
year = {2025},
author = {Perley, AS and Coleman, TP},
title = {A Dynamic Mutual Information Measure of Phase Amplitude Coupling.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251622},
pmid = {41336065},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; *Signal Processing, Computer-Assisted ; Linear Models ; Sleep/physiology ; },
abstract = {Phase-amplitude coupling (PAC) is a fundamental neural phenomenon in which the phase of a slow oscillation modulates the amplitude of a faster oscillation. PAC has been implicated in various cognitive and clinical conditions, including Parkinson's disease, epilepsy, and depression. Traditional methods for quantifying PAC compute a single summary statistic over an entire time series, limiting their ability to capture dynamic fluctuations. Growing interest in time-varying PAC has led to methods that rely on windowed time-series analysis, but these approaches struggle to track rapid changes in coupling at single-sample resolution. To address this limitation, we propose a novel dynamic mutual information measure of PAC, leveraging a state-space modeling approach based on a Gamma generalized linear model (GLM). By introducing a Gauss-Markov process on the regression weights, our method enables dynamic, interpretable PAC estimation at each time point. We validate our approach using synthetic phase-amplitude coupled signals with time-varying coupling coefficients and demonstrate superior performance in smoothly tracking PAC over time and distinguishing coupled from uncoupled states. Additionally, we apply our technique to sleep EEG data, successfully identifying PAC during sleep spindles, which may serve as a biomarker for neurophysiological conditions such as Alzheimer's disease. Our findings suggest that this dynamic PAC measure is a powerful tool for neuroscientific and clinical research, with potential applications in real-time brain-computer interfaces and neurostimulation protocols.Clinical relevanceThis work demonstrates a new technique for quantifying time-varying electrophysiological coupling. This may allow for understanding transient neural dynamics in disease states and may help more robustly inform electrical stimulation protocols for patients with neurodegenerative disorders.},
}

Humans
*Electroencephalography/methods
Algorithms
*Signal Processing, Computer-Assisted
Linear Models
Sleep/physiology

@article {pmid41336060,
year = {2025},
author = {Angerbauer, R and Indelicato, E and Cesari, M},
title = {Analysis of Resting-State EEG Features in Alzheimer's Disease and Frontotemporal Dementia with Bayesian Hierarchical Models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11251665},
pmid = {41336060},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/physiopathology/diagnosis ; *Frontotemporal Dementia/physiopathology/diagnosis ; Bayes Theorem ; *Electroencephalography/methods ; Male ; Female ; Aged ; *Rest ; Middle Aged ; },
abstract = {Electroencepholography (EEG) biomarkers are crucial for studying neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Traditional analyses often require correction for multiple comparisons, reducing sensitivity. We applied Bayesian hierarchical models (BHM) to EEG spectral power and connectivity measures to improve inference. Using EEG data from 88 subjects (36 AD, 23 FTD, 29 controls), we compared BHM and linear models (LM). BHM identified more significant group differences than LM, revealing neurophysiological alterations in AD and FTD and highlighting the advantages of hierarchical modeling for EEG analysis.},
}

Humans
*Alzheimer Disease/physiopathology/diagnosis
*Frontotemporal Dementia/physiopathology/diagnosis
Bayes Theorem
*Electroencephalography/methods
Male
Female
Aged
*Rest
Middle Aged

@article {pmid41336040,
year = {2025},
author = {Lin, CW and Chung, HW and Wu, WC},
title = {Exploring Alpha Desynchronization in Alzheimer's Disease Using Coherence Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11252896},
pmid = {41336040},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/physiopathology/diagnosis ; *Alpha Rhythm ; *Electroencephalography/methods ; Male ; Female ; Aged ; },
abstract = {Electroencephalography (EEG) is a promising tool for early diagnosis of dementia. As the most common form of dementia, Alzheimer's disease (AD) is currently incurable, but patients and those at risk can benefit from timely intervention following accurate diagnosis. Previous studies reported decreased energy in the alpha-band of EEG signals during the transition from the eyes-closed condition to the eyes-open condition, and considered energy as a surrogate index of synchronization (or functional connectivity). In this study, we explored alpha desynchronization by using coherence which is a mathematical index of connectivity. Our results showed that the AD group had lower alpha-band energy and coherence, and diminished alpha desynchronization as compared with the normal control group. Coherence and energy have different implications in synchronization/connectivity (long-range vs. local), and when combined, may advance the understanding of AD pathology-related functional connectivity changes.Clinical Relevance- This study highlights diminished alpha desynchronization in patients with Alzheimer's disease, which may be used as a biomarker for early diagnosis.},
}

Humans
*Alzheimer Disease/physiopathology/diagnosis
*Alpha Rhythm
*Electroencephalography/methods
Male
Female
Aged

@article {pmid41336035,
year = {2025},
author = {Flores-Ortiz, P and Montesinos, L and Isla, AG and Santos-Diaz, A and Arroyo-Garcia, LE},
title = {Characterization of Hippocampal Local Field Potentials using Lyapunov Exponent Analysis and Unsupervised Machine Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11252897},
pmid = {41336035},
issn = {2694-0604},
mesh = {Animals ; *Hippocampus/physiology ; *Unsupervised Machine Learning ; Rats ; Signal Processing, Computer-Assisted ; Male ; Machine Learning ; Algorithms ; Alzheimer Disease/physiopathology/diagnosis ; },
abstract = {This study explores the application of Lyapunov exponent (LE) analysis to characterize local field potentials (LFPs) from hippocampal brain slices in animal models, focusing on differentiating between basal and active states of hippocampal activity. We used LFP recordings obtained from hippocampal slices treated with kainic acid to induce active states, capturing transitions and sustained activity periods. The signals were pre-processed to standardize their length and filtered using a 4th-order Butterworth bandpass filter to isolate gamma oscillations. LE analysis was used to assess the dynamical behavior of these signals, revealing that positive LE values indicate chaotic dynamics, which were prevalent in the active state recordings. Further analysis using time-series clustering distinguished patterns in the progression from basal to active states, suggesting that LE could serve as a biomarker for neurophysiological and pathological conditions, including Alzheimer's disease. Our findings suggest that LE analysis provides a novel approach to understanding the complex dynamics of the hippocampus, potentially contributing to early diagnosis of neurodegenerative diseases.Clinical relevance- Lyapunov exponent analysis of hippocampal LFPs may serve as a biomarker for early detection of neurodegenerative diseases like Alzheimer's, aiding in diagnosis and intervention.},
}

Animals
*Hippocampus/physiology
*Unsupervised Machine Learning
Rats
Signal Processing, Computer-Assisted
Male
Machine Learning
Algorithms
Alzheimer Disease/physiopathology/diagnosis

@article {pmid41335966,
year = {2025},
author = {Emery, BA and Khanzada, S and Hu, X and Maggi, MA and Bisti, S and Amin, H},
title = {Network-Level Characterization of Hippocampal Disruptions in Alzheimer's Disease Using Large-Scale Electrophysiology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253272},
pmid = {41335966},
issn = {2694-0604},
mesh = {*Alzheimer Disease/physiopathology ; *Hippocampus/physiopathology/pathology ; Animals ; Mice ; Mice, Transgenic ; Disease Models, Animal ; Microelectrodes ; *Electrophysiology/methods ; *Nerve Net/physiopathology ; Humans ; },
abstract = {Alzheimer's disease (AD), a progressive neurodegenerative disorder, is projected to affect over 130 million people globally by 2050. While extensive efforts have focused on targeting molecular hallmarks such as amyloid-beta (Aβ) plaques and tau pathology, network-level dysfunction remains a critical but underexplored component of AD progression. Disruptions in hippocampal-cortical (HC) circuit activity emerge early in AD, compromising memory processing and cognitive functions. Characterizing these disruptions requires high-resolution platforms capable of capturing network-wide spatiotemporal dynamics. To address this, we implemented a high-density microelectrode array (HD-MEA) biosensor to assess large-scale electrophysiological activity in ex vivo hippocampal slices from well-established APP[NL] and APP[NL-G-F] mouse models. Our approach quantifies hippocampal oscillatory disturbances and examines their modulation by saffron, a natural compound with reported neuroprotective properties. Results indicate that hippocampal network activity is progressively impaired in APP[NL-G-F] mice, particularly in sharp-wave ripple (SWR) and multi-unit activity (MUA) patterns. The HD-MEA platform provides a scalable tool for investigating AD-associated network dysfunctions and exploring potential modulatory interventions.},
}

*Alzheimer Disease/physiopathology
*Hippocampus/physiopathology/pathology
Animals
Mice
Mice, Transgenic
Disease Models, Animal
Microelectrodes
*Electrophysiology/methods
*Nerve Net/physiopathology
Humans

@article {pmid41335924,
year = {2025},
author = {Brause, E and Koenen, N and Wright, MN and Schultz, T},
title = {Explaining Multimodal Features for Screening of Cognitive Impairment Using Shapley Values.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11251859},
pmid = {41335924},
issn = {2694-0604},
mesh = {Humans ; *Cognitive Dysfunction/diagnosis/diagnostic imaging ; Machine Learning ; Alzheimer Disease/diagnosis/diagnostic imaging ; Algorithms ; Speech ; Brain/diagnostic imaging ; Female ; Aged ; Male ; },
abstract = {Many approaches for Alzheimer's disease screening focus on either medical imaging or speech as modalities. However, our previous work shows that unimodal models utilizing features from either conversational speech or structural brain imaging are outperformed by models leveraging features of both modalities simultaneously. Herein, we use XAI techniques based on Shapley values to investigate which features are most influential for the multimodal models' decisions on the fusion and the input level in both state and predictive screening. We analyze individual patients using Shapley-based methods like SHAP, GradSHAP and DeepSHAP, and derive global insights for the most important features by aggregating these local techniques. We find that the models benefit from using both modalities, although volumetric features derived from brain imaging contribute more towards the classification results than acoustic and linguistic features derived from speech in both screening types.Clinical Relevance-Applying explainability methods to multimodal machine learning models enhances reliability and robustness of trained models, can support clinicians in interpreting the deployed model's decision, and may guide selection of the most important screening modalities in the future.},
}

Humans
*Cognitive Dysfunction/diagnosis/diagnostic imaging
Machine Learning
Alzheimer Disease/diagnosis/diagnostic imaging
Algorithms
Speech
Brain/diagnostic imaging
Female
Aged
Male

@article {pmid41335857,
year = {2025},
author = {Gambosi, B and Buda, C and Toschi, N and Astolfi, L},
title = {Functional Ultrasound Imaging in Simulated Brain State Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254749},
pmid = {41335857},
issn = {2694-0604},
mesh = {Humans ; *Brain/diagnostic imaging/physiopathology ; Alzheimer Disease/diagnostic imaging/physiopathology ; Ultrasonography/methods ; Neural Networks, Computer ; Computer Simulation ; Epilepsy/diagnostic imaging/physiopathology ; },
abstract = {Functional ultrasound imaging (fUSI) has emerged as a promising non-invasive neuroimaging modality that leverages neurovascular coupling to capture hemodynamic changes associated with neuronal activity. This study investigates the feasibility of fUSI for brain state classification in both healthy and pathological conditions using computational simulations based on the Wilson-Cowan neural mass model. Simulated electrophysiological signals were transformed into fUSI-like traces via convolution with an experimentally derived hemodynamic response function (HRF). Pathological conditions representing Alzheimer's disease (AD) and epilepsy were introduced by modifying excitatory-inhibitory balance parameters. A one-dimensional convolutional neural network (1D-CNN) was trained to classify healthy and pathological states based on either raw electrophysiological data or simulated fUSI signals.Results indicate that while classification performance was superior for electrophysiological data due to its finer temporal resolution, fUSI signals retained sufficient discriminative information for reliable classification, particularly in binary tasks distinguishing control from pathological conditions. Performance was found to be dependent on the progression of pathology, with more severe alterations leading to improved classification accuracy. However, the increased sensitivity of fUSI signals to noise led to some decline in classification performance under high noise conditions. In multiclass tasks distinguishing connectivity states within the same pathology, fUSI exhibited reduced accuracy, indicating challenges in capturing finer network-level distinctions.Clinical relevance-These findings support the potential of fUSI as a cost-effective alternative to traditional neuroimaging for detecting pathological brain states. While some challenges remain in resolving fine-grained connectivity states, fUSI could provide valuable insights into neurological disorders such as Alzheimer's disease and epilepsy, particularly when combined with advanced noise reduction and signal processing techniques.},
}

Humans
*Brain/diagnostic imaging/physiopathology
Alzheimer Disease/diagnostic imaging/physiopathology
Ultrasonography/methods
Neural Networks, Computer
Computer Simulation
Epilepsy/diagnostic imaging/physiopathology

@article {pmid41335802,
year = {2025},
author = {Shu, K and Yang, P and Wang, T and Song, X and Lei, B},
title = {Joint Brain Structure-Function Analysis with Correlation-Consistent Learning for Alzheimer's Disease Diagnosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253645},
pmid = {41335802},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/physiopathology/diagnostic imaging ; Humans ; *Magnetic Resonance Imaging/methods ; *Brain/physiopathology/pathology/diagnostic imaging ; Neuroimaging/methods ; Bayes Theorem ; Algorithms ; },
abstract = {In neuroimaging-based Alzheimer's Disease (AD) diagnosis, effectively integrating structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data while preserving clinical interpretability remains a significant challenge. To address this issue, we propose a novel transformer-based framework that unifies heterogeneous imaging features into coherent region-level representations. Our approach uniquely leverages prior anatomical knowledge to guide attention toward AD-relevant regions while employing a learnable mapping mechanism that transforms sMRI spatial features into biologically meaningful regional representations. We implement a consistency constraint to ensure optimal alignment between structural and functional coupling across modalities, followed by a Bayesian fusion strategy to integrate these aligned multi-modal features. Through comprehensive evaluation on the ADNI dataset, our method demonstrates not only superior diagnostic accuracy compared to existing state-of-the-art approaches but also provides clinically interpretable insights into AD-related brain connectivity patterns. This work represents a significant advancement in multi-modal neuroimaging analysis for AD diagnosis, successfully combining enhanced diagnostic performance with clinical interpretability.},
}

*Alzheimer Disease/diagnosis/physiopathology/diagnostic imaging
Humans
*Magnetic Resonance Imaging/methods
*Brain/physiopathology/pathology/diagnostic imaging
Neuroimaging/methods
Bayes Theorem
Algorithms

@article {pmid41335728,
year = {2025},
author = {Espis, A and Marzi, C and Diciotti, S},
title = {Self-supervised and supervised learning in medical imaging classification: addressing the hidden bias of workflow design.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254853},
pmid = {41335728},
issn = {2694-0604},
mesh = {Humans ; *Supervised Machine Learning ; *Workflow ; *Diagnostic Imaging/methods/classification ; *Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; Alzheimer Disease/diagnostic imaging ; Algorithms ; Bias ; },
abstract = {The rise of self-supervised learning (SSL) in medical imaging holds immense potential, particularly for leveraging unlabeled data and achieving surprising performance in scenarios with limited annotations. Our study shows that comparisons with traditional supervised learning (SL) are often confounded by differences in workflows, leading to potentially biased conclusions. The SL paradigm is typically employed with a one-stage training workflow, while the typical SSL linear evaluation workflow involves a two-stage process: pre-training a backbone with a projector, followed by fine-tuning a randomly initialized task-specific classification head replacing the projector. We show that the two-stage workflow, when applied to SL, can change the trained model performance. This is especially important when selecting the appropriate paradigm for medical imaging classification where the outcomes can have a clinical impact. We experimented with four medical imaging datasets, targeting age prediction and Alzheimer's disease diagnosis from brain MRI, pneumonia diagnosis from chest RX, and diagnosis of retina with choroidal neurovascularization from optical coherence tomography. For each dataset, we imposed different configurations of assumed label availability and class frequency distribution. For each configuration, we performed 30 experiments (5 for the larger dataset) and a robust statistical analysis of the results, which show that the different workflows can alter the trained model performance. This finding suggests that the typical comparisons between SL and SSL in literature may not solely reflect the learning paradigm itself but also the workflow, which is agnostic to the paradigm. In the field of medical imaging, where model performance directly impacts clinical decision-making and patient outcomes, ensuring fair and robust comparisons is critical. By addressing this overlooked bias, our work provides actionable insights to advance reliable methodologies, paving the way for more effective and trustworthy AI-driven solutions in healthcare.},
}

Humans
*Supervised Machine Learning
*Workflow
*Diagnostic Imaging/methods/classification
*Image Processing, Computer-Assisted/methods
Magnetic Resonance Imaging
Alzheimer Disease/diagnostic imaging
Algorithms
Bias

@article {pmid41335632,
year = {2025},
author = {Zhang, Y and Li, S and Abrol, A and Zou, C and Calhoun, V},
title = {A Hybrid CNN-Transformer Network for fMRI-Based Feature Encoding in Alzheimer's Disease Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253167},
pmid = {41335632},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnostic imaging/classification ; Humans ; *Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; *Neural Networks, Computer ; *Image Processing, Computer-Assisted/methods ; Algorithms ; Deep Learning ; },
abstract = {Functional magnetic resonance imaging (fMRI) is widely used to capture brain functional activities, but its high-dimensional nature and temporal complexity pose significant challenges for feature representation. To address these issues, this study proposes an end-to-end deep learning network for fMRI feature encoding and validates its effectiveness through Alzheimer's disease (AD) classification task. The proposed network first employs a 3D CNN to perform spatial encoding of fMRI at each time point, extracting local spatial patterns of brain images. Then, a transformer attention block specifically designed for 3D MRI is designed, which enhances spatial feature modeling through 3D positional encoding and captures long-range dependencies between brain regions. Finally, a cascaded transformer module is constructed to integrate spatial features across different time points, modeling the dynamic changes in brain activity. Experimental results on two fMRI datasets from ADNI database demonstrate that the proposed method improves feature representation and AD classification performance. By effectively capturing both the spatial and temporal characteristics of fMRI data, this approach provides a robust solution for automated fMRI feature encoding. The code is available at https://github.com/Yanteng32/CTF_FMRI.},
}

*Alzheimer Disease/diagnostic imaging/classification
Humans
*Magnetic Resonance Imaging/methods
Brain/diagnostic imaging
*Neural Networks, Computer
*Image Processing, Computer-Assisted/methods
Algorithms
Deep Learning

@article {pmid41335577,
year = {2025},
author = {Hizukuri, K and Fujita, Y and Amagai, T},
title = {Endoscopic Retrieval of Ingested Button Battery From a Colonic Diverticulum in an Alzheimer's Patient: A Case Report.},
journal = {The American journal of case reports},
volume = {26},
number = {},
pages = {e949004},
doi = {10.12659/AJCR.949004},
pmid = {41335577},
issn = {1941-5923},
mesh = {Humans ; Female ; *Alzheimer Disease/complications ; *Foreign Bodies/surgery/diagnostic imaging/complications ; Aged, 80 and over ; *Diverticulum, Colon/complications/surgery/diagnostic imaging ; *Colonoscopy ; *Electric Power Supplies ; },
abstract = {BACKGROUND Foreign body (FB) ingestion is a common problem in children. In a report of adult FB ingestion, 56% of cases involved individuals with mental illness; the most common foreign bodies were batteries (23%), drug-filled balloons for intervention (17%), razor blades (16%), and others. Standard management of an accidentally swallowed button battery (BB) is radiographic evaluation followed by natural passage through the gastrointestinal tract after traversing the esophagus and stomach. Although cases of BB retention in Meckel's diverticulum have been documented, no reports have described retention in a colonic diverticulum. CASE REPORT An 88-year-old woman with Alzheimer's disease presented with retention of a BB in an ascending colonic diverticulum. Multi-detector computed tomography images showed a radiopaque foreign body near the duodenal bulb. Based on the diagnosis of an ingested BB in the duodenal bulb, emergency upper gastrointestinal endoscopy was performed 2 h after admission. However, an abdominal radiograph obtained after admission showed that the BB had migrated into the right colon. Colonoscopy performed the same day revealed that the BB had entered an ascending colonic diverticulum, from which it was successfully removed. To our knowledge, this is the first reported case of BB retention in a colonic diverticulum. CONCLUSIONS Retention of a BB in the colonic diverticulum, which poses a risk of perforation, should be considered in patients undergoing colonoscopy for suspected foreign body ingestion.},
}

Humans
Female
*Alzheimer Disease/complications
*Foreign Bodies/surgery/diagnostic imaging/complications
Aged, 80 and over
*Diverticulum, Colon/complications/surgery/diagnostic imaging
*Colonoscopy
*Electric Power Supplies

@article {pmid41335440,
year = {2025},
author = {Trieu, C and van Harten, AC and van Leeuwenstijn, MSSA and Schlüter, LM and Boonkamp, L and Aladdin, A and Sikkes, SAM and van de Giessen, E and Verberk, IMW and Teunissen, CE and van der Flier, WM},
title = {Longitudinal Blood-Based Biomarkers and Clinical Progression in Subjective Cognitive Decline.},
journal = {JAMA network open},
volume = {8},
number = {12},
pages = {e2545862},
doi = {10.1001/jamanetworkopen.2025.45862},
pmid = {41335440},
issn = {2574-3805},
mesh = {Humans ; Male ; *Cognitive Dysfunction/blood/diagnosis ; *Biomarkers/blood ; Female ; Middle Aged ; Disease Progression ; tau Proteins/blood ; Aged ; Amyloid beta-Peptides/blood ; Prospective Studies ; Neurofilament Proteins/blood ; Longitudinal Studies ; Glial Fibrillary Acidic Protein/blood ; Peptide Fragments/blood ; },
abstract = {IMPORTANCE: Blood-based biomarkers identify Alzheimer disease and hold promise for monitoring disease progression, even in the preclinical disease stages.

OBJECTIVE: To investigate longitudinal trajectories of blood-based biomarkers and association with cognitive decline and risk of progression in individuals with subjective cognitive decline (SCD).

This prospective cohort study (Subjective Cognitive Impairment Cohort) of individuals with SCD evaluated at a memory clinic underwent biennial biomarker collection and annual cognitive assessment and diagnostic evaluation from January 1, 2005, to December 31, 2023, with follow-up through 2023.

EXPOSURE: Amyloid status was determined using positron emission tomography or cerebrospinal fluid. Plasma Aβ42/40, phosphorylated tau 217 (pTau217), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) were measured biennially.

MAIN OUTCOMES AND MEASURES: Cognitive trajectories in memory, attention, language, executive function, and global cognition and clinical progression to mild cognitive impairment or dementia.

RESULTS: A total of 298 individuals (mean [SD] age, 61.55 [8.08] years; 174 [58.4%] male) with SCD were included, of whom 80 were amyloid-positive (A+) and 218 were amyloid-negative (A-). Mean (SD) follow-up was 4.8 (2.6) years. Individuals with SCD A+ were older (mean [SD] age, 65.25 [7.14] years; 42 [52.5%] male) than those with SCD A- (mean [SD] age, 60.19 [8.00] years; 132 [60.6%] male). For pTau217, GFAP, and NfL, baseline levels were higher in the A+ group compared with A- group (estimates [SE] amyloid β, 1.11 [0.11], 0.69 [0.13], and 0.36 [0.10], respectively; P < .001 for all). Additionally, these biomarkers showed steeper increases over time in the A+ group than in A- group (estimates [SE] time × amyloid status β, 0.07 [0.02], P < .001; 0.07 [0.02], P < .001; and 0.05 [0.02], P = .005, respectively). Longitudinal increases in pTau217 and GFAP were associated with cognitive decline over time in all domains (β time × biomarker slope = -0.02 to -0.04). Longitudinal decreases in Aβ42/40 and increases in NfL were associated with cognitive decline in global cognition (β = 0.03 [0.01], P = .04) and language (β = 0.04 [0.02], P = .03), and increases in NfL were also associated with decline in global cognition (β = -0.02 [0.01], P = .004), language (β = -0.03 [0.01], P = .007), and executive functioning (β = -0.03 [0.01], P = .02). Steeper pTau217 slope was associated with progression from SCD to mild cognitive impairment or dementia (hazard ratio [HR], 3.6; 95% CI, 1.8-7.4 per 0.05 SD increase per year; C index, 0.89; 95% CI, 0.84-0.93), as were steeper GFAP slope (HR, 1.5 [95% CI, 1.0-2.2]; C index, 0.81 [95% CI, 0.73-0.88]) and steeper NfL slope (HR, 2.6 [95% CI, 1.3-5.2]; C index, 0.77 [95% CI, 0.69-0.85]). Aβ42/40 slope was not associated with progression.

CONCLUSIONS AND RELEVANCE: This cohort study of individuals with SCD suggests that longitudinal plasma pTau217 and GFAP are suitable biomarkers for monitoring AD pathology. Their changes were associated with cognitive decline and clinical progression, supporting their potential utility for early intervention and disease monitoring.},
}

Humans
Male
*Cognitive Dysfunction/blood/diagnosis
*Biomarkers/blood
Female
Middle Aged
Disease Progression
tau Proteins/blood
Aged
Amyloid beta-Peptides/blood
Prospective Studies
Neurofilament Proteins/blood
Longitudinal Studies
Glial Fibrillary Acidic Protein/blood
Peptide Fragments/blood

@article {pmid41335394,
year = {2025},
author = {Singh, R and Kaur, V and Ash, A and Singh, M and Tiwari, A and Jain, A and Sunkaria, A},
title = {Orexin-A and Circadian Disruption in Alzheimer's Disease: Implications for Amyloid-Beta Pathology.},
journal = {Molecular neurobiology},
volume = {63},
number = {1},
pages = {246},
pmid = {41335394},
issn = {1559-1182},
support = {231610119916//University Grants Commission/ ; SRG/2022/000575//Science and Engineering Research Board/ ; CRG/2021/001264//Science and Engineering Research Board/ ; },
mesh = {Animals ; *Alzheimer Disease/pathology/metabolism/physiopathology ; *Amyloid beta-Peptides/metabolism ; *Orexins/metabolism ; *Circadian Rhythm/physiology ; Male ; Mice, Inbred C57BL ; Sleep Deprivation/metabolism/complications ; Mice ; Molecular Dynamics Simulation ; },
abstract = {Alzheimer's disease (AD) is characterized by cognitive decline, circadian rhythm disruptions, and accumulation of Aβ plaques. Orexin-A, a neuropeptide involved in regulating sleep and circadian rhythms, has been implicated in these processes, although its specific role in modulating β-amyloid (Aβ) aggregation remains unclear. This study investigates how orexin-A influences Aβ aggregation and its impact on cognitive and circadian dysfunctions in AD mice subjected to acute sleep deprivation (ASD). Behavioural assessments showed significant cognitive deficits following ASD, including impaired recognition and spatial memory. Proteomic analysis revealed 1380 modulated proteins, including 105 associated with AD, 56 with cognitive functions, 11 with circadian rhythm, and six involved in Aβ clearance. Further analysis showed dysregulation of Clock and Bmal1 levels, along with reduced orexin-A expression after ASD. Since orexin-A regulates both sleep and circadian rhythm, investigating its role in modulating Aβ aggregation is essential for understanding the pathophysiology of AD. To explore this, we performed molecular dynamics (MD) simulations to gain insights into the molecular interactions between orexin-A and Aβ. Analysis revealed that orexin-A binds Aβ with high affinity and effectively inhibits its aggregation, suggesting a potential mechanism for reducing Aβ-induced neurotoxicity. These results suggest that orexin-A may play an important role in modulating Aβ aggregation and circadian dysfunction in AD, as supported by simulation results. Further studies manipulating orexin-A levels are needed to confirm its role in this context. Our findings highlight orexin-A as a potential therapeutic target for slowing cognitive decline and neurodegeneration in AD by restoring circadian and sleep-wake regulation.},
}

Animals
*Alzheimer Disease/pathology/metabolism/physiopathology
*Amyloid beta-Peptides/metabolism
*Orexins/metabolism
*Circadian Rhythm/physiology
Male
Mice, Inbred C57BL
Sleep Deprivation/metabolism/complications
Mice
Molecular Dynamics Simulation

@article {pmid41335221,
year = {2025},
author = {Shahzad, A and Ni, Y and Yang, Y and Liu, W and Teng, Z and Bai, H and Liu, X and Sun, Y and Xia, J and Cui, K and Duan, Q and Xu, Z and Zhang, J and Yang, Z and Zhang, Q},
title = {Neutrophil Extracellular Traps (NETs) in health and disease.},
journal = {Molecular biomedicine},
volume = {6},
number = {1},
pages = {130},
pmid = {41335221},
issn = {2662-8651},
support = {No. 82560591//National Natural Science Foundation of China/ ; 82460510//National Natural Science Foundation of China/ ; 82203565//National Natural Science Foundation of China/ ; 82103388//National Natural Science Foundation of China/ ; 31960145//National Natural Science Foundation of China/ ; No. 202201AY070001-011//Yunnan province applied research funds/ ; 202201AY070001-043//Yunnan province applied research funds/ ; 202301AS07001//Yunnan province applied research funds/ ; (CXTD202102)//Science and technology innovation team of tumor metabolism research, Kunming Medical University/ ; 2025S004//Scientific Research Fund of the Education Department of Yunnan Province/ ; },
mesh = {*Extracellular Traps/metabolism/immunology ; Humans ; Animals ; *Neutrophils/metabolism/immunology ; Inflammation/immunology ; },
abstract = {Neutrophil extracellular traps (NETs) are web-like structures composed of DNA, histones, and antimicrobial proteins that extend the defensive repertoire of neutrophils beyond classical phagocytosis and degranulation. Initially considered solely antimicrobial, NETs are now recognized as dynamic regulators of immunity, inflammation, and tissue remodeling. Their formation is orchestrated by the generation of reactive oxygen species, neutrophil elastase-mediated chromatin remodeling, and peptidyl arginine deiminase 4-driven histone citrullination. At the same time, clearance involves DNase activity and macrophage-mediated phagocytosis. In physiological contexts, NETs immobilize and kill pathogens, restrict biofilm formation, and coordinate immune cell crosstalk, thereby supporting host defense and repair. However, when NET formation or clearance becomes dysregulated, these structures drive a broad spectrum of pathologies. Aberrant NET activity has been implicated in infectious diseases (bacterial, viral, fungal), autoimmune disorders such as systemic lupus erythematosus, ANCA-associated vasculitis, rheumatoid arthritis, Gout, and psoriasis, cardiovascular disorders including atherosclerosis, thrombosis, acute coronary syndrome, Myocardial ischemia/reperfusion injury, hypertension, atrial fibrillation, heart failure, and viral myocarditis, as well as cancer progression, metastasis, and other inflammation-associated disorders such as asthma, Alzheimer's disease, diabetes, and pregnancy-related complications. Advances in imaging, proteomics, and single-cell sequencing have expanded our ability to characterize NETs across contexts, revealing stimulus- and disease-specific heterogeneity. At the translational levels, therapies that inhibit NETs formation, promote their degradation, or regulate their release, including PAD4 and elastase inhibitors, DNase-based approaches, and antibody strategies, are under active investigation. By integrating these advances, this review provides a framework for translating NET biology into clinically relevant applications.},
}

*Extracellular Traps/metabolism/immunology
Humans
Animals
*Neutrophils/metabolism/immunology
Inflammation/immunology

@article {pmid41335180,
year = {2025},
author = {Mao, J and Wang, C and Li, X and Du, R and Zhang, X and Shen, R and Yang, A and Kou, X},
title = {Study on the structure-activity relationships of natural γ-pyranone products and their derivatives with anti-AD activities focusing on metal chelation.},
journal = {Molecular diversity},
volume = {},
number = {},
pages = {},
pmid = {41335180},
issn = {1573-501X},
abstract = {Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory loss and cognitive impairment. It seriously affects the health and quality of life of the elderly. It has a complex pathogenesis including β-amyloid (Aβ) deposition, Tau protein hyperphosphorylation, cholinergic neurotransmitter deficiency, metal ion dyshomeostasis, and oxidative stress, etc. Despite intensive research, there is still a lack of effective clinical drugs to treat or control AD progression. Natural products and their derivatives exhibit multi-target anti-AD effects, together with low toxicity and affordability, have emerged as promising lead compounds for drug discovery. This review summarizes the studies on anti-AD activities of natural products bearing γ-pyranone structure and their derivatives, and further discusses their structure-activity relationships (SARs), which provided a theoretical basis for the development of effective anti-AD drugs.},
}

@article {pmid41335161,
year = {2025},
author = {Rucatti, GG and Muñoz-Carvajal, F and Sanhueza, N and Oyarce-Pezoa, S and Cuesta-Astroz, Y and Murgas, L and Caru-Ruiz, M and J M Martin, A and Calegaro-Nassif, M and SanMartín, CD and Sanhueza, M},
title = {The Vitamin D Receptor Ortholog Hr96 Modulates Neuronal and Mitochondrial Dynamics in a Drosophila Model of Alzheimer's Disease.},
journal = {Molecular neurobiology},
volume = {63},
number = {1},
pages = {238},
pmid = {41335161},
issn = {1559-1182},
support = {FB210008//Agencia Nacional de Investigación y Desarrollo/ ; 1117106//Agencia Nacional de Investigación y Desarrollo/ ; 11200981//Agencia Nacional de Investigación y Desarrollo/ ; IE2024-09//Universidad Mayor/ ; Puente2024-24//Universidad Mayor/ ; Semilla HCUCH 2022//Universidad de Chile/ ; },
mesh = {Animals ; *Alzheimer Disease/metabolism/pathology/genetics ; *Neurons/metabolism/pathology ; *Mitochondrial Dynamics ; Disease Models, Animal ; *Drosophila Proteins/metabolism/genetics ; *Drosophila melanogaster/metabolism ; Mitochondria/metabolism ; *Receptors, Calcitriol/metabolism/genetics ; Amyloid beta-Peptides/metabolism ; Oxidative Stress ; },
abstract = {Alzheimer's disease (AD) is the most common cause of dementia, characterized by amyloid-β (Aβ42) accumulation, with a progressive breakdown of synapsis connection, neuronal death, and cognitive loss. Mitochondrial impairment emerges early in AD, preceding cognitive symptoms and contributing to disease progression. Vitamin D (VD) is a neurosteroid that acts as a transcription factor through its nuclear receptor, the vitamin D receptor (VDR), playing a central role in metabolic control. The Drosophila VDR ortholog, hormone receptor 96 (Hr96), is known to regulate xenobiotic protection and energy metabolism, but its neuronal functions and impact on AD pathomechanisms are poorly understood. Here, we investigate Hr96's role in neuronal and mitochondrial homeostasis, hypothesizing that its signaling modulates mitochondrial dynamics and mitigates neurodegeneration in AD. We identified Hr96-regulated genes involved in lipid metabolism, oxidative stress, and mitochondrial dynamics. Modulation of Hr96 expression in fly neurons revealed that knockdown had minimal early effects but led to reduced lifespan and motor decline, while overexpression induced metabolic imbalances, circadian disruptions, and premature mortality. Mitochondrial analyses showed that Hr96 overexpression affected functionality, increased fragmentation, and upregulated fission markers, such as Drp1, suggesting a role in mitochondrial dynamics. Then, when we studied an AD fly model, Hr96 loss exacerbated Aβ42-induced neurotoxicity, reducing lifespan and motor performance. Conversely, Hr96 overexpression extended lifespan under Aβ42 toxicity but did not affect neuromuscular junction bouton number and size. Furthermore, when mitochondrial parameters were analyzed, overexpression of this gene suppresses Aβ42-linked mitochondrial phenotypes to levels closer to wild type. These findings unveil Hr96 as a potential modulator of mitochondrial and neuronal homeostasis, and that in the context of a time-dependent insult such as Aβ42 accumulation, its overexpression is protective. Further studies are needed to elucidate its role in mitochondrial regulation and transcriptional networks, paving the way for therapeutic strategies targeting mitochondrial dysfunction in neurodegeneration.},
}

Animals
*Alzheimer Disease/metabolism/pathology/genetics
*Neurons/metabolism/pathology
*Mitochondrial Dynamics
Disease Models, Animal
*Drosophila Proteins/metabolism/genetics
*Drosophila melanogaster/metabolism
Mitochondria/metabolism
*Receptors, Calcitriol/metabolism/genetics
Amyloid beta-Peptides/metabolism
Oxidative Stress

@article {pmid41335160,
year = {2025},
author = {Ismail, A and Al-Kuraishy, HM and Al-Gareeb, AI and Alexiou, A and Papadakis, M and Faheem, SA and Batiha, GE},
title = {Unveiling the Neuroprotective Effect of Paraoxonase 1 in Neurodegenerative Diseases Focusing on Alzheimer's Disease.},
journal = {Molecular neurobiology},
volume = {63},
number = {1},
pages = {244},
pmid = {41335160},
issn = {1559-1182},
mesh = {Humans ; *Aryldialkylphosphatase/metabolism/therapeutic use ; *Alzheimer Disease/enzymology/drug therapy/pathology/metabolism ; Animals ; *Neuroprotective Agents/therapeutic use/pharmacology/metabolism ; *Neurodegenerative Diseases/drug therapy/enzymology ; Oxidative Stress ; *Neuroprotection ; Blood-Brain Barrier/metabolism ; },
abstract = {Paraoxonase 1 (PON1) is a hydrolyzing paraoxon enzyme; it prevents the development of oxidative stress damage. PON1 primarily binds to high-density lipoprotein (HDL). Although PON1 is not directly expressed in the brain, it crosses the blood-brain barrier (BBB) via HDL, potentially exerting neuroprotective effects. It has been shown that the reduction of PON1 activity is associated with the development and progression of Alzheimer's disease (AD) and other neurodegenerative diseases by inducing oxidative stress and neuroinflammation. Despite these findings, the exact role of PON1 in AD and other neurodegenerative diseases is not fully elucidated. Therefore, the aim of the present review was to discuss and explain the precise role of PON1 in AD, and how PON1 activators could be effective in the management of AD. Clinical trial: not applicable.},
}

Humans
*Aryldialkylphosphatase/metabolism/therapeutic use
*Alzheimer Disease/enzymology/drug therapy/pathology/metabolism
Animals
*Neuroprotective Agents/therapeutic use/pharmacology/metabolism
*Neurodegenerative Diseases/drug therapy/enzymology
Oxidative Stress
*Neuroprotection
Blood-Brain Barrier/metabolism

@article {pmid41335145,
year = {2025},
author = {Banu, Z and Das, NR},
title = {Protective effects of alkaloidal fraction of Elaeocarpus angustifolius Blume against AlCl3-evoked neurotoxicity: insights from an in vivo model of Alzheimer's disease.},
journal = {Metabolic brain disease},
volume = {40},
number = {8},
pages = {330},
pmid = {41335145},
issn = {1573-7365},
mesh = {Animals ; *Alzheimer Disease/drug therapy/chemically induced/metabolism/pathology ; Aluminum Chloride/toxicity ; *Neuroprotective Agents/pharmacology/therapeutic use ; Rats ; Male ; *Plant Extracts/pharmacology/therapeutic use ; Disease Models, Animal ; *Alkaloids/pharmacology/therapeutic use ; Hippocampus/drug effects/metabolism/pathology ; Oxidative Stress/drug effects ; Maze Learning/drug effects ; Amyloid beta-Peptides/metabolism ; },
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and neuronal degeneration. Current treatments offer limited efficacy. Elaeocarpus angustifolius Blume (Rudraksha), used traditionally in Ayurveda for neurological disorders, has shown potential for cognitive health, warranting investigation in AD models. This study aimed to evaluate the neuroprotective efficacy of an alkaloid-rich fraction of E. angustifolius (EAF) in an AlCl3-induced rat model of AD. AD-like symptoms were induced by oral administration of AlCl3 (100 mg/kg) for 60 days, followed by a 30-day oral treatment with EAF (200 and 400 mg/kg). Cognitive performance was assessed using the Morris water maze, elevated plus maze, novel object recognition, and locomotor activity tests. Biochemical and molecular markers were analysed, and hippocampal histopathology was conducted. AlCl3 exposure caused significant cognitive and motor deficits, elevated Aβ1-42 and phosphorylated tau, decreased acetylcholine and dopamine, increased glutamate and NF-κB, and reduced NRF-2 expression, indicating oxidative stress and neuroinflammation. EAF treatment significantly improved behavioral outcomes, reduced Aβ1-42 and tau levels, restored neurotransmitter balance, enhanced antioxidant markers (GSH, SOD, CAT), and reduced MDA. It suppressed NF-κB and upregulated NRF-2, suggesting antioxidant and anti-inflammatory effects. Histopathological analysis confirmed hippocampal neuroprotection. EAF exhibited significant neuroprotective effects by mitigating oxidative stress, neuroinflammation, and AD-related pathologies, including amyloid accumulation and cholinergic dysfunction. These findings support the potential of EAF as a therapeutic candidate for AD prevention and management.},
}

Animals
*Alzheimer Disease/drug therapy/chemically induced/metabolism/pathology
Aluminum Chloride/toxicity
*Neuroprotective Agents/pharmacology/therapeutic use
Rats
Male
*Plant Extracts/pharmacology/therapeutic use
Disease Models, Animal
*Alkaloids/pharmacology/therapeutic use
Hippocampus/drug effects/metabolism/pathology
Oxidative Stress/drug effects
Maze Learning/drug effects
Amyloid beta-Peptides/metabolism

@article {pmid41334933,
year = {2025},
author = {Nagargoje, AA and Panchgalle, SP and Siddiqui, MM and Shaikh, MH and Dipti, DM and Shingate, BB},
title = {Recent Advances in the Biological Activities of Monocarbonyl Curcumin Analogues (MACs).},
journal = {Archiv der Pharmazie},
volume = {358},
number = {12},
pages = {e70164},
doi = {10.1002/ardp.70164},
pmid = {41334933},
issn = {1521-4184},
support = {//The authors received no specific funding for this work./ ; },
mesh = {*Curcumin/pharmacology/analogs & derivatives/chemistry ; Humans ; Structure-Activity Relationship ; Animals ; Antioxidants/pharmacology/chemistry ; Molecular Structure ; Anti-Infective Agents/pharmacology/chemistry ; Hypoglycemic Agents/pharmacology/chemistry ; Anti-Inflammatory Agents/pharmacology/chemistry ; },
abstract = {Curcumin, a principal component of the Indian spice turmeric, exhibits a wide range of biological activities but suffers from poor pharmacokinetic and pharmacodynamic profiles. To overcome these limitations, monocarbonyl analogues of curcumin (MACs) have been developed through structural modifications, resulting in improved bioavailability and enhanced therapeutic potential. This review highlights recent advances in the development of MACs with a focus on their antioxidant, anti-inflammatory, antitubercular, antimicrobial, antidiabetic, antileishmanial, and anti-Alzheimer properties. Moreover, insights into their structure-activity relationships (SAR) are also discussed.},
}

*Curcumin/pharmacology/analogs & derivatives/chemistry
Humans
Structure-Activity Relationship
Animals
Antioxidants/pharmacology/chemistry
Molecular Structure
Anti-Infective Agents/pharmacology/chemistry
Hypoglycemic Agents/pharmacology/chemistry
Anti-Inflammatory Agents/pharmacology/chemistry

@article {pmid41334801,
year = {2025},
author = {Ribeiro, FC and Cozachenco, D and Parkhill, M and Rodrigue, B and Borges, C and Lacaille, JC and Nader, K and De Felice, FG and Lourenco, MV and Aguilar-Valles, A and Sonenberg, N and Ferreira, ST},
title = {Genetic Reduction of the Translational Repressors FMRP and 4E-BP2 Preserves Memory in Mouse Models of Alzheimer's Disease.},
journal = {Aging cell},
volume = {},
number = {},
pages = {e70315},
doi = {10.1111/acel.70315},
pmid = {41334801},
issn = {1474-9726},
support = {//Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro/ ; //Conselho Nacional de Desenvolvimento Científico e Tecnológico/ ; //National Institute of Translational Neuroscience/ ; INSC 406020/2022-1//Instituto Nacional de Ciência e Tecnologia Saúde Cerebral/ ; //Alzheimer's Society Canada/ ; //International Brain Research Organization/ ; AARF-21-848798/ALZ/Alzheimer's Association/United States ; AARG-D-615714/ALZ/Alzheimer's Association/United States ; R-2012-37967//Instituto Serrapilheira/ ; //International Society for Neurochemistry/ ; //International Union of Biochemistry and Molecular Biology/ ; },
abstract = {Alzheimer's disease (AD) is characterized by progressive memory decline. Converging evidence indicates that hippocampal mRNA translation (protein synthesis) is defective in AD. Here, we show that genetic reduction of the translational repressors, Fragile X messenger ribonucleoprotein (FMRP) or eukaryotic initiation factor 4E (eIF4E)-binding protein 2 (4E-BP2), prevented the attenuation of hippocampal protein synthesis and memory impairment induced by AD-linked amyloid-β oligomers (AβOs) in mice. Moreover, genetic reduction of 4E-BP2 rescued memory deficits in aged APPswe/PS1dE9 (APP/PS1) transgenic mouse model of AD. Our findings demonstrate that strategies targeting repressors of mRNA translation correct hippocampal protein synthesis and memory deficits in AD models. Results suggest that modulating pathways controlling brain mRNA translation may confer memory benefits in AD.},
}

@article {pmid41334716,
year = {2025},
author = {Dong, XY and Han, Y and Wang, TB and Han, YB and Liu, L},
title = {Mediators between gut microbiota and Alzheimer's disease: A mediation Mendelian randomization study.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251401157},
doi = {10.1177/13872877251401157},
pmid = {41334716},
issn = {1875-8908},
abstract = {BackgroundSeveral recent studies have confirmed a causal relationship between gut microbiota and Alzheimer's disease (AD), but the potential mediators remain unclear.ObjectiveThis study aimed first to investigate the causal relationship between gut microbiota and AD, and second to explore potential mediators involved in this relationship.MethodsWe used a two-step Mendelian randomization study. Firstly, we mainly used inverse-variance weighted (IVW), weighted median, weighted mode, MR-Egger, and simple mode methods to assess the causal relationship between gut microbiota and AD. Secondly, we conducted mediation analysis to evaluate the roles of inflammatory factors, immune cells, and metabolites in this causal pathway. In addition, we performed sensitivity analysis, Steiger test, and linkage disequilibrium score regression (LDSC).ResultsOur results showed that ten types of gut microbiota were causally associated with AD, of which seven were associated with an increased risk of AD and three with a reduced risk. In addition, the mediation analysis showed that CD45 on Mo MDSC mediated 21.89% of the effect of class Actinobacteria on AD, while the cortisol to taurocholate ratio mediated 18.35% of the effect of genus Lactococcus on AD. Beta-hydroxyisovalerate and glycodeoxycholate levels respectively mediated 10.56% and 16.22% of the effects of class Betaproteobacteria on AD.ConclusionsOur research not only supports modulating gut microbiota as a preventive measure for AD but also emphasizes the mediating roles of inflammatory factors, immune cells, and metabolites. These findings enhance our understanding of the gut-brain axis, providing new perspectives and potential targets for AD prevention.},
}

@article {pmid41334620,
year = {2025},
author = {Falcitano, L and Calizzano, F and Mattioli, P and Kiersnowski, OC and Avanzino, L and Girtler, NG and Diociasi, A and Losa, M and Massa, F and Morbelli, S and Orso, B and Pelosin, E and Bonassi, G and Raffa, S and Pardini, M and Costagli, M and Roccatagliata, L and Arnaldi, D},
title = {MRI susceptibility map weighted imaging (SMWI) as a neurodegeneration biomarker in the prodromal to overt alpha-synucleinopathy continuum.},
journal = {Journal of Parkinson's disease},
volume = {},
number = {},
pages = {1877718X251387027},
doi = {10.1177/1877718X251387027},
pmid = {41334620},
issn = {1877-718X},
abstract = {Background and objectiveNigrostriatal dopaminergic degeneration is commonly assessed using dopamine transporter (DaT) SPECT. Iron sensitive MRI is a promising technique to assess substantia nigra, yet few studies explored its application in the prodromal stage of alpha-synucleinopathies. Here, we used susceptibility map weighted imaging (SMWI) to assess the swallow tail sign, a radiological marker for substantia nigra integrity, to detect neurodegeneration across the alpha-synucleinopathy continuum.Methods3T-MRI was performed on 115 subjects: 27 overt alpha-synucleinopathies, 34 prodromal alpha-synucleinopathies, 28 Alzheimer's disease and 26 healthy controls. SMWI was obtained with 3D multi-echo gradient-echo imaging. The presence/absence of the swallow tail sign was visually evaluated on SMWI by two neuroradiologists, blinded to the diagnosis. Swallow tail sign's visual assessment was compared across groups to investigate its sensitivity and specificity in identifying alpha-synucleinopathies. Additionally, we compared the SMWI visual analysis sign with both substantia nigra quantitative susceptibility mapping (QSM) and DaT-SPECT.ResultsThe two radiologists' inter-rater agreement was substantial (kappa = 0.8). Visual analysis showed good sensitivity (0.85) and specificity (0.82) in identifying patients with alpha-synucleinopathies. When the subjects were grouped based on DaT-SPECT results, sensitivity increased (0.92), while specificity decreased (0.74). Visual scoring was associated with quantitative substantia nigra MRI assessment obtained with QSM (p < 0.001). Lastly, subjects with the swallow tail sign rated as absent showed significantly lower (p = 0.019) uptake at DaT-SPECT (-1.857 ± 1.343) compared to those with the swallow tail sign rated as present (-0.385 ± 1.850).ConclusionsVisual analysis of SMWI swallow tail sign represents a new and reliable approach for evaluating substantia nigra neurodegeneration across the alpha-synucleinopathy continuum.},
}

@article {pmid41334604,
year = {2025},
author = {Cui, X and Ding, Z and Ji, Y and Wang, X and Wang, Y and Yuan, Z and Zhang, Y and Liu, K and Liu, Y},
title = {The combination of gut microbiota and metabolomics reveals the effects of polysaccharides from Schisandra chinensis on microbiota and metabolic profile in Alzheimer's disease rats.},
journal = {Journal of the science of food and agriculture},
volume = {},
number = {},
pages = {},
doi = {10.1002/jsfa.70361},
pmid = {41334604},
issn = {1097-0010},
support = {//National Natural Science Foundation of China Young Scientists Fund/ ; },
abstract = {BACKGROUND: Polysaccharide from Schisandra chinensis (SPJ) can attenuate the progression of Alzheimer's disease (AD) by regulating changes in gut microbiota and its metabolites, but the mechanism of action is unclear. This study aimed to investigate the anti-AD effects and regulatory mechanisms of SPJ in an Aβ25-35-induced AD model from the perspective of the 'microbe-gut-brain' axis.

RESULTS: The results showed that SPJ improved spatial learning memory ability, pathological changes in the hippocampal CA1 region and intestinal barrier integrity, and modulated the composition and abundance of gut microbiota in AD rats. Meanwhile, SPJ also regulated phenylalanine, tyrosine, and tryptophan biosynthesis, and linoleic acid, α-linolenic acid, phenylalanine, and arachidonic acid metabolism in AD rats. Furthermore, correlation analysis revealed a correlation between gut microbes and metabolites.

CONCLUSION: In short, via the 'microbe-gut-brain' axis, SPJ ameliorates cognitive deficits, spatial memory loss, and neuroinflammation in AD rats. © 2025 Society of Chemical Industry.},
}

@article {pmid41334582,
year = {2025},
author = {Materova, TA and Sulimov, AV and Ilin, IS and Varfolomeev, SD and Sulimov, VB},
title = {Search for acetylcholinesterase inhibitors by computerized screening of approved drug compounds.},
journal = {SAR and QSAR in environmental research},
volume = {},
number = {},
pages = {1-19},
doi = {10.1080/1062936X.2025.2592855},
pmid = {41334582},
issn = {1029-046X},
abstract = {This article presents the results of computational screening of approved drug compounds to find new inhibitors of acetylcholinesterase (AChE), an enzyme that plays a key role in the regulation of neurotransmission and cognitive functions. Using molecular docking and quantum chemical postprocessing methods, the authors conducted a virtual screening of a library of 2909 drug compounds approved for clinical use from two ZINC database libraries. The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). As a result of the docking, 211 of the most promising ligands were selected for calculating their enthalpy of binding to AChE using quantum chemical calculations. Based on the analysis of the free energy of binding estimated by docking score and the enthalpy of binding calculated using the quantum-chemical PM7 method with the COSMO solvent model, 16 of the most promising candidates for the role of AChE inhibitors were identified. Notable candidates include Pixantrone, Guanfacine and Hydroxystilbamidine. These compounds, although not previously known as AChE inhibitors, represent diverse chemical classes including substituted thiophenes, pyridines, and fused nitrogen-containing heterocycles, showing high potential for treating neurodegenerative diseases such as Alzheimer's disease.},
}

@article {pmid41334328,
year = {2025},
author = {Peng, R and Lan, M and Zhang, Y and Zhang, S and Yu, B and Yang, X and Li, S and Liu, Z and Kang, W},
title = {Transforming coffee from an empirical beverage to a targeted nutritional intervention: health effects of coffee's core functional components on chronic diseases.},
journal = {Frontiers in nutrition},
volume = {12},
number = {},
pages = {1690881},
pmid = {41334328},
issn = {2296-861X},
abstract = {As one of the most widely consumed beverages worldwide, coffee has garnered increasing scientific interest due to its potential health benefit in recent decades. Epidemiological studies have consistently shown that regular coffee consumption significantly reduces the incidence risks of various chronic diseases, including type 2 diabetes mellitus (T2DM), Alzheimer's disease (AD), cardiovascular disorders, and nephropathies. Pharmacological research further supports these findings, linking the protective effects of coffee to its complex composition of bioactive compounds. Coffee beans contain over 1,000 such compounds, with caffeine, trigonelline, chlorogenic acids (CGAs), cafestol, kahweol, and melanoidins constituting the core functional components. These phytochemicals act through multi-target, synergistic mechanisms that regulate neurological functions, metabolic homeostasis, and inflammatory pathways. This review systematically explores the major bioactive constituents of coffee, focusing on the molecular mechanisms underlying four key biological activities associated with chronic disease prevention: neuroprotection, anti-diabetic/anti-obesity effects, antioxidant activity, and anti-inflammatory properties. By elucidating these pharmacological pathways, we aim to establish a molecular theoretical foundation for repositioning coffee from an empirical beverage into a targeted nutritional intervention agent.},
}

@article {pmid41334059,
year = {2025},
author = {Yi, Y and Lee, CH and Shin, HS and Shin, S},
title = {Oral diseases as emerging risk factors for Alzheimer's disease: A scoping review.},
journal = {The Japanese dental science review},
volume = {61},
number = {},
pages = {292-300},
pmid = {41334059},
issn = {1882-7616},
abstract = {This scoping review examined current evidence on the relationship between oral diseases and Alzheimer's disease (AD). Systematic searches were conducted in PubMed, Scopus, Web of Science, Google Scholar, and KMbase for studies published from 1990 to December 2024, using terms of Alzheimer's disease, dementia, oral health, periodontal disease, dental caries, and tooth loss. Human and validated animal studies investigating microbiological, immunological, inflammatory, genetic, or functional links between oral health and AD were included. Of 1328 records, 841 remained after duplicates were removed, and 98 were reviewed in full; 45 met inclusion criteria. Findings were organized into four themes: general associations; periodontal disease and AD, including inflammation, amyloid-β pathways, and APOE4-related susceptibility; dental caries; and tooth loss with prosthetic rehabilitation. Evidence indicates that chronic oral diseases, especially periodontitis and tooth loss, are associated with increased risk of AD and its progression through mechanisms involving systemic inflammation, microbial translocation, amyloidogenic processes, genetic predisposition, and impaired masticatory function. Appropriate prosthetic rehabilitation may help reduce dementia risk by restoring chewing function and supporting nutrition. While causality has yet to be established, maintaining oral health throughout life may be a practical, cost-effective component of strategies to promote cognitive health in older adults.},
}

@article {pmid41333818,
year = {2025},
author = {An, L and Zhang, J and Wang, X and Ge, Y and Sun, K and Dong, J and Wang, P and Li, W and Li, M and Hu, X and Wang, B and Yu, XA},
title = {Nanocarriers based therapy and diagnosis of brain diseases: cross the blood-brain barrier.},
journal = {Science and technology of advanced materials},
volume = {26},
number = {1},
pages = {2554048},
pmid = {41333818},
issn = {1468-6996},
abstract = {The blood-brain barrier (BBB) is the protective interface that isolates the central nervous system from circulating blood, which restricts approximately 98% of small molecule drugs and nearly all large molecules from entering the brain. Current methods to bypass the BBB, such as laser-guided interstitial thermal therapy and magnetic resonance guided focused ultrasound, are fraught with risks like impairing BBB integrity and brain damage, and are not suitable for long-term treatment. Nanocarriers have emerged as promising tools due to their ability to enhance drug delivery across the BBB while minimizing systemic toxicity. These nanocarriers leverage mechanisms including receptor-mediated, carrier-mediated, cell mediated and extra-stimuli mediated transport to improve BBB traverse and brain targeting. The review evaluates these strategies separately, discussing their potential and limitations for clinical application, and highlights recent advancements in integrating and optimizing nanocarriers utilizing synergistic strategies for the treatment and diagnosis of neurological disorders, including tumors, Alzheimer's disease, Parkinson's disease, and brain infections.},
}

@article {pmid41333297,
year = {2025},
author = {Shaikh, Y and Shahnoor, S and Fahim, MAA and Khan, AM and Shaikh, T and Moeed, A and Asghar, MS},
title = {Trends in Alzheimer's disease and heart failure-related mortality among older American adults: Insights from the CDC WONDER database.},
journal = {American heart journal plus : cardiology research and practice},
volume = {60},
number = {},
pages = {100677},
pmid = {41333297},
issn = {2666-6022},
abstract = {INTRODUCTION: Alzheimer's disease is one of the leading causes of death among the elderly in the United States with heart failure sharing similar risk factors. This study investigated trends and disparities in Alzheimer's disease mortality among older adults with heart failure from 1999 to 2020 in the United States.

METHODS: Making use of ICD-10 codes death certificate data from the Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research database was retrieved for patients aged ≥65 years between 1999 and 2020. Age-adjusted mortality rates (AAMRs), per 100,000 people, and Annual Percentage Change (APCs) with their respective 95 % Confidence Intervals (CI) were also calculated. Data was stratified by year, gender, race and geographical distribution.

RESULTS: Alzheimer's disease with coexisting heart failure was responsible for 192,459 deaths between 1999 and 2020. Overall the AAMR increased from 21.32 in 1999 to 24.56 in 2005 (APC: 1.9760*; 95 % CI: 0.6001 to 3.9507) after which a significant decrease to 16.52 by 2013 was observed (APC: -4.9301*; 95 % CI: -6.5209 to -4.0119). AAMRs decreased from this point forward reaching 22.21 in 2020 (APC: 4.1573*; 95 % CI: 3.0373 to 5.7232). Women had higher AAMRs than men (21.57 vs 18.41). Among racial groups, the Non-Hispanic (NH) White (21.62) population had the highest AAMRs followed by NH Black/African American (17.87), Hispanic/Latino (14.3) and NH Asian/Pacific Islander (8.96). Furthermore, AAMRs also varied by census region (West: 24.05; Midwest: 22.83; South: 21.1; Northeast: 13.38). Moreover, nonmetropolitan areas had higher AAMRs compared to metropolitan areas (27.23 vs 19.09). States in the top 90th percentile such as Kentucky, Oklahoma, Washington, North Dakota and Mississippi had AAMRs that were three times higher relative to states in the lower 10th percentile including Nevada, Florida, New York, District of Columbia and Hawaii.

CONCLUSION: Alzheimer's disease mortality with associated heart failure has shown considerable variation in adults ≥65 years. AAMRs were highest in women, NH Whites, residents of the West and nonmetropolitan patient populations. Targeted interventions and a more holistic approach to patient management are essential in achieving favorable outcomes for vulnerable groups moving forward.},
}

@article {pmid41333169,
year = {2025},
author = {Farki, A and Moradi, E and Koundal, D and Tohka, J},
title = {Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {41333169},
issn = {2331-8422},
abstract = {Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer's disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dementia. Instead, here we investigate longitudinal MRI image-to-image prediction that forecasts a participant's entire brain MRI several years into the future, intrinsically modeling complex, spatially distributed neurodegenerative patterns. We implement and evaluate five deep learning architectures (UNet, U2-Net, UNETR, Time-Embedding UNet, and ODE-UNet) on two longitudinal cohorts (ADNI and AIBL). Predicted follow-up MRIs are directly compared with the actual follow-up scans using metrics that capture global similarity and local differences. The best performing models achieve high-fidelity predictions, and all models generalize well to an independent external dataset, demonstrating robust cross-cohort performance. Our results indicate that deep learning can reliably predict participant-specific brain MRI at the voxel level, offering new opportunities for individualized prognosis.},
}

@article {pmid41333020,
year = {2025},
author = {Lang, J and Guo, K and Yang, J and Yang, P and Wei, Y and Han, J and Zhao, S and Liu, Z and Yi, H and Yan, X and Chen, B and Wang, C and Xu, J and Ge, J and Zhang, W and Zhou, X and Fang, J and Su, J and Yan, K and Hu, Y and Wang, W},
title = {SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.},
journal = {Frontiers in pharmacology},
volume = {16},
number = {},
pages = {1698202},
pmid = {41333020},
issn = {1663-9812},
abstract = {INTRODUCTION: In recent years, the increasing complexity and volume of data in traditional Chinese medicine (TCM) research have rendered the conventional experimental methods inadequate for modern TCM development. The analysis of intricate TCM data demands proficiency in multiple programming languages, artificial intelligence (AI) techniques, and bioinformatics, posing significant challenges for researchers lacking such expertise. Thus, there is an urgent need to develop user-friendly software tools that encompass various aspects of TCM data analysis.

METHODS: We developed a comprehensive web-based computing platform, SZBC-AI4TCM, a comprehensive web-based computing platform for traditional Chinese medicine that embodies the "ShuZhiBenCao" (Digital Herbal) concept through artificial intelligence, designed to accelerate TCM research and reduce costs by integrating advanced AI algorithms and bioinformatics tools.

RESULTS: Leveraging machine learning, deep learning, and big data analytics, the platform enables end-to-end analysis, from TCM formulation and mechanism elucidation to drug screening. Featuring an intuitive visual interface and hardware-software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer's disease as an example, we showcase its functionalities, operational methods, and analytical capabilities.

DISCUSSION: SZBC-AI4TCM not only provides robust computational support for TCM research but also significantly enhances efficiency and reduces costs. It offers novel approaches for studying complex TCM systems, thereby advancing the modernization of TCM. As interdisciplinary collaboration and cloud computing continue to evolve, SZBC-AI4TCM is poised to play a strong role in TCM research and foster its growth in addition to contributing to global health. SZBC-AI4TCM is publicly for access at https://ai.tasly.com/ui/\#/frontend/login.},
}

@article {pmid41332987,
year = {2025},
author = {Huai, JX and Chang, EE and Zhu, YR and Ma, WL and Lv, TS and Sun, J and Zhou, XQ},
title = {Diabetic encephalopathy: metabolic reprogramming as a potential driver of accelerated brain aging and cognitive decline.},
journal = {Frontiers in cell and developmental biology},
volume = {13},
number = {},
pages = {1701406},
pmid = {41332987},
issn = {2296-634X},
abstract = {Diabetic encephalopathy (DE) is a serious neurological complication of diabetes and is expressed as progressive decline in cognitive function, emotional disorders, and changes in brain structure. This review brings together the relevant evidence and demonstrates that metabolic reprogramming, the adaptive reconfiguration of the core metabolic pathway in response to hyperglycemia, is a potential driver of accelerated brain aging in DE. The main pathological characteristics are: abnormal brain insulin signaling, resulting in a decrease in neuronal glucose intake and a decrease in mitochondrial oxidative phosphorylation, oxidative stress and neuroinflammation caused by high blood sugar, in which excess reactive oxygen species (ROS), impairs mitochondrial integrity and leads to activation of microglia cells. The impaired mitophagy and the macrophages remove defects and cause the accumulation and energy collapse of the dysfunctional organelles. In addition, it promotes excessive glycolytic flux, lipolysis disorder, lactic acid accumulation, and ceramide-dependent synaptic damage. We further examine shared metabolic mechanisms between DE and neurodegenerative diseases such as alzheimer's disease (AD) and treatment strategies for pathological metabolic reprogramming including GLP-1 receptor agonists, NAD[+] boosters, and AMPK activators. This analysis laid the foundation for new intervention measures against the development of DE.},
}

@article {pmid41332878,
year = {2025},
author = {Cribb, L and Moreno-Betancur, M and Sarant, J and Wolfe, R and Pase, MP and Rance, G and Mielke, MM and Murray, AM and Owen, A and Woods, RL and Zhou, Z and Wu, Z and Sheets, KM and Chong, TT and Shah, RC and Ryan, J},
title = {The Effect of Treating Hearing Loss with Hearing Aids on Plasma Biomarkers of Alzheimer's Disease and Related Dementias.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.19.25340558},
pmid = {41332878},
abstract = {BACKGROUND: Promising evidence indicates that treating hearing loss with hearing aids (HAs) could reduce dementia risk. We extend this evidence by investigating the effect of HAs on plasma biomarkers of Alzheimer's disease and related dementias (ADRD).

METHODS: We emulated two target trials using observational data from Australian participants of the ASPREE study. Eligible participants had self-reported hearing problems, no past HA use, and were dementia-free. HA prescriptions and frequency of HA use were measured by questionnaire. Phosphorylated-tau181 (pTau181), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and amyloid-β (Aβ) 42/40 were measured after approximately 6-8 years. We estimated the effect of new HA prescription (first target trial) and the frequency of HA use (second target trial) using targeted maximum likelihood estimation, with multiple imputation for missing data.

RESULTS: Across imputed datasets, a median of 2842 eligible individuals were included (mean age 75 years, 48% female), with a median of 735 receiving a new HA prescription. Among survivors, the estimated mean differences comparing HA prescription and no HA prescription were 1.8 pg/mL (95% CI: -0.6, 4.1), 0.1 pg/mL (-7.8, 8.0), -2.2 pg/mL (-14.5, 10.1), and -0.7 (-2.6, 1.2) for the concentrations of pTau181, NfL, GFAP, and (Aβ42 x 1000)/Aβ40, respectively. Mean differences did not differ substantially across levels of potential baseline effect modifiers, including APOE-ε4 genotype and cognition.

CONCLUSION: In community-dwelling older people with hearing loss and no dementia, we found minimal effects of HA prescription and frequency of HA use on plasma ADRD biomarkers after a 7-year follow-up.},
}

@article {pmid41332870,
year = {2025},
author = {Novotny, JS and Čarná, M and Lyburn, I and Kuruvilla, T and Stokin, GB and , },
title = {The spectrum of Alzheimer's disease.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.17.25340406},
pmid = {41332870},
abstract = {While hippocampal (H) and inferior parietal lobule (IPL) atrophy are used routinely in Alzheimer's disease (AD) diagnostics, the role of enlarged choroid plexus (ChP) remains unclear. We here examined the AD Neuroimaging Initiative (ADNI) cohort (N=872) to investigate the contribution of enlarged ChP in predicting AD using MRI volumetry. Analyses revealed that no individual volumetric brain changes, nor their combination, can predict AD. Among AD patients, only ∼ 19%, 12% and 5% exhibited changes in H, ChP or IPL volumes, while 45% showed no volumetric brain changes at all, not even longitudinally. Amyloid-b peptides, therefore, contribute to brain atrophy and neuronal loss at best only in a subset of amyloid PET-CT positive AD patients. These findings suggest that despite shared amyloidopathy, the observed brain volumetric phenotypes, together with their corresponding cognitive and CSF biomarker profiles, represent unique entities within the AD spectrum much like the synucleinopathies, tauopathies and TDP43-proteinopathies.},
}

@article {pmid41332865,
year = {2025},
author = {Ellis, RJ and Airaud, A and Jasodanand, VH and Kowshik, SS and Bellitti, M and Kolachalama, VB and Estiri, H and Glymour, MM and Dufouil, C and Sperling, RA and Bennett, DA and Patel, CJ and , and , },
title = {Prognostic performance across Alzheimer's biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.19.25340533},
pmid = {41332865},
abstract = {IMPORTANCE: Evaluating prognostic performance of Alzheimer's biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals versus established risk factors in asymptomatic individuals is can inform effcient screening strategies.

OBJECTIVE: To determine and compare the prognostic performance of amyloid biomarkers, multi-modal physiological measures, and clinical/modifiable risk fac-tors [1] , we conducted a modality-wide assessment of predictors of AD (MODAL-AD) in cognitively asymptomatic patients.

DESIGN: We used clinical trials (A4/LEARN), longitudinal cohorts (ADNI, AIBL, HABS, NACC, OASIS), and the UK Biobank spanning 2004-2025 (median follow-up time range: 1.8-13.72 years) in time-varying survival and binary classification analyses.

SETTING: Settings included a United States clinical trial, longitudinal cohort studies spread across medical centers in the United States and Australia, and the volunteer-based UK Biobank.

PARTICIPANTS: Patients were cognitively asymptomatic and age 65+ at baseline, and potentially progressed to either clinical impairment, clinical AD diagnosis, or incurred AD ICD-codes. Patients were volunteer or convenience samples.

EXPOSURES: PTau-217, amyloid-PET, CSF markers (AB1-42, pTau-181, total-Tau), plasma proteomics, multimodal brain-imaging, and cognitive tests were evaluated as predictors, along with demographics (age, sex, education), APOE geno-type, and modifiable risk factors in the 2024 Lancet report [1] .

MAIN OUTCOMES AND MEASURES: PTau-217 and amyloid-PET from A4/LEARN were used to predict clinical impairment (CDR score of 0.5+ on two consecutive visits). PTau-217, amyloid-PET imaging across five cohorts, and CSF markers were used to predict clinical AD diagnosis. Plasma proteomics, multimodal neuroimaging, and cognitive assessments from the UK Biobank were used to predict AD ICD-codes.

RESULTS: Sample-sizes ranged from 356-28,533 (31-519 cases; female percentages: 48.45-67.39). Models of demographics, APOE genotype, and risk-factors as predic-tors did not show statistically significant differences in time-dependent area under the receiver operating characteristic curve (AUROC) compared to separate models using amyloid biomarkers. Predicting cognitive impairment in A4/LEARN, pTau-217 improved AUROC by 0.045-0.084 (best: 0.616 (CI: 0.51-0.723) vs. 0.7 (CI: 0.609-0.793)). Amyloid-PET improved AD prediction (maximum AUROC increase 0.074; 0.561 (CI: 0.468-0.653) vs. 0.635 (CI: 0.537-0.733)), and CSF biomarkers showed slightly larger gains (maximum AUROC increase 0.127; 0.627 (CI: 0.438-0.816) vs. 0.754 (CI: 0.577-0.931)). In UK Biobank analyses, mean AUROC improvements were minor across proteomics (0.044), neuroimaging (0.143, with 99.8%/0.2% class-balance), and cognitive tests (0.064).

CONCLUSIONS AND RELEVANCE: In cognitively asymptomatic populations, biomarkers offer limited advantage over demographics, APOE genotype, and modifi-able risk factors, supporting their importance in early AD screening strategies.

QUESTION: How does the prognostic performance of amyloid biomarkers (i.e., pTau-217, amyloid-PET, cerebrospinal fluid markers) and discovery-driven modalities (i.e., plasma protoemics, multimodal brain imaging, cognitive tests) compare to demo-graphics and modifiable risk factors for predicting clinical impairment, clinical AD diagnosis, and AD ICD code outcomes in asymptomatic patients?

FINDINGS: In this prognostic study of > 300,000 patients, across cohorts, physiological modalities, and outcomes, predictive performance of demographics and modifiable risk factors did not statistically significantly differ from amyloid biomarkers, plasma proteomics, and other modalities.

MEANING: Alzheimer's screening in asymptomatic patients can benefit from incorpo-rating modifiable risk factors as additional predictors to amyloid biomarkers.},
}

@article {pmid41332864,
year = {2025},
author = {Maciel Felinto, T and Worth, T and Welch, E and Cabeza, R},
title = {DEVELOPING A PROTOCOL USING WEARABLE CAMERAS FOR MEMORY TRAINING WITH OLDER ADULTS: A METHODOLOGICAL REPORT.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.17.25339784},
pmid = {41332864},
abstract = {Although not as pronounced as in Alzheimer's Disease, healthy aging is associated with substantial deficits in memory functions, particularly in episodic and autobiographical memory. Efforts to train these impaired functions are hindered by the lack of transfer of the training effects to other tasks and daily activities and limited by artificial laboratory stimuli. Addressing these limitations, we developed an autobiographical memory training protocol using wearable cameras to record participants' own life events as photos, and laptops to train their memory using these events at home. We tested the feasibility of the protocol in a study with 15 healthy older adults. The results showed that the protocol and set of instructions we designed enabled participants to use the equipment (wearable camera and laptop) successfully at home. In a novel addition, we used automated image processing to protect the privacy of the participants' photos, which are considered Protected Health Information. Older adults' responses to the overall study and camera use was positive, indicating that studies using wearable cameras can be engaging and motivating, and potentially even successful in improving memory. The methods will be the central focus on this paper, and the results will be expanded upon in a subsequent paper.},
}

@article {pmid41332861,
year = {2025},
author = {Wiranto, Y and Setiawan, DR and Watts, A and Ashourvan, A and , },
title = {Development of Alzheimer's Disease Risk Score for Future Primary Care: A White-Box Approach.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.08.02.24311399},
pmid = {41332861},
abstract = {IMPORTANCE: Interpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimer's disease (AD) and promote early intervention at non-specialist settings.

OBJECTIVE: To develop a risk score to predict the likelihood of AD with interpretable machine learning using variables that are obtainable at integrated primary care settings.

DESIGN: A secondary data analysis including cohort studies from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the National Alzheimer's Coordinating Center (NACC) extracted in August 2023 and March 2024.

SETTING: The ADNI and NACC are multi-site cohort studies in North America.

PARTICIPANTS: Participants with normal cognition or mild cognitive impairment at baseline visit were identified. Participants with the same diagnosis overtime were assigned to the stable group, and those converted to AD were placed in the progressive group.

MAIN OUTCOMES AND MEASURES: Cognitive tests and daily functioning measured with Functional Assessment Questionnaire (FAQ) at baseline visit.

RESULTS: A total of 676 participants from ADNI and 4592 participants from NACC were identified. After removing incomplete data, 665 ADNI (mean age [SD]: 73.44 [6.90]; 293 [44.1%] female; 374 stable and 291 progressive) and 3657 NACC participants (mean age [SD]: 70.96 [10.03]; 2405 [65.8%] female; 2445 stable and 1212 progressive) remained. Combinations of 4 measures were selected to generate 10 scorecards using FasterRisk algorithm, showing strong performance (area under the curve [AUC] = 0.868-0.892) in ADNI and remaining robust when validated in NACC (AUC = 0.795). The features were Category Animal ≤ 20 (2 points), Trail Making Test B ≤ 143 (-3 points), Logical Memory Delayed ≤ 3 (4 points), Logical Memory Delayed ≤ 8 (3 points), and FAQ ≤ 2 (-5 points). The probable AD risk corresponded to total points: 7.4% (-8), 25.3% (-4), 50% (-1), 74.7% (2), and > 90% (≥ 6). We refer to this model as the (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or FLAME scorecard.

CONCLUSIONS AND RELEVANCE: Our findings highlight the potential to predict AD development using obtainable information, allowing for applicability at integrated primary care. While our scope centers on AD, this foundation paves the way for other dementia types.

KEY POINTS: Question: Can accessible information, such as demographics, cognitive tests, and functioning questionnaire, yield in reliable results for predicting Alzheimer's disease development using interpretable machine learning?Findings: The results of 665 participants from the Alzheimer's Disease Neuroimaging Initiative demonstrated robust performance of determining Alzheimer's disease development using four separate measures of (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or the FLAME scorecard. It remains reliable when externally validated with a separate dataset of 3657 participants from the National Alzheimer's Coordinating Center.Meaning: The FLAME scorecard shows potential to be implemented in integrated primary care settings to promote early detection and intervention of cognitive decline due to Alzheimer's disease.},
}

@article {pmid41332834,
year = {2025},
author = {Cohen, CE and Fernandez, SM and Yaman, U and Ehyaei, AR and Porter, T and O'Brien, E and , and , and Maruff, P and Hardy, J and Laws, SM and Salih, DA and Shoai, M},
title = {Genetic drivers of progression in Alzheimers disease are distinct from disease risk.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.17.25340247},
pmid = {41332834},
abstract = {BACKGROUND: Recent trials in Alzheimers disease (AD) demonstrate encouraging outcomes. These trials target risk mechanisms identified through genetic analysis whilst directly aiming to reduce progression rates. Evidence from other neurodegenerative diseases suggests the genetics of progression is distinct from risk of disease. To expand these initial successes and improve clinical outcomes further we need to understand genetics of progression of disease. These can be deduced through rigorous analysis of meticulously phenotyped longitudinal cohorts. In this study we first looked at known genetic drivers of risk, namely polygenic risk scores for AD and APOE-ϵ4, to assess their role in progression. This was then extended to a genome wide association analysis to identify the role of other genetic variants in progression of AD.

METHODS: A total of 387 individuals with, genetic data, amyloid positivity and in active decline (ADNI (n=222) and AIBL(n=165)) were used to perform generalised mixed effects linear model genome wide association studies of longitudinal cognitive decline as measured by mini mental state examination. The resulting summary statistics were subjected z, and colocalization analyses.

RESULTS: Established AD risk factors, including APOE-ϵ4 dosage and polygenic risk scores, were not associated with disease progression amyloid positive individuals who are actively declining. A mixed effects GWAS meta analysis revealed one genome wide significant locus on chromosome 22 (rs78369883) and 25 nominally significant loci linked with AD progression. Functional annotation, finemapping, and colocalization analyses implicated genes primarily involved in immune response, neurodegeneration (including tau pathology), brain resilience, and neurogenesis. These progression-related genes were significantly enriched in neuronal-interferon-microglial signalling pathways and normal homeostatic processes of neuronal networks, with specific enrichment in dopaminergic and inhibitory neuronal populations.

CONCLUSION: These findings enhance our understanding of the biological underpinnings of AD progression, opening new avenues for therapeutic intervention.},
}

@article {pmid41332818,
year = {2025},
author = {Bettonagli, V and Galli, A and Bazzoli, E and Tolassi, C and Caratozzolo, S and Gumina, B and Libri, I and Ferreira, D and Outeiro, TF and Pilotto, A and Padovani, A and , },
title = {The Impact of Insulin Resistance on Grey Matter Changes Along the Alzheimer's Disease Continuum Insulin Resistance and Grey Matter in AD.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.17.25339566},
pmid = {41332818},
abstract = {BACKGROUND AND OBJECTIVES: Insulin resistance is emerging as a modifiable risk factor for Alzheimer's, though its impact on grey matter volume across clinical stages remains poorly understood. The objective of the research is to investigate how insulin resistance affects grey matter integrity across the Alzheimer's disease continuum using structural MRI.

METHODS: Imaging, clinical, and metabolic data were extracted from 374 non-diabetic participants within the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Participants were classified as cognitively impaired (CI: n=186; 137 mild cognitive impairment, 49 early-to-moderate dementia; all AD biomarker positive) or cognitively unimpaired (CU: n=188; 122 amyloid-negative, 66 amyloid-positive). Insulin resistance was assessed at the time of MRI and clinical evaluation using the dichotomized triglyceride-glucose index (TyG). The Interactions between TyG and diagnostic group on grey matter volume were investigated using both voxel-wise and region-of-interest (ROI) based analyses, adjusted for age, sex, education, vascular risk factors, and global cognitive performance across the AD continuum.

RESULTS: Insulin resistance significantly impacted gray matter volume across the AD continuum, demonstrating stage-dependent effects. In early AD disease stages, insulin resistance was associated with lower grey matter volume in fronto-parietal regions, a finding that extended to several cortical areas in CI individuals. Temporal and fronto-limbic regions were particularly highlighted by the IR-diagnosis interaction. In amyloid-positive CU individuals, IR was linked to bilateral temporal atrophy, in contrast to amyloid-negative CU participants.

DISCUSSION: This study underscores the impact of insulin resistance on brain structure across the AD continuum, particularly within key vulnerability areas characteristic of AD pathology. These findings highlight the need for future research into potential therapeutic strategies targeting insulin signaling to mitigate neurodegeneration in AD.},
}

@article {pmid41332807,
year = {2025},
author = {Mastaglio, E and Egleston, B and Lee, KT and Fetzer, D and Brown, S and Domchek, SM and Fleisher, L and Wen, KY and Wagner, L and Roberts, JS and Cacioppo, C and Christiansen, J and Howe, S and Wood, EM and Weinberg, M and Karpink, K and Selmani, E and Feng, J and John, S and Schweickert, K and McLeod, B and Bradbury, AR},
title = {A Randomized Hybrid Type I Effectiveness-Implementation Study of an eHealth Delivery Alternative for Cancer Genetic Testing for Hereditary Cancer (eREACH2): study protocol.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.19.25340515},
pmid = {41332807},
abstract = {BACKGROUND: Germline cancer genetic testing has become a standard evidence-based practice, with established risk reduction and cancer screening guidelines for genetic carriers. There are also targeted treatments for some genetic carriers with cancers such as breast and ovarian cancer. Yet, many at-risk patients do not have access to genetic services, leaving many genetic carriers unidentified. The eREACH 2 study (A Randomized Hybrid Type I Effectiveness-Implementation Study of an eReach Delivery Alternative for Cancer Genetic Testing for Hereditary Cancer) evaluates whether an interactive, patient-centered digital alternative for genetic education and disclosure of results is non-inferior compared to the traditional model of pre-and post-test counseling with a genetic counselor.

METHODS: This is a Hybrid Type 1 effectiveness implementation study in which participants are randomized using a 2×2 design to test a self-directed, patient-informed, digital intervention to deliver clinical genetic testing versus the traditional pre-test (visit 1) and post-test (visit 2: disclosure) counseling delivered by a genetic counselor. The 4 arms include A) genetic counselor for visit 1 and visit 2, B) genetic counselor for visit 1 and digital intervention for visit 2, C) digital intervention for visit 1 and genetic counselor for visit 2, and D) digital intervention for both visits. Participants are adults who meet National Comprehensive Cancer Network and/or American Society of Clinical Oncology guidelines for germline genetic testing and are recruited from both community and medical sites across the United States by way of clinician referral as well as patient self-referral. The primary outcomes are non-inferiority in uptake of genetic testing and change in genetic knowledge and general anxiety from baseline to post-disclosure.

DISCUSSION: With many barriers to accessing genetic services, innovative delivery models are needed to address these gaps and increase uptake of genetic services. The eREACH2 study evaluates the effectiveness of an interactive patient-centered digital intervention to deliver clinical genetic testing. We expect this work will inform evidence-based guidelines and the standard-of-care for delivery of genetic testing and is designed to be broadly applicable and easily adaptable for other populations and settings even beyond oncology (e.g. Alzheimer's disease).

TRIAL REGISTRATION: This protocol was registered at clinicaltrials.gov (NCT05427240) on 6/7/2022.},
}

@article {pmid41332786,
year = {2025},
author = {Huuki-Myers, LA and Divecha, HR and Bach, SV and Valentine, MR and Eagles, NJ and Mulvey, B and Bharadwaj, RA and Zhang, R and Evans, JR and Grant-Peters, M and Miller, RA and Kleinman, JE and Han, S and Hyde, TM and Page, SC and Weinberger, DR and Martinowich, K and Ryten, M and Maynard, KR and Collado-Torres, L},
title = {APOE E4 Alzheimer's Risk Converges on an Oligodendrocyte Subtype in the Human Entorhinal Cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.20.689483},
pmid = {41332786},
issn = {2692-8205},
abstract = {UNLABELLED: The entorhinal cortex (ERC) is implicated in early progression of Alzheimer's disease (AD). Here we investigated the impact of established biological risk factors for AD, including APOE genotype (E2 versus E4 alleles), sex, and ancestry, on gene expression in the human ERC. We generated paired spatially-resolved transcriptomics (SRT) and single-nucleus RNA sequencing data (snRNA-seq) in postmortem human ERC tissue from middle aged brain donors with no history of AD. APOE -dependent changes in gene expression predominantly mapped to a transcriptionally-defined oligodendrocyte subtype, which varied substantially with ancestry, and suggested differences in oligodendrocyte differentiation and myelination. Integration of SRT and snRNA-seq data identified a common gene expression signature associated with APOE genotype, which we localized to the same oligodendrocyte subtype and a white matter spatial domain. This suggests that AD risk in ERC may be associated with disrupted oligodendrocyte function, potentially contributing to future neurodegeneration.

LAY SUMMARY: Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for 60-80% dementia cases. Apolipoprotein E (APOE) genotype is the strongest genetic risk factor for AD, and the entorhinal cortex (ERC) is a brain region implicated in its earliest progression. Our study investigated how APOE genotype impacts gene expression in the ERC. We identified genotype-dependent effects on oligodendrocytes with different transcriptional profiles related to maturation that may help explain how APOE genotype mediates its effects on AD risk.},
}

@article {pmid41332773,
year = {2025},
author = {Chlebowski, AC and Yang, Y and Siddique, NA and Siddique, T and Spencer, PS and Steele, JC and Kisby, GE},
title = {Development of Patient-Derived Neuroprogenitor Cells (hNPCs), Neurons and Astrocytes to Explore the Etiology of Guam Parkinsonism-Dementia Complex (PDC).},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.10.07.680309},
pmid = {41332773},
issn = {2692-8205},
abstract = {Parkinsonism-Dementia Complex (PDC) is one phenotype of a disappearing neurodegenerative disease (Guam ALS-PDC) that shows clinical and neuropathological relationships with amyotrophic lateral sclerosis (ALS), atypical parkinsonism and Alzheimer's disease. ALS-PDC has been linked with exposure to environmental factors (notably cycad plant neurotoxins), but evidence from human and animal studies is inconclusive. Patient-derived induced pluripotent stem cells (iPSCs) provide a powerful in vitro system to explore the underlying cause of PDC. iPSC lines were derived from lymphocytes of a PDC-affected Guamanian Chamorro female patient and an age- and gender-matched healthy Chamorro resident of PDC-unaffected Saipan using non-integrating episomal plasmids. iPSCs derived from both patients expressed pluripotency markers (Oct4, SSEA-4, TRA-1-60, Sox2) prior to the generation of neuroprogenitor cells (hNPCs), neurons and astrocytes. An embryoid body protocol was used to derive hNPCs from both iPSC lines while a differentiation media was used to generate neurons from hNPCs. hNPCs derived from both iPSC patients' lines displayed established neuroprogenitor markers (nestin, Sox2), while the differentiated hNPCs exhibited both neuronal (beta-tubulin III, Map2, doublecortin) and synaptic (synaptophysin, PSD-95) markers. Expression of these protein markers in hNPCs and neurons by dot blotting was also observed for both lines. Astrocyte progenitor cells and mature astrocytes with appropriate markers were also developed from the hNPCs of both lines using commercial kits. Development of these patient-derived iPSCs provides a human model for evaluating the role of environmental (e.g., cycad toxins) and genetic factors in ALS-PDC and possibly other related neurodegenerative diseases.},
}

@article {pmid41332677,
year = {2025},
author = {Rao, I and Kellis, M and Tanigawa, Y},
title = {sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.19.689166},
pmid = {41332677},
issn = {2692-8205},
abstract = {A precise understanding of disease-associated spatio-temporal transcriptional dynamics is critical for nominating therapeutic interventions that drive desired perturbation responses. In disease contexts, pathological processes unfold across diverse cellular states, spatial environments, and timescales; however, current computational approaches have a limited ability to jointly model these complex dynamics or infer cellular trajectories from in silico perturbation experiments. Here, we present sc4D, a biologically interpretable spatio-temporal (4D) analysis framework for single-cell transcriptomics in disease studies, integrating autoencoder embeddings with optimal transport. Applying sc4D to longitudinal spatial transcriptomics samples from Alzheimer's disease mouse models, we report known disease biology and novel, testable mechanisms, including late-stage microglia-astrocyte syncytium near amyloid-β plaques. In silico perturbations predict the restoration of protective, anti-inflammatory microglia through CAMTA1 activation or Donepezil, as validated by independent experimental findings. Overall, our work highlights the critical benefits of joint spatio-temporal modeling for elucidating disease mechanisms and predicting candidate interventions to improve cellular response.},
}

@article {pmid41332529,
year = {2025},
author = {Sun, X and Hudson, HR and Orr, TC and Koutarapu, S and Rosenbloom, A and Ingalls, M and Braubach, O and Keene, CD and Beechem, JM and Orr, ME},
title = {Spatial Multi-Omics Workflow and Analytical Guidelines for Alzheimer's Neuropathology.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.16.688710},
pmid = {41332529},
issn = {2692-8205},
abstract = {Spatial biology technologies enable high-dimensional profiling within intact tissues, revealing how molecular and cellular organization drives function and disease. As these platforms gain broader adoption, standardized analytical frameworks are needed to ensure data quality and reproducibility. Here, we present an end-to-end pipeline for the GeoMx Digital Spatial Profiler that simultaneously generates whole-transcriptome and 637-protein measurements from user-defined regions within the same tissue sections. The workflow integrates morphology-guided region selection, quality control, normalization, and multi-modal data interpretation. Applied to formalin-fixed cortical tissues from Alzheimer's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, and controls, the framework resolves spatially distinct molecular domains. Transcript and protein signals diverge across amyloid plaque cores and surrounding glial-rich regions, with RNA-protein concordance varying by disease condition, while single-neuron profiling with and without pathogenic tau deposition illustrates protein assay sensitivity. This dataset provides a rigorously validated resource for spatial multi-omic analyses and establishes broadly applicable guidelines for reliable, reproducible profiling of complex tissues.},
}

@article {pmid41332414,
year = {2025},
author = {Zhao, X and Xu, Z and Wang, D and Li, T and Xu, L and Li, Z and Bai, X and Zhu, H and Liu, Y and Wang, Y},
title = {Nanotechnology-based targeted regulation of NLRP3 Inflammasome: therapeutic strategies and clinical application prospects in inflammatory diseases.},
journal = {Drug delivery},
volume = {32},
number = {1},
pages = {2580730},
doi = {10.1080/10717544.2025.2580730},
pmid = {41332414},
issn = {1521-0464},
mesh = {*NLR Family, Pyrin Domain-Containing 3 Protein/metabolism/antagonists & inhibitors ; Humans ; *Inflammasomes/metabolism ; Animals ; *Inflammation/drug therapy/metabolism ; Drug Delivery Systems/methods ; Nanotechnology/methods ; *Anti-Inflammatory Agents/administration & dosage/pharmacology ; Reactive Oxygen Species/metabolism ; Nanoparticles ; Nanomedicine/methods ; },
abstract = {The NLRP3 inflammasome plays a critical role in the onset and progression of various inflammatory diseases, making targeting its activation an important research direction for treating these conditions. Nanotechnology can effectively inhibit the activation of the NLRP3 inflammasome through several mechanisms, such as scavenging reactive oxygen species (ROS), regulating calcium ion flux, and stabilizing mitochondrial function, thereby alleviating inflammation and promoting tissue repair. Studies have demonstrated that nanomaterials exhibit promising anti-inflammatory effects in animal models, showing broad application potential, particularly in the treatment of conditions such as atherosclerosis, diabetes, and Alzheimer's disease. However, the clinical translation of nanotherapy still faces numerous challenges, including issues related to material biocompatibility, long-term safety, targeting efficiency, and controlled drug delivery. Future research should integrate targeting ligands, responsive materials, and multifunctional nanoplatforms to enhance the specificity and efficacy of treatments while minimizing side effects. Additionally, the prospects of nanotechnology in personalized treatment and clinical applications are substantial, necessitating further integration of basic research with clinical validation to expedite the clinical translation of NLRP3-targeted nanomedicines.},
}

*NLR Family, Pyrin Domain-Containing 3 Protein/metabolism/antagonists & inhibitors
Humans
*Inflammasomes/metabolism
Animals
*Inflammation/drug therapy/metabolism
Drug Delivery Systems/methods
Nanotechnology/methods
*Anti-Inflammatory Agents/administration & dosage/pharmacology
Reactive Oxygen Species/metabolism
Nanoparticles
Nanomedicine/methods

@article {pmid41332255,
year = {2025},
author = {Mahapatra, G and Gao, Z and Bateman, J and Lockhart, SN and Bergstrom, J and Piloso, JE and Craft, S and Molina, AJA},
title = {Platelets from Older Adults Exhibit Differences in Mitochondrial Function Associated with Impaired Glucose Metabolism.},
journal = {Clinical science (London, England : 1979)},
volume = {},
number = {},
pages = {},
doi = {10.1042/CS20242841},
pmid = {41332255},
issn = {1470-8736},
support = {R01AG054523,R01AG072734,and R01AG061805/NH/NIH HHS/United States ; P30AG049638 and P30AG072947//Wake Forest Alzheimer's Disease Research Center/ ; },
abstract = {Impaired glucose tolerance (IGT) and insulin resistance, including prediabetes and diabetes, increases risk of developing age-related disorders, such as cardiovascular disorders, kidney disorders, and Alzheimer's disease (AD). We analyzed mitochondrial bioenergetics of platelets collected from 208 adults, 55 years and older, with IGT and insulin resistance and without (normoglycemic, NG). Platelets from IGT participants exhibited unique mitochondrial bioenergetic profiles exemplified by higher mitochondrial respiration compared to NG. IGT platelets exhibited higher glucose-dependent maximal (Max) and spare respiratory (SRC) capacities, and higher fatty acid oxidation (FAO)-dependent maximal coupled (MaxOXPHOS) and uncoupled (MaxETS) respiration, compared to NG. Correlating mitochondrial bioenergetics from all 208 participants with measures of glucose tolerance (oral glucose tolerance test values measured 120 mins after glucose administration (OGTT_120), and oral glucose tolerance test area under the curve (OGTT_AUC)), and historical glucose measures (hemoglobin A1 (HbA1c)) revealed significant positive associations. Most associations were unaltered with age, sex, and BMI adjustments. Examining NG and IGT participants separately, we found platelet respiration and HbA1c exhibited positive association in NG participants. Significant positive associations emerged between platelet SRC, FAO, FAO+CI (oxygen flux due to fatty acid oxidation + complex I activities) and HbA1c. No significant associations were observed in the IGT group. Given the utilization of blood based mitochondrial bioenergetic profiling strategies in clinical research, this work provides new insights into the clinical features of insulin resistance that can impact platelet mitochondrial bioenergetics.},
}

@article {pmid41331940,
year = {2025},
author = {Hogan, AL and Kane, M and Chiu, P and Richter, G and Maurel, C and Wu, S and Scherer, NM and Don, EK and Lee, A and Blair, IP and Chung, RS and Morsch, M},
title = {Human TDP-43 overexpression in zebrafish motor neurons triggers MND-like phenotypes through gain-of-function mechanism.},
journal = {Acta neuropathologica communications},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40478-025-02159-w},
pmid = {41331940},
issn = {2051-5960},
support = {Ideas Grant 2029547//National Health and Medical Research Council/ ; Ideas Grant 2029547//National Health and Medical Research Council/ ; Ideas Grant 2029547//National Health and Medical Research Council/ ; 20200003//The ALS Foundation, Netherlands/ ; DIS-202403-01218//FightMND/ ; },
}

@article {pmid41331838,
year = {2025},
author = {Kumar, N and Jangid, K and Kumar, V and Devi, B and Arora, T and Mishra, J and Kumar, V and Dwivedi, AR and Parkash, J and Bhatti, JS and Kumar, V},
title = {Investigations of 3-Hydroxy Chromone Derivatives as Multipotent Therapeutics for the Treatment of Alzheimer's Disease: In Silico and In Vitro Interventions and Fluorescence Studies.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00847},
pmid = {41331838},
issn = {1948-7193},
abstract = {Chromone-based small organic molecules are designed and synthesized as putative multipotent ligands to intervene in several interlinked pathological pathways of Alzheimer's disease. The synthesized compounds were evaluated as acetylcholinesterase, monoamine oxidase, and amyloid β aggregation inhibitors using biochemical assays. Most of the compounds were found to inhibit the enzymes in a lower micromolar concentration range. In the series, two compounds, i.e., NSS-16 and NSS-18, displayed a balanced activity profile with the IC50 values of 1.77 and 1.53 μM against AChE and 2.06 and 1.51 μM against MAO-B. NSS-16 and NSS-18 showed moderate inhibitory activity against the self-induced Aβ aggregation with inhibition percentages of 17.8 and 24.0%, respectively. These compounds also showed potent antioxidant activity and formed metal chelates. In addition, the compounds were tested against SH-SY5Y neuronal cells and found to be neuroprotective and noncytotoxic. Moreover, the compounds inhibited reactive oxygen species (ROS) production up to 70% and exhibited a mixed type of inhibition in enzyme kinetic studies of AChE. These chromone derivatives showed a strong fluorescence intensity with a quantum yield of 30-50% and can be utilized in various biological studies including in vitro and in vivo assessments. Computational studies showed that the lead compounds fit well in the active cavity of enzymes and displayed thermodynamic stability for a time interval of 100 ns. Thus, these compounds displayed a multipotent activity profile and have the potential to be developed as potential therapeutics for AD.},
}

@article {pmid41331787,
year = {2025},
author = {Chen, H and Fan, L and Guo, Q and Wong, MY and Zhu, J and Foxe, N and Wang, W and Nessim, A and Carling, G and Liu, B and Lopez-Lee, C and Huang, Y and Amin, S and Patel, T and Mok, SA and Song, WM and Zhang, B and Gong, S and Ma, Q and Fu, H and Gan, L and Luo, W},
title = {DAP12 deletion reduces neuronal SLIT2 and demyelination and enhances brain resilience in female tauopathy mice.},
journal = {Molecular neurodegeneration},
volume = {20},
number = {1},
pages = {124},
pmid = {41331787},
issn = {1750-1326},
support = {GM152585//National Institute of Health, United States/ ; R01 AG075092/AG/NIA NIH HHS/United States ; NIH U54NS100717//National Institute of Health, United States/ ; R01AG064239//National Institute of Health, United States/ ; },
mesh = {Animals ; Mice ; *Tauopathies/metabolism/pathology/genetics ; Female ; *Brain/metabolism/pathology ; Mice, Transgenic ; Microglia/metabolism ; Slit Homolog 2 Protein/metabolism ; *Neurons/metabolism/pathology ; *Demyelinating Diseases/metabolism/pathology/genetics ; *Nerve Tissue Proteins/metabolism ; Humans ; tau Proteins/metabolism ; Male ; Disease Models, Animal ; },
abstract = {BACKGROUND: Pathogenic tau accumulation drives neurodegeneration in Alzheimer's disease (AD). Enhancing the aging brain's resilience to tau pathology would lead to novel therapeutic strategies. DAP12 (DNAX-activation protein 12), highly and selectively expressed by microglia, plays a crucial role in microglial immune responses. Previous studies have shown that tauopathy mice lacking DAP12 exhibit higher tau pathology but are protected from tau pathology-induced cognitive deficits. However, the exact mechanism behind this resilience remains elusive.

METHODS: We investigated the effects of DAP12 deletion on tau pathology, as well as tau-induced brain inflammation and neurodegeneration, in homozygous human Tau P301S transgenic mice. In addition, we conducted single-nucleus RNA sequencing of hippocampal tissues to examine cell type-specific transcriptomic changes at the single-cell level. Furthermore, we utilized the CellChat package to profile cell-cell communication in the mouse brain and investigated how these interactions are affected by tau pathology and Dap12 deletion.

RESULTS: We demonstrated that Dap12 deletion reduced tau processing in primary microglia and increased tau pathology in female tauopathy mice, with minimal effects on males. Despite this, Dap12 deletion markedly reduced brain inflammation, synapse loss, and demyelination, indicating enhanced resilience to tau toxicity. Single-cell transcriptomic profiling revealed that Dap12 deletion blocked tau-induced alterations in microglia, neurons, and oligodendrocytes. CellChat analysis identified aberrant tau-induced SLIT2 signaling from excitatory neurons to oligodendrocytes. Dap12 deletion suppressed Slit2 upregulation and mitigated demyelination, while lentiviral-Slit2 overexpression induced myelin loss in tauopathy mice. Elevated SLIT2 levels were associated with demyelination in tauopathy mouse model and human AD brains. Spatial transcriptomics revealed a spatial correlation of SLIT2 expression and tau pathology in AD brain tissue.

CONCLUSIONS: Our study identifies a novel DAP12-dependent mechanistic link between upregulated Slit2 expression in excitatory neurons and oligodendrocyte-dependent myelination loss in tauopathy. Despite elevating tau load, the absence of microglial Dap12 ameliorates neuroinflammation and improves brain functions in tauopathy mice. Our study suggests that selectively targeting the toxic aspects of DAP12 signaling while preserving its beneficial functions may be a promising strategy to enhance brain resilience in AD.},
}

Animals
Mice
*Tauopathies/metabolism/pathology/genetics
Female
*Brain/metabolism/pathology
Mice, Transgenic
Microglia/metabolism
Slit Homolog 2 Protein/metabolism
*Neurons/metabolism/pathology
*Demyelinating Diseases/metabolism/pathology/genetics
*Nerve Tissue Proteins/metabolism
Humans
tau Proteins/metabolism
Male
Disease Models, Animal

@article {pmid41331637,
year = {2025},
author = {Lv, J and Zhu, D and Mao, H and Li, C and Xu, W and Huang, J},
title = {Empowerment among primary caregivers of persons with Alzheimer's disease: associations between disease perception and caregiving partnership.},
journal = {BMC nursing},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12912-025-04154-x},
pmid = {41331637},
issn = {1472-6955},
support = {No. 2024JJ9461.//Supported by Hunan Science and Technology Innovation Programme Project/ ; },
}

@article {pmid41331379,
year = {2025},
author = {Kushawaha, SK and Vashisht, K and Kumar, H and Ashawat, MS and Baldi, A},
title = {Targeting protein kinase C signaling cascades in alzheimer's disease: emerging neuroprotective roles of aurothioglucose.},
journal = {Inflammopharmacology},
volume = {},
number = {},
pages = {},
pmid = {41331379},
issn = {1568-5608},
abstract = {Protein Kinase C (PKC), a zinc-dependent signaling enzyme pivotal for neuronal survival and synaptic plasticity, has emerged as a central player in the pathogenesis of Alzheimer's disease (AD). Dysregulated PKC activity contributes to amyloid-β accumulation, tau-driven neurofibrillary tangles, and chronic neuroinflammation, mediated through key molecular cascades such as NF-κB, GSK-3β, and MAPK. Notably, conditions such as osteoporosis and rheumatoid arthritis further illustrate how chronic cytokine release can link systemic inflammation to PKC dysregulation and subsequent neurodegeneration. Although mechanistic insights into these pathways have expanded, AD remains a therapeutic enigma with no disease-modifying interventions available. Interestingly, traditional Indian medical texts like the Charaka-Samhita documented herbal and metallic remedies, including gold-based formulations such as Swarna Prashana, reputed for enhancing cognition. Translating this ancient wisdom into modern medicine, aurothioglucose, an FDA-approved agent for rheumatoid arthritis, has demonstrated potent anti-inflammatory properties through PKC modulation. Emerging preclinical evidence now positions aurothioglucose as a promising neuroprotective candidate, capable of mitigating oxidative stress, dampening neuroinflammation, and preserving synaptic integrity via PKC-linked pathways. This review underscores the evolving role of aurothioglucose in AD, highlighting its potential to bridge traditional knowledge with contemporary therapeutics, while emphasizing the pressing need for translational studies to confirm its disease-modifying efficacy, as supported by evidences from current state of art.},
}

@article {pmid41331377,
year = {2025},
author = {da Conceição, RA and Barbosa, MLC and de Souza, AMT},
title = {Multitarget-directed ligands in Alzheimer's disease: identification of AChE and BACE1 inhibitors by in silico approaches.},
journal = {Journal of computer-aided molecular design},
volume = {40},
number = {1},
pages = {7},
pmid = {41331377},
issn = {1573-4951},
support = {001//Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/ ; 312878/2022-2//Conselho Nacional de Desenvolvimento Científico e Tecnológico/ ; },
mesh = {*Alzheimer Disease/drug therapy/metabolism ; *Amyloid Precursor Protein Secretases/antagonists & inhibitors/metabolism/chemistry ; Humans ; *Aspartic Acid Endopeptidases/antagonists & inhibitors/metabolism/chemistry ; Ligands ; *Cholinesterase Inhibitors/chemistry/pharmacology/therapeutic use ; *Acetylcholinesterase/metabolism/chemistry ; Molecular Docking Simulation ; Computer Simulation ; Drug Discovery ; },
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions of people worldwide, with its prevalence expected to rise in the coming years. Due to the complexity of AD and the intricate interplay among its pathological mechanisms, the development of multitarget-directed ligands (MTDLs) has emerged as a promising therapeutic strategy. These compounds could simultaneously modulate multiple pathogenic pathways. Specifically, cholinergic and amyloid mechanisms, implicated in the onset of the disease, are regulated by AChE and BACE1, respectively. Therefore, targeting both pathways offers substantial therapeutic potential for AD. Computational tools can be useful in the identification of potential MTDL for these enzymes, reducing both costs and time in the drug discovery process. This review explores the relevance of this approach in the research and development for novel AD therapies, highlighting ongoing efforts focused on the identification and development of MTDLs for AChE and BACE1 inhibition through in silico methods. Virtual screening was the most frequently applied technique for a fast selection of ligands based on their affinity for the enzymes of interest. The in silico ADMET prediction also appears with a technique that allows the screening of compounds with drug-likeness. Moreover, evidence suggests that combining multiple computational methods can effectively identify drug candidates with optimized properties for target modulation and brain bioavailability.},
}

*Alzheimer Disease/drug therapy/metabolism
*Amyloid Precursor Protein Secretases/antagonists & inhibitors/metabolism/chemistry
Humans
*Aspartic Acid Endopeptidases/antagonists & inhibitors/metabolism/chemistry
Ligands
*Cholinesterase Inhibitors/chemistry/pharmacology/therapeutic use
*Acetylcholinesterase/metabolism/chemistry
Molecular Docking Simulation
Computer Simulation
Drug Discovery

@article {pmid41331360,
year = {2025},
author = {Sun, S and Dochtermann, D and Wang, Z and Pyarajan, S and , and Galfalvy, H and Haghighi, F},
title = {Genetic architecture of suicidal ideation continuum: latent profile analysis of data using the million veteran program cohort.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41331360},
issn = {1476-5578},
support = {CX002074//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; CX001728//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; BX006069//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; BX003794//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; RX00318//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; MVP000//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; },
abstract = {Suicidal ideation (SI) and behavior are complex phenotypes, with multiple contributing risk-factors. This study used longitudinal data from the Million Veteran Program Mental Health Survey to identify SI profiles among Veterans based on trajectories of ideation and depression severity and compared them to a non-suicidal (no-SI) control group. Latent profile analysis (LPA) was performed to identify SI profiles using data from Veterans (n = 34,322) endorsing SI in their electronic health record. LPA identified four highly reproducible SI profiles: mild ideators with and without depression, variable ideators, and persistent ideators. Veterans across the SI profiles were significantly more likely to have diagnoses of suicidal ideation or behavior, mental disorders, and TBI compared to Veterans with no-SI. The variable ideators showed higher rates of comorbid conditions. The mild ideators without depression and persistent ideators had a significantly higher proportion of deaths by suicide than the no-SI Veterans. European and African American GWAS and pan-ancestry meta-analyses of SI profiles compared to no-SI controls were also performed, which identified genome-wide significant loci across all SI profiles proximal to genes implicated in auditory and vestibular functioning, Alzheimer's, diabetes, and asthma. In summary, SI profiles identified were associated with novel genetic variants not identified by previous suicide GWAS studies. Additionally, Veterans within the mild SI profile that did not present with high-risk comorbidities had the highest rate of suicide deaths, indicating the need for upstream suicide risk prevention interventions across the SI risk continuum.},
}

@article {pmid41330788,
year = {2025},
author = {Zimmer, JA and Sims, JR and Evans, CD and Nery, ESM and Wang, H and Wessels, AM and Tronchin, G and Sato, S and Raket, LL and Andersen, SW and Sapin, C and Paget, MA and Gueorguieva, I and Ardayfio, P and Khanna, R and Brooks, DA and Matthews, BR and Mintun, MA and , },
title = {Donanemab in early symptomatic Alzheimer's disease: results from the TRAILBLAZER-ALZ 2 long-term extension.},
journal = {The journal of prevention of Alzheimer's disease},
volume = {},
number = {},
pages = {100446},
doi = {10.1016/j.tjpad.2025.100446},
pmid = {41330788},
issn = {2426-0266},
abstract = {BACKGROUND: Donanemab significantly slowed clinical progression in participants with early symptomatic Alzheimer's disease (AD) during the 76-week placebo-controlled period of TRAILBLAZER-ALZ 2.

METHODS: Participants who completed the placebo-controlled period were eligible for the 78-week, double-blind, long-term extension (LTE). Early-start participants were randomized to donanemab in the placebo-controlled period. Delayed-start participants (randomized to placebo) started donanemab in the LTE. Participants who met amyloid treatment course completion criteria were switched to placebo. An external control cohort comprised participants from the AD Neuroimaging Initiative (ADNI).

RESULTS: At 3 years, donanemab slowed disease progression on the Clinical Dementia Rating Scale (CDR)-Sum of Boxes (CDR-SB) in early-start participants versus a weighted ADNI control (-1.2 points; 95 % confidence interval [CI], -1.7, -0.7). Seventy-six weeks after initiating donanemab, delayed-start participants also demonstrated slower CDR-SB progression versus a weighted ADNI control (-0.8 points; 95 % CI, -1.3, -0.3). Participants who completed treatment by 52 weeks demonstrated similar slowing of CDR-SB progression at 3 years. Compared with delayed-start participants, early-start participants demonstrated a significantly lower risk of disease progression on the CDR-Global over 3 years (hazard ratio=0.73; p < 0.001). In both groups, >75 % of participants assessed by positron emission tomography 76 weeks after starting donanemab achieved amyloid clearance (<24.1 Centiloids). The addition of LTE data to prior modeling predicted a median reaccumulation rate of 2.4 Centiloids/year. No new safety signals were observed compared to the established donanemab safety profile.

CONCLUSIONS: Over 3 years, donanemab-treated participants with early symptomatic AD demonstrated increasing clinical benefits and a consistent safety profile, with limited-duration dosing.

TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04437511.},
}

@article {pmid41330691,
year = {2025},
author = {Prasanthi, RPJ and Schommer, E and Thomasson, S and Thompson, A and Feist, G and Ghribi, O},
title = {Expression of concern: Regulation of β-amyloid levels in the brain of cholesterol-fed rabbit, a model system for sporadic Alzheimer's disease.},
journal = {Mechanisms of ageing and development},
volume = {228},
number = {},
pages = {112121},
doi = {10.1016/j.mad.2025.112121},
pmid = {41330691},
issn = {1872-6216},
}

@article {pmid41330684,
year = {2026},
author = {Shim, S and Cho, H and Lee, JH and Cho, WW and Kim, J and San Lee, J and Lee, SM and Shin, DS},
title = {Signal-enhanced detection of p-tau181 in human plasma as a biomarker for Alzheimer's disease using a multi-channel impedance analyzer with nanoliposome amplification.},
journal = {Analytica chimica acta},
volume = {1382},
number = {},
pages = {344851},
doi = {10.1016/j.aca.2025.344851},
pmid = {41330684},
issn = {1873-4324},
mesh = {Humans ; *Alzheimer Disease/blood/diagnosis ; *tau Proteins/blood ; Biomarkers/blood ; Electric Impedance ; *Electrochemical Techniques/instrumentation/methods ; *Liposomes/chemistry ; },
abstract = {Alzheimer's disease (AD), caused by amyloid-β (Aβ) aggregation-derived senile plaques, is among the most prevalent neurodegenerative disorders. Recently, Roche and Lilly announced the development of an AD diagnosis that utilizes phosphorylated tau (p-tau) and apolipoprotein E epsilon 4 allele as biomarkers, rather than Aβ. Additionally, the National Institute on Aging and Alzheimer's Association Research Framework suggests that p-tau molecules can serve as critical biomarkers for AD diagnosis. Here, we quantified p-tau181 in human plasma using a novel electrochemical assay method called the Electrical-Immunosorbent Assay (El-ISA), which employs a multichannel impedance analyzer (ToAD) with a 96-interdigitated microelectrode sensor array. The El-ISA partially follows the typical immunological sandwich assay process, quantitatively measuring the residual detection probes after the immunoreaction. The clinical performance of the El-ISA was evaluated using approximately 60 samples of human blood plasma, and its diagnostic accuracy was assessed based on clinical diagnostic data. Furthermore, impedance signals were amplified for protein quantification at the pg/mL level in human plasma by employing biotinylated nanoliposomes. We anticipate that the ToAD/El-ISA will evolve into a clinical in vitro diagnostic device for p-tau-based AD diagnosis in human blood.},
}

Humans
*Alzheimer Disease/blood/diagnosis
*tau Proteins/blood
Biomarkers/blood
Electric Impedance
*Electrochemical Techniques/instrumentation/methods
*Liposomes/chemistry

@article {pmid41330635,
year = {2025},
author = {Crowe, A and Yao, Y and Newman, M and Hoxha, K and Baffic, T and Brunden, KR},
title = {HDAC6 Inhibition Reduces Seeded Tau and α-Synuclein Pathologies in Primary Neuron Cultures and Wild-type Mice.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1092-25.2025},
pmid = {41330635},
issn = {1529-2401},
abstract = {A previous compound screen identified two molecules with histone deacetylase 6 (HDAC6) inhibitory activity that reduced Alzheimer's disease (AD)-like tau inclusions in a primary rat cortical neuron model seeded with AD-brain derived tau fibrils. Testing here of additional HDAC6-selective inhibitors confirmed that compounds of this type decreased neuronal tau inclusions. Moreover, HDAC6 inhibitors also reduced Parkinson's disease (PD)-like α-synuclein aggregates in primary neurons seeded with recombinant α-synuclein fibrils. Knockdown of HDAC6 expression through treatment of seeded neuron cultures with AAV harboring HDAC6-specific shRNA also resulted in a reduction of tau and α-synuclein inclusions. Multiple compounds were evaluated for their ability to inhibit brain HDAC6 in mice, and ACY-738 was found to effectively inhibit brain HDAC6 activity upon oral dosing. ACY-738 was utilized in an efficacy study in which tau and α-synuclein pathologies were induced in wild-type mice through intracerebral injections of AD-brain derived tau and α-synuclein fibrils. Groups of male and female mice first received ACY-738 in drinking water one day prior to (pre-seeding) or one week after (post-seeding) brain injections of fibrils, followed by continued dosing for an additional 3 months. A control group of fibril-injected mice received water without ACY-738. Immunohistochemical evaluations revealed that ACY-738 administration resulted in significant reductions of tau pathology in both dosing schemes. Moreover, α-synuclein pathology was significantly reduced in mice with pre-seeding ACY-738 administration, with a strong trend toward reduction after post-seeding dosing. These results suggest that HDAC6 inhibitors have potential for the treatment of AD, PD and related diseases.Significance Statement The spread and abundance of brain tau pathology correlate with AD patient cognitive status, and there are presently no approved drugs that target tau. We demonstrate that HDAC6 inhibition or knockdown reduce both tau and α-synuclein inclusions that develop in wild-type rodent neuron models. A preferred HDAC6 inhibitor, ACY-738, was identified that inhibits brain HDAC6 when administered orally to mice. This compound was examined in a wild-type mouse model that develops concurrent seeded tau and α-synuclein brain inclusions. Significant reductions of both tau and α-synuclein inclusions were observed in mice dosed with ACY-738, suggesting that HDAC6 inhibition may be a therapeutic strategy for AD, PD and related diseases.},
}

@article {pmid41330513,
year = {2025},
author = {Gao, X and Jiang, P and Liu, L and Huang, Y and Tan, H and Guo, W and Xie, H and Xing, P and Wang, Q and Peng, S and Hua, J and Wang, Y and Lan, Y and Jiang, L and Lin, H and Guo, H},
title = {Unraveling the genetic interplay between sleep disorders and Alzheimer's disease: From shared genes to potential therapeutic targets.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120788},
doi = {10.1016/j.jad.2025.120788},
pmid = {41330513},
issn = {1573-2517},
abstract = {BACKGROUND: Sleep disorders are potential risk factors for Alzheimer's disease (AD), but their genetic association and shared gene mechanisms remain unclear. This study investigate the genetic correlation between AD and four sleep disorders phenotypes, and examined AD-sleep shared pathways in major depressive disorder (MDD) to assess a broader neuropsychiatric implication.

METHODS: Linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) were employed to investigate shared genetic architecture. SNP-Level PLACO analysis identified pleiotropic loci. MAGMA mapped these loci to the genes which implicated in AD pathology, while expression quantitative trait loci (QTL) identified brain-expressed genes involved in key pathways.

RESULTS: Data from 1,862,604 participants, including 71,880 CE or AD-by-proxy cases and 383,378 controls, along with 92,765 individuals with sleep disorders and 1,314,581 controls, revealed positive genetic correlations between combined sleep disorders (CSD) and AD (LDSC: rg = 0.075, P = 0.030; HDL: rg = 0.133, P = 0.024) and between sleep apnea syndrome (SAS) and AD (LDSC: rg = 0.081, P = 0.018; HDL: rg = 0.132, P = 0.027). Nine specific genes including MARK4, GPC2, PVRL2, ACMSD, AC006126.3, BIN1, APOC1, APOC4, and APOC2 were implicated in AD pathology, with common pathways involving complement activation, immune response activation, and protein-lipid complex formation. Crucially, these AD-sleep shared pathways were also significantly enriched for MDD risk.

CONCLUSION: These findings highlight a genetic association between sleep disorders and AD, which extends to MDD through common biological pathways, revealing shared genetic risk factors and mechanisms, which may inform novel therapeutic strategies.},
}

@article {pmid41330455,
year = {2025},
author = {Vargas-Soria, M and Corraliza-Gomez, M and Infante-Garcia, C and Stitt, AW and Simó, R and Garcia-Alloza, M},
title = {Diabetes-linked metabolic dysfunction relates with distinct tau phosphorylation patterns, neuroinflammation and cognitive impairment in mouse models of Alzheimer's disease.},
journal = {Brain, behavior, and immunity},
volume = {},
number = {},
pages = {106204},
doi = {10.1016/j.bbi.2025.106204},
pmid = {41330455},
issn = {1090-2139},
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques and tau neurofibrillary tangles, with tau pathology being closely linked to cognitive decline. Growing evidence suggests that metabolic dysfunction including type 1 diabetes (T1D) and type 2 diabetes (T2D), as well as prediabetes (PreDM), exacerbate AD by promoting different degrees of insulinopenia, insulin resistance and hyperglycemia which can drive chronic inflammation and oxidative stress across multiple organs. Precisely how these metabolic disturbances influence tau phosphorylation remains unclear. To address this, we studied mouse models of AD, T1D, PreDM, T2D and the combination of AD with all three metabolic alterations, at 26 weeks of age, when pathologies are well established. The fact that we are including models of insulin resistance and insulin deficiency allows us to further explore the specific role of insulin as observed in the clinic. We assessed metabolic status, tau phosphorylation and cytokine levels in the brain cortex and cognitive function using the Morris water maze (MWM) and novel object discrimination (NOD) tests. Our results revealed that AD mice with metabolic disorders exhibited tau hyperphosphorylation, particularly at Ser199, Ser202/Thr205 and Ser404, correlating with metabolic dysfunction, cognitive impairment and inflammatory markers. Notably, AD-T2D mice showed the most severe deficits in MWM and NOD performance, indicating a synergistic cognitive decline. Machine learning analysis by random forest effectively classified AD-metabolic phenotypes, identifying key molecular and metabolic markers of neurodegeneration, mainly blood glucose and plasma insulin. These findings highlight the critical role of metabolic dysfunction in exacerbating tau pathology and accelerating cognitive decline in AD. Targeting metabolic pathways may provide concomitant therapeutic opportunities for AD patients with diabetes. Future research should explore interventions that restore insulin signaling and glucose metabolism to mitigate AD progression, probably by repurposing antidiabetic drugs.},
}

@article {pmid41330181,
year = {2025},
author = {Tong, P and Li, X and Li, S and Li, S and Chen, B and Chen, D and Xia, C},
title = {Integrated metabolomics, transcriptomics and network pharmacology analysis to reveal the mechanisms of Danggui Buxue tang in treating Alzheimer's disease.},
journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology},
volume = {150},
number = {},
pages = {157591},
doi = {10.1016/j.phymed.2025.157591},
pmid = {41330181},
issn = {1618-095X},
abstract = {BACKGROUND: Alzheimer's disease (AD) is a prevalent, multifactorial, and multisystem chronic neurodegenerative disorder. Existing pharmacotherapeutic interventions for AD present significant limitations. Prior studies have indicated that Danggui Buxue Tang (DBT) reduces Aβ deposition and improves cognitive function. Nevertheless, the therapeutic mechanisms of DBT are not yet fully elucidated.

PURPOSE: The objective of this study is to clarify the underlying mechanisms by which DBT alleviates AD. This will be accomplished through an integrated approach that encompasses network pharmacology, transcriptomics, metabolomics analyses, and experimental validation.

METHODS: The chemical composition of DBT was elucidated using HPLC. The neuroprotective efficacy of DBT was assessed through behavioral evaluations and pathological tissue examination. Network pharmacology was leveraged to identify potential therapeutic targets for DBT in AD. Transcriptomic analysis was conducted on SH-SY5Y cells using Illumina sequencing to pinpoint differentially expressed genes. Additionally, an untargeted metabolomic assessment of cerebral tissue was performed using UPLC-MS/MS to ascertain differentially accumulated metabolites. The impact and underlying mechanisms of DBT on AD were explored by integrating network pharmacology, transcriptomics, and metabolomics, and subsequently confirmed through qPCR and WB.

RESULTS: Behavioral assessments and histopathological analyses have shown that DBT significantly alleviates AD-related symptoms. In APP/PS1 mice, DBT markedly improved cognitive impairment and anxiety-like behavior, and reduced neuronal damage. Further integrative analyses suggest that these effects are mediated, at least in part, by the suppression of neuroinflammation-driven neuronal apoptosis through the modulation of the TNF/NF-κB and MAPK signaling cascades. Additionally, DBT seems to reestablish cerebral metabolic equilibrium by regulating the metabolism of glycerophospholipids, sphingolipids, and terpenoids in the brain tissue affected by Alzheimer's disease.

CONCLUSIONS: These results highlight the therapeutic potential of DBT for alleviating AD pathology. Our study provides new mechanistic understandings of DBT, suggesting that its therapeutic effects result from the synergistic regulation of multiple pathways to simultaneously inhibit neuroinflammation and neuronal apoptosis, thereby effectively opposing the pathological features of AD.},
}

@article {pmid41330156,
year = {2025},
author = {Costa, A and Provensi, G and Titi, C and Leri, M and Bucciantini, M and Ferraroni, M and Keeton, AB and Moore, AM and Piazza, GA and Abadi, AH and Angeli, A and Supuran, CT},
title = {Exploring dual inhibitors Carbonic Anhydrases and Phosphodiesterase 5 as potential agents for treatment Alzheimer's disease.},
journal = {European journal of medicinal chemistry},
volume = {303},
number = {},
pages = {118404},
doi = {10.1016/j.ejmech.2025.118404},
pmid = {41330156},
issn = {1768-3254},
abstract = {In this study, we report for the first time the design and evaluation of a series of compounds with potential therapeutic relevance for Alzheimer's disease (AD), able to inhibit both human Carbonic Anhydrase (hCA) isoforms most involved in this disease as well as Phosphodiesterase 5 (PDE5), using sildenafil as the structural scaffold. A total of 19 new dual-target molecules were synthesized and biologically assessed, leading to the identification of compound 8a as the most promising candidate, exhibiting potent inhibition toward both enzymatic targets. The binding interactions of three selected derivatives (6, 8a, and 10d) with hCA II were elucidated by X-ray crystallography experiments. Moreover, compound 8a demonstrated a favourable safety profile, as it did not markedly impair cell viability on differentiated SH-SY5Y at concentrations up to 100 μM and conferred protection against Aβ-induced cytotoxicity showing superior efficacy compared to the single-target reference agents acetazolamide (AAZ) and sildenafil in mitigating oxidative stress. In vivo, chronic administration of compound 8a prevented deficits in both recognition and working memory in Aβ1-42-infused mice, outperforming vehicle-treated controls. Collectively, these findings highlight the potential of dual CA/PDE5 inhibition as a novel therapeutic strategy for Alzheimer's disease.},
}

@article {pmid41329964,
year = {2025},
author = {Chen, M and Tang, G and Zhang, Y and Qian, ZJ},
title = {Exploring the Protective Effects of Two Alkaloids 1 and 2 from Aspergillus terreus C23-3 on Neuronal Cells by Combining Bioinformatics Prediction and Experimental Verification.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00739},
pmid = {41329964},
issn = {1948-7193},
abstract = {Alzheimer's disease (AD) is an irreversible neurodegenerative disease that can lead to brain cell death and brain atrophy, manifested as memory loss, cognitive decline, and behavioral abnormalities. Its mechanism is complex, and there is currently no effective treatment method. The search for new therapies and natural drug candidates has become the focus of research. In recent years, marine-derived strains of Aspergillus terreus have become an important research direction for treating AD due to the unique structure and biological activity of their secondary metabolites. In this study, we investigated the potential of two alkaloids from Aspergillus terreus C23-3 in the treatment of AD through bioinformatics analysis and experimental validation. Bioinformatics analyses showed that the two alkaloids may act by modulating key targets associated with AD, especially alkaloid 2, which may exert significant therapeutic effects on AD by inhibiting glycogen synthase kinase-3β (GSK-3β) activity and reducing the level of hyperphosphorylation of Tau proteins. Molecular docking experiments showed that alkaloids 1 and 2 formed stable complexes with GSK-3β with a high affinity. Cellular experiments showed that alkaloids 1 and 2 could effectively inhibit apoptosis and injury in HT-22 cells. Further studies showed that alkaloid 2 reduced the phosphorylation level of Tau protein and attenuated oxidative-stress-induced neurological injury by inhibiting GSK-3β and its related pathways. These results suggest that alkaloid 2 has significant potential for AD therapy.},
}

@article {pmid41329905,
year = {2025},
author = {Krell-Roesch, J and Syrjanen, JA and Hansen, AL and Vemuri, P and Scharf, EL and Fields, JA and Kremers, WK and Lowe, VJ and Graff-Radford, J and Jack, CR and Petersen, RC and Racette, SB and Woll, A and Vassilaki, M and Geda, YE},
title = {Physical and Cognitive Activities and Trajectories of AD Neuroimaging Biomarkers: Longitudinal Analysis in the Mayo Clinic Study of Aging.},
journal = {Neurology},
volume = {105},
number = {12},
pages = {e214405},
doi = {10.1212/WNL.0000000000214405},
pmid = {41329905},
issn = {1526-632X},
mesh = {Humans ; Male ; Female ; Aged ; Longitudinal Studies ; *Alzheimer Disease/diagnostic imaging/metabolism/psychology ; Biomarkers/metabolism ; Positron-Emission Tomography ; Middle Aged ; *Aging/psychology ; *Cognitive Dysfunction/diagnostic imaging/metabolism ; Aged, 80 and over ; *Cognition/physiology ; Aniline Compounds ; tau Proteins/metabolism ; *Brain/diagnostic imaging/metabolism ; Neuroimaging ; Fluorodeoxyglucose F18 ; Thiazoles ; },
abstract = {BACKGROUND AND OBJECTIVES: Engagement in physical and cognitive activities is associated with a decreased risk of mild cognitive impairment (MCI) and dementia, but the association with Alzheimer disease (AD) neuroimaging biomarkers is less clear. We thus examined associations of physical and cognitive activities with longitudinal trajectories of AD neuroimaging biomarkers among older adults free of dementia.

METHODS: We conducted a longitudinal study within the population-based Mayo Clinic Study of Aging (mean follow-up durations 1.3-3.4 years). Participants were aged 50 years or older and were cognitively unimpaired or had MCI at baseline. Engagement in physical and cognitive activities during 12 months before baseline was assessed through questionnaires. Participants underwent AD neuroimaging biomarker assessments at 1 or more time points. We ran linear mixed-effect models to examine associations between physical and cognitive activity composite scores and trajectories of individual yearly change in amyloid deposition (Pittsburgh compound B [PiB]-PET centiloid), tau burden (tau-PET standardized uptake value ratio [SUVR]), and regional glucose hypometabolism (fluorodeoxyglucose [FDG]-PET SUVR), adjusted for age, sex, APOE ɛ4 carrier status, and medical comorbidity.

RESULTS: We included 1,176 participants (47% female; mean [SD] age, 68.7 [9.6] years) for PiB-PET trajectories, 399 participants (49% female; mean [SD] age, 71.9 [11.0] years) for tau-PET trajectories, and 983 participants (46% female; mean [SD] age, 67.9 [9.2] years) for FDG-PET trajectories. PiB-PET and tau-PET measures increased during follow-up (3.4 [SD 4.0] and 1.3 [SD 2.1] years, respectively), whereas FDG-PET values decreased over 2.9 (SD 3.5) years of follow-up. Participants with higher total physical activity (interaction estimate 0.0017; 95% CI 0.0003-0.0031; p = 0.021) and higher moderate-to-vigorous physical activity (interaction estimate 0.0015; 95% CI 0.0001-0.0029; p = 0.040) had a less pronounced decrease in FDG-PET over time. Participants with higher cognitive activity experienced a less pronounced increase in PiB-PET (interaction estimate -0.2253; 95% CI -0.4437 to -0.0070; p = 0.043) and a smaller decrease in FDG-PET (interaction estimate 0.0015; 95% CI 0.0001-0.0028; p = 0.038) over time.

DISCUSSION: Physical activity was associated with less synaptic dysfunction and cognitive activity with less synaptic dysfunction and lower amyloid burden over time, albeit effect sizes were small. Further research is needed to validate findings and clarify causal inference between physical and cognitive activities and AD neuroimaging biomarkers.},
}

Humans
Male
Female
Aged
Longitudinal Studies
*Alzheimer Disease/diagnostic imaging/metabolism/psychology
Biomarkers/metabolism
Positron-Emission Tomography
Middle Aged
*Aging/psychology
*Cognitive Dysfunction/diagnostic imaging/metabolism
Aged, 80 and over
*Cognition/physiology
Aniline Compounds
tau Proteins/metabolism
*Brain/diagnostic imaging/metabolism
Neuroimaging
Fluorodeoxyglucose F18
Thiazoles

@article {pmid41329842,
year = {2025},
author = {Lin, J},
title = {Visual Evoked Potentials as a Biomarker for Visual Hallucination Pathway Integrity in Late-Stage Alzheimer's Disease.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251403219},
doi = {10.1177/15500594251403219},
pmid = {41329842},
issn = {2169-5202},
abstract = {ObjectiveAlzheimer's disease (AD) often presents visual hallucinations (VH) in late stages. Visual evoked potentials (VEPs) are noninvasive electrophysiological measures that reflect the functional integrity of the visual conduction pathway. This study uses visual evoked potentials (VEP) to assess visual pathway dysfunction and evaluates VEP as a biomarker for disease progression.MethodsA retrospective study of 112 AD patients (2016-2024) was conducted, categorizing individuals into VH and non-VH groups based on the presence of visual hallucinations. VEP testing assessed P100 latency and amplitude. Baseline characteristics and VEP parameters were compared between groups, and correlations with disease duration were analyzed.ResultsNo significant differences were observed between the two groups in terms of age, sex, years of education, homocysteine (HCY) levels, or Mini-Mental State Examination (MMSE) scores (p > 0.05). However, disease duration was significantly longer in the VH group than in the non-VH group (p = 0.00). VEP findings revealed a significantly prolonged P100 latency (p = 0.01) and reduced P100 amplitude (p = 0.00) in the VH group. Correlation analysis indicated a positive correlation between P100 latency and disease duration (r = 0.21, p = 0.03) and a negative correlation between P100 amplitude and disease duration (r = -0.34, p = 0.00), suggesting progressive impairment of the visual conduction pathway over the course of the disease.ConclusionAD patients with visual hallucinations exhibit more severe impairments in the integrity of the visual conduction pathway than those without hallucinations, as evidenced by prolonged P100 latency and decreased amplitude. These changes are closely associated with disease duration.},
}

@article {pmid41329626,
year = {2025},
author = {Farfel, JM and Capuano, AW and Nag, S and Sampaio, MCM and Gibbons, J and Wilson, RS and Bennett, DA},
title = {The Pathology, Alzheimer's and Related Dementias Study (PARDoS): Design and Characteristics of the First 4700+ Brazilian Participants.},
journal = {Neuroepidemiology},
volume = {},
number = {},
pages = {1-33},
doi = {10.1159/000547564},
pmid = {41329626},
issn = {1423-0208},
abstract = {The Pathology, Alzheimer's and Related Dementias Study (PARDoS) is a community-based clinical-pathologic study of aging and dementia in a large and diverse sample of Brazilians. Its long-term objective is to identify the environmental, genetic and molecular drivers of common conditions across the adult life span with an emphasis on Alzheimer's Disease and Related Disorders clinical and neuropathologic traits. From July 31st 2021 through February 11th, 2025, 4,790 brains were collected at two autopsy centers and a major hospital system in the State of Sao Paulo, Brazil. Samples of other organs are also being collected. Their mean age was 71.7 years (range 18-106), 40.2% were Black/Mixed, 52.7% were male, their mean education was 6.3 years (range 0-25). Among those aged 65+, 32.9% had dementia and 18.8% had mild cognitive impairment. Neuropathologic data collection is ongoing. PARDoS fills several major gaps among clinical-pathologic studies given the large numbers and its unique age and education range, and socioeconomic status, race, sex, and other organ collection. Here we present the study design, demographic characteristics of the first 4,790 autopsied participants, and clinical characteristics of the first 4,283 with informant interviews.},
}

@article {pmid41329558,
year = {2025},
author = {Bhardwaj, A and Jaiswal, S and Bhardwaj, K and Paliwal, T and Bharadwaj, S and Negi, S and Kumar, G and Jain, S and Kishore, D and Sharma, S and Dwivedi, J},
title = {Novel Assembling of Furano-Fused Azepinone Derivatives for Inhibition of Acetylcholinesterase Responsible for Alzheimer's Disease: Synthesis, Molecular Docking, DFT, In Vitro, and In Silico Studies.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00744},
pmid = {41329558},
issn = {1948-7193},
abstract = {A new assembly of furano-fused azepinone derivatives was carried out in two steps, i.e., 3 + 2 cycloaddition followed by hydroxylammonium-O-sulfonic acid (HOSA)-assisted Beckmann rearrangement in aqueous conditions. This methodology uses a readily available starting synthon, dimedone, to synthesize five- and six-membered condensed furano-azepinone derivatives 5(a-n), and their structures were validated by spectral techniques. In vitro antiacetylcholinesterase (AchE) activity revealed that compound 5n (IC50= 2.38 ± 0.02 nM) showed higher inhibitory activity than reference drugs galantamine (IC50 = 2.84 ± 0.01 nM). Later, cytotoxicity studies of the synthesized compounds were conducted on SHSY5Y cell lines, indicating the concentration-dependent inhibition, i.e., the highest cell viability at 25 μM, whereas the lowest viability at 400 μM. Further intracellular ROS measurements indicate that 5n exhibits superior ROS-scavenging capabilities in fluorescence-based assays. Molecular docking and density functional theory (DFT) analyses were applied to further validate the binding interactions of the compounds with the AchE active site. The combined experimental and computational investigation revealed that 5n exhibits significant anti-AchE activity and warrants further exploration for its medicinal utility in Alzheimer's disease and related challenges. The design, synthesis, and AchE inhibitory properties of the synthesized furano-azepinone derivatives were patented under Indian patent number 202511048244.},
}

@article {pmid41329493,
year = {2025},
author = {Idrees, T and Toschi, E},
title = {Continuous Glucose Monitoring in Older Adults With Diabetes and Alzheimer Disease and Related Dementias-Promise and Perspective.},
journal = {JAMA network open},
volume = {8},
number = {12},
pages = {e2541947},
doi = {10.1001/jamanetworkopen.2025.41947},
pmid = {41329493},
issn = {2574-3805},
}