Latest AI and machine learning research in geriatrics for healthcare professionals.
Whole slide images (WSIs) are the gold standard for pathological diagnosis and sub-typing. Current main-stream two-step frameworks employ offline feature encoders trained without domain-specific knowledge. Among them, attention-based multiple instance learning (MIL) methods are outcome-oriented and offer limited interpretability. Clustering-based approaches can provide explainable decision-making ...
The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and volatile organic compounds (VOCs) in industrial environments poses complex toxicological risks that standard additive models fail to capture. This study elucidates a novel "metabolic blockade" mechanism wherein PFAS competitively inhibits the renal excretion of VOC metabolites, thereby amplifying neurotoxic burdens. Utilizing a Do...
Lung cancer is the leading cause of cancer-related mortality worldwide, predominantly affects older individuals, with non-small cell lung cancer (NSCL...
Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, curren...
Aging of hematopoietic stem and progenitor cells (HSPCs) impairs regenerative capacity and predisposes to hematological diseases. Here, we constructed...
Calcium dynamics controls learning and memory. Changes in calcium-induced calcium release (CICR), which is caused by opening ryanodine receptors (RyR)...
Whole slide images (WSIs) enable weakly supervised prognostic modeling via multiple instance learning (MIL). Spatial transcriptomics (ST) preserves in...
Body mass index (BMI), type 2 diabetes (T2D) and associated cardiometabolic features modify Alzheimer's disease (AD) risk, yet shared mechanisms remai...
Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality rema...
INTRODUCTION: Cognitively unimpaired (CU) adults show substantial variation in their risk of developing mild cognitive impairment (MCI), yet most subt...
Background: Natural language processing (NLP) systems integrated into clinical workflows show promise for detecting early cognitive impairment, yet ca...
Metaphorical comprehension in images remains a critical challenge for Nowadays AI systems. While Multimodal Large Language Models (MLLMs) excel at bas...
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically inter...
Early Alzheimer's disease often evades timely detection because typical diagnostics are based on symptomatic thinking rather than intrinsic neurodegen...
End-to-end autonomous driving systems have achieved significant progress, yet their adversarial robustness remains largely underexplored. In this work...
Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imag...
Introduction: Plasma phosphorylated tau-217 is widely used as a plasma-based biomarker for Alzheimer's Disease detection, demonstrating superior accur...
-Synuclein (-syn) strains can serve as discriminators between Parkinson's disease (PD) and related -synucleinopathies. The relationship between -syn s...
Normative modeling learns a healthy reference distribution and quantifies subject-specific deviations to capture heterogeneous disease effects. In Alz...
When a mouse perceives a hawk's shadow, it may have only seconds to decide where to run, yet the safest refuge is often neither visible nor nearby. To...