Geriatrics

Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic Shock.

BACKGROUND: Cardiogenic shock (CS) is a critical condition of end-organ hypoperfusion with high mortality. Fluctuations in blood glucose (BG) levels may exacerbate cardiovascular instability in critically ill patients. Time In Tight Range (TITR), defined as the percentage of time in the target BG range of 3.9-7.8 mmol/L (70-140 mg/dL), has become an increasingly important index of glycaemic status...

GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.

INTRODUCTION: Accurate clinical diagnosis of neurodegenerative diseases remains challenging, particularly when individuals have mixed pathologies. We implemented the generalizable protein-based neurodegenerative disease artificial intelligence (GPND-AI) classifier using the NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) central nervous system (CNS) panel to classify Alzheimer's disease, Parkin...

Bibliometric analysis of Alzheimer's and dementia research in Latin America.

INTRODUCTION: Dementia is increasing rapidly in Latin America and the Caribbean (LAC), but research output remains limited. Tracking publication trend...

Multimodal deep learning neuroimaging approach to enhance CT-based diagnosis of Alzheimer's disease.

Neuroimaging plays a critical role in the diagnosis of Alzheimer's disease (AD), with Magnetic Resonance Imaging (MRI) and Positron Emission Tomograph...

Integrated plasma proteomics and metabolomics reveal immunometabolic pathways and predictive signatures for age-related eye diseases.

BACKGROUND AND AIMS: Age-related eye diseases (AREDs) share aging as a major risk factor, but the systemic molecular changes preceding disease onset r...

Discovery of novel MDM2 inhibitors from a Penicillium metabolome library: An integrated phylogenetic, machine learning, and molecular simulation approach.

Restoring the tumor-suppressor function of p53 by inhibiting its negative regulator, MDM2, represents a significant therapeutic avenue for cancers tha...

Fluoroscopic image-driven deep learning model for predicting intussusception irreducibility during air enema in children.

BACKGROUND: Accurate identification of irreducible intussusception during air enema is crucial for optimizing enema strategies. Current methods are li...

Discovering non-linear dynamics of miRNAs in Alzheimer's disease-related cognitive impairment: a cross-species approach with explainable machine learning.

BACKGROUND: MicroRNA (miRNA) biomarker studies in Alzheimer's disease (AD) typically assume monotonic relationships between expression levels and dise...

Interpretable Deep Survival Analysis of Alzheimer's Disease via Metabolic Genetic Variants.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease. Traditional models for estimating AD onset cannot capture nonlinear i...

Student Performance in Bitewing Caries Detection: Artificial Intelligence Versus Alternative E-learning.

INTRODUCTION AND AIMS: This study compared 3 methods for teaching caries detection in bitewings: a prerecorded lecture, a preannotated dataset, and an...

Choroidal vascularity across aging and the spectrum of age-related macular degeneration: an AI-based OCT study.

PURPOSE: To characterize the choroidal morphology across a spectrum of participants affected by normal aging as well as age-related macular degenerati...

Integrated High-Throughput Targeted Metabolomics and Machine Learning for Early Prediction and Prevention of Postoperative Delirium in Older Adult Surgical Patients: Prospective Multicenter Cohort Study.

BACKGROUND: Postoperative delirium (POD) is a common and severe complication in older adult patients with hip fracture, yet its pathogenesis remains u...

Assessing osteosarcopenia using automated multi-structure-segmentation and Hounsfield unit-based analysis of lumbar muscles and vertebral bodies on computed tomography.

Sarcopenia and osteoporosis are prevalent age-related conditions that substantially increase the risk of falls, fractures, and functional decline. Thi...

[Current applications and prospects of artificial intelligence in perioperative comprehensive management for gastrointestinal surgery].

The shift from the traditional empirical approach to a more data-driven method in the diagnosis and treatment of GI cancers is significant due to adva...

Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer's disease via data fusion and machine learning.

Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) can improve patient care. This ...

Rotenone impairs osteoclastogenesis by inhibiting cell migration: insights from machine learning.

PURPOSE: The migration and fusion of osteoclast precursors (OCPs) are critical steps in osteoclastogenesis and require substantial energy. Although ox...

Response to comment on "Assessing deep learning model performance in osteoporosis screening with lumbar spine radiographs".

End-to-End Image Compression With Segmentation Guided Dual Coding for Wind Turbines.

Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects...

Assessing elderly walkability to urban parks using mobility analysis and multi-source data: a case study of central Fuzhou, China.

Ensuring equitable park access amid rapid urbanization and population ageing is essential. We propose an integrated framework that couples street-netw...

The role of mechanical power in lung ventilation for the prevention of ventilator-induced lung injury: a narrative review.

Ventilator-induced lung injury continues to limit outcomes in patients with acute respiratory failure despite established lung-protective strategies.M...