Osteoporosis

Deep Learning Models Capture Umbrella Sampling-Derived Energetic Trends: A Troponin C Case Study.

Deep learning models have transformed several fields lately. In the past, capturing thermodynamic trends from free energies has relied on computationally expensive and time-consuming umbrella sampling simulations for dynamic proteins. Here, we investigate whether modern deep learning protein design methods (ProteinMPNN and ThermoMPNN) can obtain comparable energetic readouts more expeditiously. As...

The Role of Artificial Intelligence in Addressing Menopausal Mental Health Challenges.

Menopause marks a crucial transition in a woman's life and is often accompanied by physical and psychological changes that can adversely affect mental health. Depression, anxiety, cognitive changes, and sleep disturbances are common during the menopausal transition, yet they are frequently underdiagnosed and undertreated, particularly in lowand middle-income countries. Emerging technologies, espec...

Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.

BACKGROUND: Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by...

Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis.

BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demons...

Accuracy of breast density assessment using artificial intelligence by convolutional neural network for carriers of UGT1A1 polymorphisms with Gilbert's Syndrome - a pilot study.

INTRODUCTION: Gilbert's Syndrome (GS), an indirect hyperbilirubinemia resulting from the reduced hepatic bilirubin-glucuronyltransferase enzyme activi...

Systematic review of risk prediction models for autogenous arteriovenous fistula dysfunction in maintenance hemodialysis patients.

OBJECTIVE: To systematically evaluate existing risk prediction models for autogenous arteriovenous fistula (AVF) dysfunction in maintenance hemodialys...

Genotype-Specific Electrophysiological Remodeling in PLN R14del Cardiomyopathy: Implications for Precision Antiarrhythmic Therapy.

BACKGROUND: Inherited PLN (phospholamban) R14del variants cause dilated cardiomyopathy with a high burden of malignant ventricular arrhythmias. Howeve...

Deep Learning-Based Deblurring for Computed Tomography and a Phantom Evaluation for Coronary Artery Calcium Detectability.

INTRODUCTION: This study aimed to develop and evaluate a deep learning (DL) deblurring algorithm for computed tomography (CT). Phantom testing assesse...

Automated body composition quantification from non-contrast CT for urolithiasis classification and exploratory incident risk assessment.

To develop a deep learning-based body composition quantification framework from non-contrast CT for urolithiasis classification (calcium, non-calcium,...

AI-Driven Advances in Vascular Aging Research: From Mechanisms to Precision Medicine.

Vascular aging is a fundamental contributor to the development of chronic diseases and has emerged as a critical focus in biomedical research. With th...

A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.

OBJECTIVE: Based on multicenter clinical data, this study aimed to develop and validate a predictive model for chronic low back pain (CLBP) after lumb...

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex.

Cortical control of movement is a distributed computation spanning multiple densely interconnected regions. Although we have rich anatomical atlases a...

Prediction of Acromial and Scapular Spine Fractures After Reverse Total Shoulder Arthroplasty using Machine Learning: A Retrospective Cohort Study.

BACKGROUND: Acromial and scapular spine fractures (ASSF) are an uncommon but significant complication following reverse total shoulder arthroplasty (r...

Body composition's effect on the bone-vascular axis of osteoporosis discovered in AI-based CT analysis of COPD patients.

OBJECTIVES: This study aimed to investigate the effect of body composition on the inverse relationship between vertebral bone density (T12 BMD) and to...

An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.

OBJECTIVE: This study aimed to develop and validate an interpretable machine learning model using readily available biochemical indicators to predict ...

Comparing conventional correction formulas and machine learning-based prediction of ionized calcium.

BACKGROUND AND AIM: Accurate assessment of ionized calcium (Ca++) is critical in clinical settings but remains technically and logistically challengin...

BHOD: a bone health optimized dietary pattern designed for osteoporosis prevention.

Existing dietary patterns were not specifically designed to target osteoporosis and lack the precision required for effective prevention. We aimed to ...

Pinoresinol diglucoside from Eucommia ulmoides attenuates diabetic cardiomyopathy through suppressing the store-operated calcium entry and downregulating the STIM1/Orai1/NFAT3 signaling pathway.

Pinoresinol diglucoside (PDG), an active component derived from Eucommia ulmoides, exhibits therapeutic effects against apoptosis, inflammation, and h...

A PLM-based method for predicting protein ion channel modulators for drug discovery and safety evaluation.

Ion channels are central to regulating neuronal communication, cardiac rhythm, and muscle contraction. Their modulation can induce therapeutic benefit...

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BACKGROUND: The objective of this study was to evaluate the performance of multiple machine learning algorithms to provide evidence supporting early i...