AI Medical Compendium

RLSFmode: A deep learning approach for predicting RNA-small molecule binding modes via molecular surface modeling.

International journal of biological macromolecules May 25, 2026

As RNA becomes an emerging therapeutic target, predicting the binding mode of RNA-small molecule through emerging algorithms is of crucial importance in drug development. A complete prediction of binding modes should not only include recognition of n... read more

Constitutive activation of a hybrid two-component regulator reveals cross-regulation of polysaccharide utilization genes in Bacteroides.

The Journal of biological chemistry May 25, 2026

Human gut microbes, such as Bacteroides, rely on specialized gene clusters known as polysaccharide utilization loci (PULs) to metabolize diverse dietary and host-derived glycans. A major class of transcription regulators of these PULs is the hybrid t... read more

AI-Informed Architectural Insights of Three-Dimensional Glandular Networks Identify Prostate Cancer Patients at a Higher Risk of Biochemical Recurrence.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc May 25, 2026

Pathologists diagnose and grade prostate cancer using thin two-dimensional (2D) histological sections, but these 3-5 micron sections are too thin to visualize complete glandular networks and three-dimensional (3D) spatial relationships of adenocarcin... read more

Diagnosing the Spatiotemporal Evolution of Anthropogenic CO2 Emission Drivers in China Under Systemic Disruptions Using Interpretable Machine Learning.

Environmental research May 25, 2026

Understanding how anthropogenic CO2 emissions (ACE) respond to large-scale systemic disruptions is essential for climate mitigation and environmental management. Here, we develop an interpretable machine learning framework that integrates an XGBoost ... read more

Unveiling hidden heavy metal hotspots in mining landscapes using integrated hyperspectral remote sensing.

Environmental pollution (Barking, Essex : 1987) May 25, 2026

Regional ecological risk assessments typically rely on interpolated toxic heavy metal (THM) surfaces derived from sparse field samples. However, this approach is fundamentally constrained by sampling density and interpolation errors, resulting in hig... read more

Fast MR elastography via deep learning-based phase interpolation: A technical feasibility study.

Magnetic resonance imaging May 25, 2026

Clinical MR elastography (MRE) typically requires acquisition of four vibration phase images synchronized with external vibrations, prolonging acquisition time and increasing slice misalignment risk due to inadequate breath-holding. To address this l... read more

Multi-epitope tick vaccines: From computational design to field deployment, immunoinformatics approaches, validation challenges, and translational pathways.

Parasitology international May 25, 2026

Ticks are major vectors of pathogens affecting both humans and animals, yet effective and sustainable control strategies remain limited. Conventional vaccines based on the concealed antigen Bm86 provide only partial protection and are often constrain... read more

Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study.

Clinical nutrition ESPEN May 25, 2026

BACKGROUND: Protein-energy wasting (PEW) is common in chronic kidney disease (CKD) and is linked to poor outcomes. Early risk stratification may enable timely nutritional intervention. OBJECTIVE: To develop an electronic medical record (EMR)-based ma... read more

Multimodal digital phenotyping of bipolar disorder subtypes: Differences between bipolar disorder I and II based on passive and active behavioral features from smartphones.

Journal of affective disorders May 25, 2026

BACKGROUND: Distinguishing between bipolar disorder type I and II constitutes a significant clinical challenge that relies on retrospective patient recall. Misclassification carries risks of inappropriate pharmacological management. Digital phenotypi... read more

Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.

Brain research bulletin May 25, 2026

Family heritage is one of the most powerful risk factors for attention-deficit/hyperactivity disorder (ADHD). Children with familial ADHD (ADHD-F) and non-familial ADHD (ADHD-NF) have both shared and distinct behavioral characteristics and clinical o... read more

Artificial Intelligence Medical Compendium