Infectious Disease

DeepHeptox: An Interpretable Deep Learning Model for Multi-Endpoint Hepatotoxicity Prediction of Chemical Compounds.

Hepatotoxicity represents a major adverse outcome of chemical exposure, as the liver plays a central role in xenobiotic metabolism and detoxification. Accurate prediction of hepatotoxicity is therefore essential for drug development and chemical safety assessment. Computational methods provide an efficient and ethical alternative for assessing hepatotoxicity before experimental validation. To addr...

Regenerable "Two-in-One" molecularly imprinted nanozymes colorimetric sensor array combined with deep learning enables selective removing and visual discriminate of tetracycline antibiotics.

Tetracycline (TC) and its analogues are widely employed broad-spectrum antibiotics. Their abuse and environmental residues have raised severe ecological and public health risks, accelerating spread of bacterial resistance. Traditional methods fail to simultaneously detect and eliminate multiple tetracycline antibiotics (TCs) in complex matrices. Herein, we fabricate a multifunctional photo-respons...

Developing machine learning models to improve cardiovascular risk prediction for people living with HIV.

BACKGROUND: As life expectancy rises for people with HIV, atherosclerotic cardiovascular disease (ASCVD) has become a major contributor to morbidity. ...

Predicting breast cancer pathological complete response with clinical and imaging data.

INTRODUCTION: Pathological complete response (pCR) is a key prognostic indicator in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NA...

Random Forest Modeling to Predict Small Molecule Accumulation in Gram-Negative Bacteria.

Despite extensive efforts over the past ∼60 years to discover new classes of Gram-negative-active antibiotics, the development pipeline remains relati...

Bridging the Vaccine Gap: Scientific and Technological Advances for Diseases Lacking Effective Vaccines.

Despite remarkable achievements in infectious disease control, more than 20 major pathogens responsible for significant global morbidity and mortality...

Reinforcement learning for treatment decision-making in sepsis: a scoping review.

This scoping review summarizes the progress of reinforcement learning (RL) in clinical decision-making for sepsis at the intersection of medicine and ...

DB-IRES: a deep learning model based on ensemble learning for predicting internal ribosome entry sites.

BACKGROUND: Internal ribosome entry sites (IRES) are cap-independent translation initiation elements present in specific viral and cellular mRNAs. The...

A combi-elasto ultrasound based deep learning model on liver fibrosis staging for suspected MASLD/MASH patients.

OBJECTIVES: Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) weakened the ...

Integrating single-cell sQTL mapping with deep-learning splicing prediction identifies causal variants under influenza infection.

BACKGROUND: Realizing the full potential of human genetics requires identifying causal variants and genes underlying association signals. Molecular qu...

Mechanistic study on comorbidities of kidney stones and sepsis using transcriptome sequencing combined with single-cell spatial transcriptome sequencing.

Kidney stones and sepsis have a complex pathological association. Urinary obstruction and infection caused by kidney stones can easily induce sepsis, ...

Recent developments in the therapeutic management of acid-related gastrointestinal diseases.

INTRODUCTION: Acid-related gastrointestinal diseases, particularly gastroesophageal reflux disease (GERD) and peptic ulcer disease (PUD), remain major...

Integrated statistical and machine-learning optimization for enhanced heparosan production by Lactococcus lactis.

Lactococcus lactis SH6 was engineered for heterologous heparosan production, and its growth medium was optimized using a combination of experimental d...

Artificial Intelligence Across the Vaccine Clinical Trial Lifecycle: Evidence, Readiness, and Guardrails.

Artificial intelligence (AI) is increasingly being used to support clinical research, but its value in vaccine clinical trials requires careful eviden...

Synergistic Static and Dynamic Interfacial Regulation Toward Robust Zinc Batteries.

Aqueous zinc (Zn) metal batteries (AZMBs) are promising for sustainable energy storage, yet their development is severely compromised by the interfaci...

Artificial Intelligence Applications in Antimicrobial Resistance: Comprehensive Review of Predictive Models, Diagnostic Innovations, and Clinical Integration.

Antimicrobial resistance (AMR) represents a critical global health crisis, driving increased mortality, treatment failure, and economic burden. Artifi...

A flexible rank-based framework for individualized treatment selection with mixed-type multivariate outcomes.

Personalized treatment selection is often based on a single primary endpoint, even though complex diseases are typically monitored using multiple, het...

Truth, tone, and trust: integrating an AI exercise to foster responsible science communication in a graduate biomedical science course.

As generative AI becomes a standard fixture in graduate education, the pedagogical challenge has shifted from detection to integration. This article d...

Differentiating septic arthritis from non-infectious inflammatory causes of acute monoarticular arthritis in children: A machine learning approach based on routine laboratory tests.

OBJECTIVE: Acute monoarthritis in children poses a diagnostic challenge, particularly in distinguishing septic arthritis from non-infectious inflammat...

Identifying severe COVID-19 risk variants modulating enhancer reporter activity in lung cells.

Common genetic variants contribute to risk for complex human diseases. However, despite thousands of associations, variants modulating disease risk an...