Infectious Disease

Application of Whole-Genome Sequencing and Metagenomic Sequencing in Microbial Analysis of Milk Powder and Its Processing Environment: Current Findings and Challenges.

As dairy enterprises increasingly focus on microbial contamination, traditional detection technologies are gradually showing limitations in terms of detection capability, accurate source tracking, and rapid response, especially when dealing with microbial communities in complex processing environments. Fortunately, whole-genome sequencing (WGS) and metagenomic sequencing provide innovative alterna...

T3SS effector and regulator discovery by predicting interacting partners of T3SS chaperones in Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a prominent opportunistic pathogen whose virulence is closely linked to its Type III Secretion System (T3SS), a specialized apparatus that injects effector proteins into host cells. T3SS chaperones are essential for stabilizing, delivering, and regulating T3SS expression. Multiple T3SS effectors and regulators have been identified and characterized in the P. aeruginosa re...

Structural basis of secreted acid phosphatase polymerization in the Leishmania parasite.

Enzymes that assemble into filaments typically transition between protomeric and polymeric states in response to cellular conditions. In contrast, the...

AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review.

Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions ...

Alphafuser: a parsimonious approach to predicting higher-order protein complexes.

Many biological processes rely on the activity of multiple proteins acting in concert as part of higher-order oligomeric complexes. However, the major...

An XGBoost-Based Multicenter Model for Predicting HBV-Related Hepatocellular Carcinoma: Development and Validation.

BACKGROUND: The 5-year survival rate for hepatocellular carcinoma (HCC) is stage-dependent, yet existing models lack accuracy in predicting hepatitis ...

Analysis of AI-Generated Radiography Responses Using a Closed-System LLM.

PURPOSE: To evaluate the accuracy and educational utility of Microsoft Copilot's (GPT-4, July 2025, closed-system version) responses to radiography qu...

Machine learning method for the prediction of Bedaquiline-resistant Mycobacterium tuberculosis.

The study addresses the increasing resistance to the FDA-approved drug Bedaquiline (BDQ) in Mycobacterium tuberculosis (MTB). The absence of any defin...

An integrated machine learning and computational framework with experimental validation for the identification of novel CXCR4 inhibitors.

Chemokine receptor 4 (CXCR4) is a clinically significant G protein-coupled receptor implicated in HIV-1 entry, cancer progression, immune regulation, ...

Integrating AI-enhanced kinase enrichment analysis (KEA) with geometric deep learning and federated learning for precision drug repurposing.

Artificial intelligence (AI) is reshaping drug repurposing by integrating systems biology with molecular design. Here, we present a unified framework ...

A Novel Model in Predicting Dengue Fever Considering Spatial Heterogeneity of Nine Impact Factors and Four AI Algorithms in China.

BACKGROUND: Previous studies have explored the relationships between dengue fever (DF) and its impact factors, using various models. However, few have...

Risk prediction for linezolid-induced thrombocytopenia and exposure-guided safety optimization.

Linezolid (LZD)-associated thrombocytopenia (TP), a type of major hematotoxicity that can lead to the discontinuation of LZD treatment in patients wit...

AI for Hospital Infection Prevention and Control: Real-World Implementation, Impact, and the Gap Beyond Model Development.

BACKGROUND: Artificial intelligence and machine learning (AI/ML) may strengthen hospital infection prevention and control (IPC) through automated surv...

Evaluation of AI-enhanced FTIR spectroscopy for species and biovar typing of intracellular pathogenic Brucella spp.

BACKGROUND: Rapid and accurate identification of intracellular pathogenic Brucella species and biovars is essential for effective public health survei...

Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.

BACKGROUND: Tuberculosis-diabetes mellitus (TB-DM) multimorbidity significantly increases the risk of multidrug-resistant/rifampicin-resistant tubercu...

Using Generative Artificial Intelligence to Identify Central Line-associated Bloodstream Infections.

BACKGROUND: Central line-associated blood stream infection (CLABSI) surveillance is mandated and publicly reported in United States hospitals but requ...

Lipid abnormalities in chronic viral hepatitis: associations and machine learning-enhanced prediction.

Exploring the mechanisms and management of sepsis-related cardiac dysfunction.

Sepsis-related cardiac dysfunction (SRCD) represents a critical determinant of both acute mortality and long-term cardiovascular morbidity in sepsis s...

Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?

Although current antiretroviral therapy effectively suppresses viral replication in infected individuals, infections remain incurable. One of the main...

Teachers' Knowledge, Attitudes, and Behaviors Regarding Hearing Loss and Inclusive Education.

OBJECTIVE: This study assessed the knowledge levels of classroom teachers concerning students with hearing loss (HL) and hearing aids (HAs) and examin...