Sepsis

Investigating the Effect of Hospital Infection Control Informatization on Optimizing Microbiological Specimen Submission Before Antibiotic Therapy: Failure Mode and Effects Analysis.

BACKGROUND: Antimicrobial resistance (AMR) poses a critical global health threat, with inappropriate antibiotic use being a major driver. Timely microbiological specimen submission before initiating antibiotic therapy is a cornerstone of antimicrobial stewardship (AMS), enabling pathogen-directed therapy and reducing unnecessary broad-spectrum exposure. However, suboptimal compliance remains commo...

Staphylococcus aureus resistance to non-β-lactam antibiotics: global genomic epidemiology and machine learning feasibility assessment.

Staphylococcus aureus is increasingly resistant to β-lactam antibiotics, making non-β-lactam cell-wall-targeting drugs crucial alternatives. Growing resistance to these agents highlights the need to identify genomic factors influencing susceptibility. Machine learning can integrate genomic and phenotypic data to predict minimum inhibitory concentrations (MICs) and uncover resistance mechanisms acr...

GOLD 2026: Transforming COPD Management with Early Intervention, Multi-dimensional Assessment, and Personalized Care.

The 2026 report from the Global Initiative for Chronic Obstructive Lung Disease (GOLD) introduces substantial conceptual and practical updates to the ...

[The evolution of antimicrobial therapy for sepsis].

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host immune response to infection, representing a critical global public healt...

MedFusion-gP-AKI: development and multicenter validation of a machine learning fusion model for early prediction of KDIGO stage 3 acute kidney injury in critically ill traumatic cervicothoracic spinal cord injury patients.

KDIGO stage-3 acute kidney injury (AKI), a life-threatening complication in critically ill patients with traumatic cervicothoracic spinal cord injury ...

Identifying and timing patient outcomes in clinician notes using large language models.

BACKGROUND: Key challenges in leveraging unstructured clinician notes for predictive models include identifying and timing patient outcomes. To addres...

The essential role of methylation in maize (Zea mays) defense against the seed-colonizing fungus Aspergillus flavus.

UNLABELLED: The interaction between maize (Zea mays) and Aspergillus species poses a huge threat to global food safety due to the production of aflato...

CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.

Developing short, stable, and potent antimicrobial peptides is a promising strategy to combat antibiotic resistance and persistence. We present CAMPER...

Resveratrol ameliorates sepsis-induced immune dysfunction by targeting GADD45A to maintain hematopoietic stem cell homeostasis: A predictive study integrating ensemble machine learning, single-cell transcriptomics, and molecular dynamics simulation.

OBJECTIVE: To investigate the potential pharmacological mechanisms of resveratrol (RES) in ameliorating sepsis-associated immune dysfunction via the G...

Gut microbial modulation of 3-hydroxyanthranilic acid and dopaminergic signalling influences attention in obesity.

BACKGROUND: Obesity-related alterations in the gut microbiota have been linked to cognitive decline, yet their relationship with attention remains poo...

The relationship between urinary tract infections and sepsis: results from Mendelian randomization and MIMIC data study.

Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.

BACKGROUND: Vancomycin-resistant Enterococcus (VRE) infection is a life-threatening complication after liver transplantation (LT). This study aimed to...

Integrative multi-omics and machine learning identify mitochondrial biomarkers for pathogen-specific sepsis stratification and translational prioritization.

BACKGROUND: Sepsis is a leading cause of critical illness and mortality, yet substantial heterogeneity limits risk stratification and biomarker transl...

From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids.

Brucellosis is an important zoonotic disease affecting humans, livestock, and wildlife, yet prevalence estimates in wild species are often underestima...

Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning.

BACKGROUND: Patients with myocardial infarction (MI) complicated by out-of-hospital cardiac arrest (OHCA) represent a heterogeneous population with va...

Development of machine-learning models to diagnose influenza among international travellers based on symptoms and epidemiological information.

INTRODUCTION: The importation of infectious diseases has increased dramatically in recent years. The diagnosis of such diseases has traditionally been...

A machine learning-assisted Ag-TiO2 SERS platform for intraoperative osteomyelitis diagnosis.

Osteomyelitis is a progressive bone infection with high disability and recurrence rates. However, current diagnostic approaches, including bacterial c...

AI-Driven Analysis of Cardiopulmonary Exercise Tests to Identify Gas Exchange and Ventilatory Thresholds.

BACKGROUND: A cardiopulmonary exercise test (CPET) provides the estimated lactate threshold (θLT) and respiratory compensation point (RCP) through vis...

A multi-gradient microfluidic chip-based neutrophil chemotaxis analysis for sepsis auxiliary diagnosis and prognostic monitoring.

Sepsis involves life-threatening immune dysregulation where impaired neutrophil chemotaxis is a critical indicator. We developed a multi-gradient neut...

Surgical MAchine learning model for predicting Risk of postoperative infecTions in general elective surgery (SMART): A modelling study.

OBJECTIVE: To apply a machine learning (ML) model that we developed and internally validated for predicting postoperative infection likelihood after e...