Sepsis

Genome-Wide Association Study and Fine Mapping Uncover Key Genetic Loci and Credible Genes of Intracranial Aneurysm and Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Intracranial aneurysm (IA) is a common neurovascular disorder; rupture causes aneurysmal subarachnoid hemorrhage with high mortality. Despite available interventions, effective preventative therapies are lacking. We aimed to identify novel genetic determinants of IA and aneurysmal subarachnoid hemorrhage to improve risk prediction and nominate hypothesis-generating therapeutic candidat...

Advancing poultry health: A meta-analysis of epitope-based and peptide-based vaccines against Avian Pathogenic E. coli with machine learning insights.

INTRODUCTION: Avian Pathogenic Escherichia coli (APEC) causes colibacillosis in poultry, which leads to tremendous economic losses. Traditional control methods, including antibiotics and conventional vaccines, are less effective due to the genetic diversity of APEC and developing antimicrobial resistance (AMR). Novel epitope- and peptide-based vaccines, supported by machine learning (ML), hold hig...

Environmental drivers of high-risk antibiotic resistance genes propagation in the plastisphere unveiled by meta-analysis and interpretable machine learning.

The plastisphere serves as an expanding reservoir and dissemination vector for antibiotic resistance genes (ARGs), yet the environmental driving facto...

Network analysis of serotonin CNVs shows biological convergence from genetic heterogeneity and discriminates between autism and developmental delay.

Differentiating autism spectrum disorder (ASD) from developmental delay (DD) is critical for guiding early intervention, but overlapping features and ...

A machine learning system enables just-in-time risk-stratified sepsis evaluations in the neonatal intensive care unit.

INTRODUCTION: NICU sepsis evaluation balances rapid antibiotic administration for mortality reduction against unnecessary treatment. Objective risk st...

Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning.

Acute kidney injury (AKI) is a severe and frequent complication following bee stings, with a reported incidence of 30-50%. Early identification is cli...

Machine learning-driven analysis of serum GDF15 trajectories identifies novel sepsis sub-phenotypes and predicts 28-day mortality.

BACKGROUND: Sepsis heterogeneity complicates management and prognosis. Growth differentiation factor 15 (GDF15) may offer novel insights into sepsis s...

Methods for determining indications for antibiotic prescriptions to facilitate antimicrobial stewardship: a scoping review.

BACKGROUND: Effective antimicrobial stewardship (AMS) requires accurate information on the reason for prescribing antimicrobials. To design targeted i...

Integrating single-cell and bulk transcriptomic analyses to explore key lactylation-related genes in benign prostatic hyperplasia.

Benign prostatic hyperplasia (BPH) is a prevalent age-related disorder characterized by chronic inflammation, metabolic dysregulation, and abnormal ce...

Integration of single-cell RNA-sequencing and machine learning identifies GRN and FCER1G as potential peroxisomal targets in influenza pathogenesis.

BACKGROUND: Influenza, an acute respiratory infection caused by influenza viruses, imposes a significant burden on the healthcare system. Peroxisomes ...

Integrative multi-omics and machine learning reveal glycolysis-related biomarkers driving vascular remodeling in pulmonary arterial hypertension.

Metabolic reprogramming toward aerobic glycolysis, a phenomenon analogous to the Warburg effect, is increasingly recognized as a hallmark of pulmonary...

Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance.

Augmented renal clearance (ARC) frequently occurs in critically ill septic patients and is known to impact survival outcomes. To address this, we aime...

SurgWound-Bench: a benchmark for surgical wound diagnosis.

Surgical site infection (SSI) is one of the most common and costly healthcare-associated infections, and surgical wound care remains a significant cha...

Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.

Sepsis-induced myocardial dysfunction (SIMD) is a common and heterogeneous complication in patients with sepsis and is associated with increased morta...

Machine learning identification of maternal inflammatory response and systemic inflammatory response from placental membrane whole slide images.

INTRODUCTION: The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Acute placental inflammation in the membranes...

Transcriptome and experimental verification identified candidate biomarkers related to mitochondrial metabolism in sepsis-associated encephalopathy.

BACKGROUND: Mitochondrial metabolism (MM) abnormalities have been implicated in multiple diseases, but its contribution to sepsis-associated encephalo...

Environmental Antibiotic Contamination and AMR: Integrating Pathways, Impacts, and AI-Driven Mitigation.

The widespread contamination of the environment with antibiotic residues is a significant factor contributing to the global crisis of antimicrobial re...

Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

BACKGROUND: Infections are a leading cause of hospitalizations and emergency department (ED) visits in home care. Existing prediction tools often unde...

An explainable machine learning model for early pediatric sepsis prediction using cytokine and routine laboratory data.

BACKGROUND: Early sepsis diagnosis in children remains challenging due to nonspecific presentations. This study aimed to develop an interpretable mach...

Integrating Causal Inference and Agent-Based Modelling to Assess the Impact of Clinicians' Guideline Adherence in Older Adults Hospitalized with Pneumonia.

This study integrates agent-based modeling (ABM) and causal machine learning (ML) to assess the impact of clinicians' adherence to antibiotic guidelin...