Sepsis

Integrating theory and machine learning to reveal determinants of plasmid copy number.

Plasmids are extrachromosomal mobile genetic elements whose copy numbers (PCNs) critically influence microbial evolution, antibiotic resistance and pathogenicity. Despite their importance and immense diversity, the ecological, evolutionary and molecular factors determining PCN remain poorly understood. Here, we present a theoretical model to explain the empirical power-law relationship between pla...

Lightweight large language models for early sepsis prediction via a semantic abstraction rule engine.

Sepsis remains a major global health challenge due to its high mortality rate and the difficulty of predicting its onset from nonspecific early symptoms. To address this, we explored the feasibility of using fine-tuned small-scale large language models (LLMs) to perform accurate sepsis prediction from structured clinical time-series data. We developed a Semantic Abstraction Rule Engine (SARE) that...

Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.

Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and ac...

From risk to resilience: A narrative review on strengthening veterinary clinical biosecurity to prevent healthcare-associated infections.

Veterinary clinical biosecurity is key to preventing infectious diseases in animal clinics, particularly when there is high patient turnover, a mix of...

Machine learning-based precision subtyping and risk prediction in sepsis: a retrospective analysis using MIMIC-IV database.

Generative artificial intelligence for surgical site infection surveillance.

BACKGROUND: Surgical site infection (SSI) surveillance can be time consuming and resource intensive. This study investigates the potential of generati...

Can machine learning support infection control measures by predicting carbapenemase-producing Enterobacterales colonization at admission?

BACKGROUND: Early identification of patients with carbapenemase-producing Enterobacterales (CPE) colonization is crucial for infection control; howeve...

Serum-antibody Profiling of H3N2-infected Ferrets Using a Combinatorial Phage-display Random Peptide Library.

The repertoire of antibodies in serum, known as the "IgOme", is highly diverse and unique to each individual as it reflects the cumulative history of ...

Rhythms of mood: Bidirectional regulation of molecular clocks and neurotransmission in affective disorders.

Mood disorders, primarily major depressive and bipolar disorder, are characterized by significant neurochemical dysregulation and disturbances in biol...

Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids.

Leigh syndrome (Leigh) is an untreatable mitochondrial disorder characterized by lactic acidosis and basal ganglia and midbrain pathology, leading to ...

Machine learning predicts ICU mortality in cardiogenic shock: interpreting SOFA, lactate, and pH thresholds.

The role of triglyceride-glucose (TyG) index in predicting mortality risk in critically ill patients with sepsis: a machine learning model in a cohort study.

Distinct signatures in the human gut and oral microbiomes of gastric cancer.

Microbiome dysbiosis is increasingly recognized as a hallmark of gastric cancer (GC). Here, we analyzed gut and oral shotgun metagenomic data from 317...

Development of a calibrated logistic regression clinical risk score for predicting postoperative sepsis following retrograde intrarenal surgery.

Circulating Neutrophils Exhibit an Altered Immune Response in Chronic LUTS: An Image-Based Deep Learning Analysis.

INTRODUCTION AND HYPOTHESIS: Chronic lower urinary tract symptoms (LUTS) are characterised by persistent symptoms and elevated urinary leukocyte count...

Development of a LightGBM-based survival prediction model for major burn patients using LDH/LYM ratio as the core predictor.

BACKGROUND: Patients with extensive burns face a high mortality risk. Early identification of prognostic indicators may facilitate timely intervention...

Explainable machine learning for predicting infections that require hospitalization in patients with systemic lupus erythematosus.

OBJECTIVES: To develop and validate an explainable artificial intelligence (XAI)-based machine learning (ML) model for predicting infections requiring...

Realizing Aqueous High-Order Tripartite Synapse.

Future integration of artificial intelligence in biological settings imposes a non-negotiable requirement for advanced aqueous synapses, demanding not...

Decoupling lactylation-associated metabolic remodelling in Asthma: Integrated multi-omics and machine learning identify RCC2 as a potential therapeutic target.

BACKGROUND: Aerobic glycolysis promotes lactate accumulation and histone lactylation (Kla), yet the cell-type-specific distribution of this axis and i...

Evaluating the performance of large language models versus prosthodontic residents on the 2024 and 2025 National Prosthodontic Resident Examination.

STATEMENT OF PROBLEM: Recent advancements in large language models (LLMs) offer expanded reasoning, multimodal processing, and improved factual ground...