Sepsis

An interpretable deep learning framework for predictive modeling of postoperative infections in ICU patients.

A significant proportion of intensive care unit (ICU) patients undergo surgical procedures, and some may develop postoperative infections. Accurately predicting postoperative infection risk and identifying key contributing factors is crucial for improving postoperative management and understanding infection mechanisms. However, this task is challenging due to the complex interplay of multiple risk...

ACAT1-mediated lactylation reprogramming governs immune-stromal crosstalk in ulcerative colitis.

Ulcerative colitis (UC), a chronic inflammatory bowel disorder characterized by progressive colonic inflammation, presents escalating global incidence rates and heightened risk of colorectal carcinogenesis. Despite emerging evidence implicating lactylation - a novel post-translational modification - in diverse biological pathways, its mechanistic involvement in UC pathogenesis remains poorly eluci...

Equiflow: An open-source software package for evaluating changes in cohort composition.

Clinical research studies routinely apply exclusion criteria and data preprocessing steps that can substantially alter dataset composition, potentiall...

Deep learning analysis of sugar beet (Beta vulgaris ssp vulgaris) protease inhibitors interacting with identified trypsins from its primary pathogen, the sugar beet root maggot Tetanops myopaeformis.

Beta vulgaris ssp. vulgaris (sugar beet) is one of two plants globally from which sugar is widely produced, accounting for 55% of U.S. sugar $1B (U.S....

Development and external validation of a machine learning model for predicting chronic critical illness in ICU patients with acute pancreatitis.

BACKGROUND: To develop and validate a machine learning (ML) model to assess the risk of chronic critical illness (CCI) in intensive care unit (ICU) pa...

A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a major complication in the intensive care unit (ICU), and early risk stratification remains challen...

Comparing the Generalizability of Multiregional versus Locally Trained Deep Learning Models for Trachoma Detection.

OBJECTIVE: To test the advantage of geographically diverse, multiregional training of artificial intelligence models over single-region training for d...

A Histone Deacetylase Activity Model for the Discovery and Validation of Sepsis Biomarkers.

INTRODUCTION: Histone deacetylases (HDACs) play critical roles in immune regulation and inflammatory responses in sepsis. This study identified HDAC-r...

A responsible AI framework for infection surveillance in low-resource settings: ethics, opportunities and threats for LMICs (EOT-LMICs).

The integration of artificial intelligence (AI) into infection surveillance represents a significant shift in public health; however, most AI framewor...

Dynamic trajectories of organ dysfunction in sepsis using the SOFA-2 score and early prediction from multicenter cohorts.

OBJECTIVES: The Sequential Organ Failure Assessment-2 (SOFA-2) score reflects contemporary intensive care practices, yet its longitudinal application ...

Circulating Immune Profiles in ACTH-Dependent Cushing's Syndrome.

OBJECTIVE: ACTH-dependent Cushing's syndrome (CS) causes profound immune dysfunction and severe infections. This study aimed to characterize immune ce...

Using Machine Learning to Design Effective Antimicrobial Dosing Regimens.

Resistant bacterial infections remain a major clinical challenge, often necessitating combination therapy, namely use of two or more antibiotics with ...

Clinical decision support system for breathlessness.

PURPOSE OF REVIEW: This review summarizes high-level evidence on clinical decision support systems, both more classical rules-based and emerging artif...

Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.

Individualized treatment strategies and long-term prognosis of congenital hydrocephalus: an integrated analysis based on multicenter retrospective data and machine learning.

INTRODUCTION: Hydrocephalus is a common pediatric neurological disorder characterized by abnormal head enlargement, intellectual disability, visual im...

Rapid Identification and Quantification of Aqueous Antibiotics over a Machine Learning-Integrated Raman Sensor Array.

Surface-enhanced Raman spectroscopy (SERS) is a promising technique for on-site detection of aqueous pollutants, whereas single-substrate SERS suffers...

A machine learning model for post-operative sepsis prediction in acute surgical patients: a multi-centre, prospective study.

Parsimonious and explainable biomarker-based severity score for hospitalised patients with COVID-19-related respiratory infections: development, validation and XAI benchmarking.

BACKGROUND: Severity scoring systems are increasingly important tools for stratifying hospitalised patients, guiding treatment decisions, and enabling...

Performance of Large Language Models vs Conventional Machine Learning for Predicting Clinical Outcomes With Limited Data: Comparative Study.

BACKGROUND: Machine learning (ML) can be used to predict clinical outcomes. Training predictive models typically requires data for hundreds or thousan...

Bulk Microarray and Single-Cell Transcriptomic Analyses Reveal Bacterial Lipopolysaccharide-Related Biomarkers in Sepsis.

Sepsis, a life-threatening condition triggered by dysregulated host response to infection, poses significant global health challenges. Identifying lip...