Sepsis Diagnosis and Treatment

Integrated Bioinformatics Analysis Reveals APEX1 as a Potential Biomarker for Septic Cardiomyopathy

The paper identifies APEX1 as a potential biomarker for septic cardiomyopathy through integrated bioinformatics analysis of gene expression data.

Therapeutic Effect of Anti-FABP4 mAb (6H2) on Systemic Inflammation in Sepsis: Insights from Abdominal Organ Analysis

This study evaluates the therapeutic effect of anti-FABP4 mAb 6H2 in sepsis, showing reduced inflammation and organ damage in mice.

Generating synthetic data with a mechanism-based Critical Illness Digital Twin: Demonstration for Post Traumatic Acute Respiratory Distress Syndrome

The paper demonstrates generating synthetic cytokine time series data using a Critical Illness Digital Twin to improve ML/AI predictive performance for post-traumatic acute respiratory distress syndrome.

Preparing for the next Pandemic: Simulation-based Deep Reinforcement Learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents

This paper uses simulation-based deep reinforcement learning to discover effective multimodal treatment strategies for systemic inflammation using existing immunomodulatory drugs.

A Genome-Wide Association Study of Survival in Patients with Sepsis

This paper conducts a genome-wide association study (GWAS) to identify genetic variants associated with 28-day survival in sepsis patients, finding three novel variants linked to reduced survival.

Repositioning linifanib as a potent anti-necroptosis agent for sepsis

This study identifies linifanib as a potential anti-necroptosis agent for sepsis through bioinformatics and experimental validation.

Identification of deleterious neutrophil states and altered granulopoiesis in sepsis

The paper identifies deleterious neutrophil states and altered granulopoiesis in sepsis using single-cell multi-omics data from sepsis patients and controls.

Transcriptional response in a sepsis mouse model reflects transcriptional response in sepsis patients

The paper compares the transcriptional response in a sepsis mouse model to human sepsis patients and identifies a shared molecular signature.

Metagenomic evidence for a polymicrobial signature of sepsis

This study identifies microbial hallmarks of sepsis using metagenomic data and shows that polymicrobial communities, not single pathogens, are key in sepsis diagnosis.

The orchestrated cellular and molecular responses of the kidney to endotoxin define the sepsis timeline

This study uses single-cell RNA sequencing to define the temporal cellular and molecular changes in the kidney during sepsis, identifying key gene expression shifts and communication failures that mark critical points in the sepsis timeline.

Transcription Factors STAT5A and SPI1 Reveals RHBDD2 as a Potential Biomarker in Sepsis and Septic Shock

This study identifies RHBDD2 as a potential biomarker in sepsis and septic shock through gene expression analysis and pathway enrichment.

Deficiency in reverse cholesterol transport in mice augments sepsis

This study shows that liver-specific SR-BI deficiency in mice increases susceptibility to CLP-induced sepsis, highlighting RCT's role in sepsis resistance.

Gut lymph purification regulates monocyte activity in rats with ischemia-reperfusion injury-induced sepsis

This study shows that gut lymph purification reduces intestinal toxic substances and modulates monocyte activity in rats with sepsis induced by ischemia-reperfusion injury.

Immunosuppression, rather than inflammation, is a salientfeature of sepsis in an Indian cohort

The paper analyzes transcriptomic data from sepsis patients in India, finding that immune response genes are more suppressed than inflammation-related genes in severe sepsis and septic shock.

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

The paper identifies 54 differentially expressed genes in pediatric septic shock patients and links them to immune response and chemokine pathways, distinguishing survivors from non-survivors.

Higher levels of grp78 and lower levels of tlr4 anticipate a positive evolution of sepsis and patient survival

The paper correlates grp78 and tlr4 mRNA levels in septic patients to predict sepsis progression and survival.

Viral and bacterial infection elicit distinct changes in plasma lipids in febrile children

This study identifies distinct lipidomic changes in febrile children with bacterial vs. viral infections, showing potential for metabolic biomarkers to aid in clinical diagnosis.

Multimorbidity states with high sepsis-related deaths: a data-driven analysis in critical care

This study identifies multimorbidity subgroups in critical care patients and finds one subgroup has over 2-fold higher sepsis-related mortality.

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

The paper develops a predictive model for ICU mortality in septic and cardiogenic shock using feature selection and Bayesian Networks, identifying time-dependent predictive features.

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

This paper evaluates a machine learning algorithm for predicting severe sepsis and shows it reduces mortality, length of stay, and readmission rates in real-world clinical use.

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

This study develops machine learning models to predict sepsis onset using real-time physiological data and white blood cell counts, achieving early detection up to 3.5 hours before symptoms.

Mortality prediction in sepsis via gene expression analysis: a community approach

The paper develops gene-expression-based prognostic models for 30-day mortality in sepsis using community-onset and hospital-acquired sepsis cohorts, showing improved prediction when combined with clinical scores.

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

This paper validates a sepsis prediction algorithm using vital sign data across multiple settings, showing strong performance and generalizability.

Pediatric Severe Sepsis Prediction Using Machine Learning

This study develops a machine learning algorithm using EHR data to predict pediatric severe sepsis onset, achieving high AUROC scores and outperforming existing clinical scores.

Sepsis Reconsidered: Identifying Novel Metrics For Behavioral Landscape Characterization With A High-Performance Computing Implementation Of An Agent-Based Model

This paper uses a high-performance computing agent-based model of sepsis to identify novel metrics for characterizing the behavioral landscape of sepsis and highlights challenges in clinical data sparseness and reproducibility.

Determining controllability of sepsis using genetic algorithms on a proxy agent-based model of systemic inflammation

This paper uses genetic algorithms on an agent-based model of the immune system to explore multi-targeted control strategies for sepsis, showing that multi-point and time-dependent interventions are necessary.

Evaluation of the Angus ICD9-CM Sepsis Abstraction Criteria

The paper evaluates the Angus ICD9-CM sepsis abstraction criteria, finding high specificity but moderate sensitivity in identifying suspected infections in emergency department patients.