Early Warning Models Using Machine Learning to Predict Sepsis-Associated Chronic Critical Illness: A Study Based on the Medical Information Mart for Intensive Care Database.

Background Patients with chronic critical illness (CCI) experience poor prognoses and incur high medical costs. However, there is currently limited clinical awareness of sepsis-associated CCI, resulting in insufficient vigilance. Therefore, it is necessary to build a machine learning model that can predict whether sepsis patients will develop CCI.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis.

Background: Cuproptosis is a copper-dependent cell death that is connected to the development and immune response of multiple diseases. However, the function of cuproptosis in the immune characteristics of sepsis remains unclear.

Method: We obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages.

Diagnostic Value and Clinical Application of Metagenomic Next-Generation Sequencing for Infections in Critically Ill Patients.

Objective: To evaluate the diagnostic value and clinical application of metagenomic next-generation sequencing (mNGS) for infections in critically ill patients.

Methods: Comparison of diagnostic performance of mNGS and conventional microbiological testing for pathogens was analyzed in 234 patients. The differences between immunocompetent and immunocompromised individuals in mNGS-guided anti-infective treatment adjustment were also analyzed.

Comparison of endotracheal aspirate and bronchoalveolar lavage fluid metagenomic next-generation sequencing in severe pneumonia: a nested, matched case-control study.

Objectives: To compare clinical outcomes in patients with severe pneumonia according to the diagnostic strategy used.

Methods: In this retrospective, nested, case-control study, patients with severe pneumonia who had undergone endotracheal aspirate (ETA) metagenomic next-generation sequencing of (mNGS) testing (n = 53) were matched at a ratio of 1 to 2 (n = 106) by sex, age, underlying diseases, immune status, disease severity scores, and type of pneumonia with patients who had undergone bronchoalveolar lavage fluid (BALF) mNGS. The microbiological characteristics and patient's prognosis of the two groups were compared.