Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.

Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database.

Isolation and characterization of ; a novel clade of phages within the subfamily .

Unlabelled: is a commensal inhabitant of the mammalian gut microbiota, frequently associated with various gastrointestinal diseases. There is increasing interest in comprehending the variety of bacteriophages (phages) that target this bacterium, as such insights could pave the way for their potential use in therapeutic applications. Here, we report the isolation and characterization of four newly identified infecting tailed phages (W70, A7-1, A5-4, and A73) that were found to constitute a novel genus, , within the subfamily .

Copy Number Variation and Epilepsy: State of the Art in the Era of High-Throughput Sequencing-A Multicenter Cohort Study.

Background: Genetic epilepsy diagnosis is increasing due to technological advancements. Although the use of molecular diagnosis is increasing, chromosomal microarray analysis (CMA) remains an important diagnostic tool for many patients. We aim to explore the role and indications of CMA in epilepsy, given the current genomic advances.

Atrial Fibrillation and Mortality after Gastrointestinal Surgery: Insights from a Systematic Review and Meta-Analysis.

Background: Heart failure, stroke and death are major dangers associated with atrial fibrillation (AF), a common abnormal heart rhythm. Having a gastrointestinal (GI) procedure puts patients at risk for developing AF, especially after large abdominal surgery. Although earlier research has shown a possible connection between postoperative AF and higher mortality, the exact nature of this interaction is yet uncertain.

Spatio-Temporal Pattern of Tuberculosis Distribution in Romania and Particulate Matter Pollution Associated With Risk of Infection.

The study proposes a dynamic spatio-temporal profile of the distribution of tuberculosis incidence and air pollution in Romania, where this infectious disease induces more than 8,000 new cases annually. The descriptive analysis for the years 2012-2021 assumes an identification of the structuring patterns of mycobacterium tuberculosis risk in the Romanian population, according to gender and age, exploiting spatial modeling techniques of time series data. Through spatial autocorrelation, the degree of similarity between the analyzed territorial systems was highlighted and the relationships that are built between the analysis units in spatial proximity were investigated.