AIMC Topic: Sepsis
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Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.
Scientific reports
Jul 1, 2025
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...
Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.
Pediatric emergency care
Jul 1, 2025
Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid...
Identification of key genes and development of an identifying machine learning model for sepsis.
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
Jun 30, 2025
OBJECTIVE AND DESIGN: This study aims to identify key genes of sepsis and construct a model for sepsis identification through integrated multi-organ single-cell RNA sequencing (scRNA-seq) and machine learning.
Comprehensive analysis and experimental validation of BST1 as a novel diagnostic biomarker for pediatric sepsis using multiple machine learning algorithms.
European journal of pediatrics
Jun 25, 2025
Bone marrow stromal cell antigen-1 (BST1) expression is elevated in a variety of human diseases, but its relationship with pediatric sepsis is unclear. This study aimed to investigate the expression of BST1 in pediatric sepsis patients and its value ...
Exploring the molecular mechanisms of lactylation-related biological functions and immune regulation in sepsis-associated acute kidney injury.
Clinical and experimental medicine
Jun 12, 2025
Lactylation, a novel post-translational modification, has been implicated in various pathophysiological processes; however, its role in sepsis-associated acute kidney injury (SA-AKI) remains unclear. This study aimed to investigate the expression pat...
Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.
Scientific reports
Jun 4, 2025
Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and early prediction of sAKI has an important role in timely intervention, ultimately improving the patients...
A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.
Renal failure
Jun 2, 2025
Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable outcomes. This research aimed to develop a machine learning-based model for early SAD prediction in ...
Discovery of CYP1A1 Inhibitors for Host-Directed Therapy against Sepsis.
Journal of medicinal chemistry
May 29, 2025
Bacterial sepsis remains a leading cause of death globally, exacerbated by the rise of multidrug resistance (MDR). Host-directed therapy (HDT) has emerged as a promising nonantibiotic approach to combat infections; thus, multiple HDT targets have bee...
Immunogenic cell death biomarkers for sepsis diagnosis and mechanism via integrated bioinformatics.
Scientific reports
May 27, 2025
Immunogenic cell death (ICD) has been implicated in sepsis, a condition with high mortality, through mechanisms involving endoplasmic reticulum stress and other pathophysiological pathways. This study aimed to identify and validate ICD-related biomar...
Analysis of the heterogeneous treatment effect of vasoactive drug dosage and time on hospital mortality across different sepsis phenotypes: a retrospective cohort study.
European journal of medical research
May 24, 2025
BACKGROUND: The heterogeneity of sepsis poses challenges for the individualized treatment of vasoactive drugs.
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