Daily Sepsis Research Analysis

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host immune response to infection, and there is currently a lack of early rapid identification and effective treatment methods. During the pathogenesis of sepsis, immune cells such as monocytes exhibit abnormal activation of aerobic glycolysis. However, the mechanism of glycolysis in immune cells during sepsis remains to be elucidated. Here, we investigated the role of glycolysis-related regulatory genes in the development of sepsis. Through analysis of the GEO database, we found that HK3 is significantly elevated in the peripheral blood of sepsis patients. Receiver operating characteristic (ROC) curve analysis demonstrated that HK3, as a novel metabolic checkpoint, serves as an excellent diagnostic biomarker for sepsis. Immune cell infiltration analysis revealed a significant increase in monocyte infiltration in the peripheral blood of sepsis patients. Single-cell RNA sequencing analysis demonstrated a significant increase in HK3 expression in monocytes from the sepsis group compared to the control group. Using an LPS-induced monocyte sepsis model, we found that HK3 boosts glycolytic activity and lactate accumulation. Mechanistically, this enhances inflammatory cytokine secretion through H3K18 lactylation-dependent activation of IL-6 and TNF-α genes. Notably, targeted HK3 knockdown effectively suppressed this pro-inflammatory cascade, highlighting its critical role in sepsis pathogenesis. Our findings not only establish HK3 as a key metabolic regulator in sepsis but also elucidate its molecular mechanism in driving excessive monocyte-mediated inflammation. Moreover, we identify HK3 as a promising therapeutic target for mitigating hyperinflammatory responses in sepsis.

OBJECTIVE: To systematically review machine learning-based sepsis prediction studies, examining model explainability and the extent to which explanations reflect key sepsis biomarkers. DATA SOURCES: Following the PRISMA guidelines, we reviewed the titles, abstracts, and full texts. The search was conducted in four major bibliographic databases with publication dates from January 1, 2019 to July 16, 2025. STUDY SELECTION: The included studies provided a clear definition of sepsis based on the Sepsis-3 criteria and involved critically ill adult human subjects. DATA EXTRACTION AND SYNTHESIS: Two authors (IP and AKa) independently reviewed and assessed each study. Using statistical methods, we assessed study quality and explainability trends. RESULTS: A total of 37 studies were included. Our analysis revealed a notable temporal increase (≈67% greater odds per year) in the use of explainability methods in sepsis prediction models. However, key sepsis biomarkers (procalcitonin or C-reactive protein) were not among the top predictive features, highlighting a gap between the model output and known sepsis pathophysiology. DISCUSSION: Model attributions often mirror what electronic health records measure most consistently (vital signs) rather than what is most biologically specific, partly due to the high missingness and irregular sampling of CRP/PCT in public datasets. Heterogeneity in feature selection and reliance on local datasets limit generalizability, while sparse code/data sharing constrains reproducibility. CONCLUSION: This review newly quantifies the rise of explainability use in sepsis prediction and identifies a consistent gap between model explanations and key sepsis biomarkers, providing a foundation for future work to bridge data-driven insights with sepsis pathophysiology. SYSTEMATIC REVIEW REGISTRATION NUMBER: CRD420251101470.

INTRODUCTION: Propofol and midazolam are commonly used sedatives in patients with sepsis-associated acute kidney injury (S-AKI), yet the association of their combined use on patient prognosis remains unclear. This study aimed to compare the associations of propofol monotherapy, midazolam monotherapy, and their combination with 30-day mortality in S-AKI patients. METHODS: This study analyzed 3335 S-AKI patients from the Medical Information Mart for Intensive Care IV database, categorized by sedation strategy: no sedation, propofol alone, midazolam alone, or combination therapy. The primary outcome was 30-day all-cause mortality. Associations were assessed using Kaplan-Meier survival analysis, Cox proportional hazards models, and inverse probability of treatment weighting. Subgroup and mediation analyses were also performed. RESULTS: After multivariable adjustment, both midazolam monotherapy (hazard ratio [HR] = 1.945, 95% confidence interval: 1.519-2.490) and combination therapy (HR = 1.573, 95% confidence interval: 1.275-1.942) were associated with significantly increased 30-day mortality risk compared to propofol monotherapy. This finding was consistent after inverse probability of treatment weighting (HR = 1.742 and 1.328, respectively). Subgroup analyses generally supported this trend across different populations. Mediation analysis indicated that metabolic acidosis significantly mediated part of the increased mortality risk associated with midazolam (alone: 15.84%; combined: 10.21%). CONCLUSIONS: Propofol monotherapy was associated with more significant survival benefits compared to midazolam monotherapy or combination therapy. Metabolic acidosis is a key pathological mechanism mediating this difference, which has important guiding value for improving the prognosis of this high-risk population.