Daily Sepsis Research Analysis
Analyzed 14 papers and selected 3 impactful papers.
Summary
Analyzed 14 papers and selected 3 impactful articles.
Selected Articles
1. Hexokinase 3 promoted cytokine production of monocytes by targeting metabolic reprogramming and histone lactylation in sepsis.
This study identifies HK3 as a metabolic checkpoint in sepsis monocytes, linking enhanced glycolysis and lactate accumulation to H3K18 lactylation and transcriptional activation of IL-6 and TNF-α. Multi-omic analyses and functional knockdown demonstrate HK3’s diagnostic potential and therapeutic relevance in curbing hyperinflammation.
Impact: It uncovers a novel metabolic-epigenetic mechanism driving monocyte hyperinflammation in sepsis and positions HK3 as both a biomarker and targetable node.
Clinical Implications: While preclinical, these findings suggest prioritizing translational studies of HK3 inhibitors or lactylation modulators and evaluating HK3 as a rapid blood-based biomarker for early sepsis recognition.
Key Findings
- HK3 expression is significantly elevated in sepsis peripheral blood and shows strong diagnostic performance by ROC analysis.
- Single-cell RNA-seq localizes HK3 upregulation to monocytes, alongside increased monocyte infiltration.
- HK3 enhances glycolysis and lactate accumulation, promoting IL-6 and TNF-α expression via H3K18 lactylation.
- HK3 knockdown suppresses the pro-inflammatory cascade, indicating therapeutic potential.
Methodological Strengths
- Integrated multi-omic approach (GEO bulk data, immune infiltration, single-cell RNA-seq) plus functional validation.
- Mechanistic linkage demonstrated between metabolism (glycolysis/lactate) and epigenetic regulation (H3K18 lactylation).
Limitations
- Findings are primarily from in vitro LPS-stimulated monocytes without in vivo sepsis validation.
- Clinical thresholds and external validation cohorts for the HK3 biomarker are not reported in the abstract.
Future Directions: Validate HK3’s diagnostic and therapeutic utility in prospective human cohorts and animal sepsis models; develop and test HK3 inhibitors or lactylation-modulating strategies.
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.
2. Machine learning for early detection and prediction of sepsis: explainability and key sepsis biomarkers representation-A systematic review.
This registered PRISMA-guided review of 37 studies shows a marked annual increase in explainability methods for sepsis prediction but finds that CRP and procalcitonin rarely rank among top features. The work highlights gaps between data-driven explanations and sepsis biology, limited generalizability due to local datasets, and poor reproducibility from sparse code/data sharing.
Impact: By quantifying explainability trends and exposing biomarker underrepresentation, this review sets an agenda to align ML predictions with sepsis pathophysiology and improve reproducibility.
Clinical Implications: Encourages inclusion and standardized collection of CRP/PCT and other biologically specific markers in EHRs and model pipelines, with external validation and transparent code/data sharing to support deployment.
Key Findings
- Explainability method use increased substantially over time (≈67% greater odds per year) in sepsis prediction models.
- CRP and procalcitonin were rarely among top predictive features, revealing a disconnect between model outputs and sepsis biology.
- Generalizability and reproducibility were limited by heterogeneous features, local datasets, and sparse code/data sharing.
Methodological Strengths
- Registered, PRISMA-guided systematic approach with dual independent reviewers.
- Quantitative assessment of temporal trends in explainability across included studies.
Limitations
- Relies on published studies with variable quality and heterogeneity; limited ability to perform meta-analysis across disparate features.
- Public datasets’ missingness patterns may bias conclusions about biomarker feature importance.
Future Directions: Create multicenter datasets with standardized CRP/PCT sampling, share code/data for reproducibility, and design hybrid models that integrate physiologic time series with biologically specific biomarkers.
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.
3. Propofol versus Midazolam With 30-Day Mortality in Sepsis-Associated Acute Kidney Injury: A MIMIC-IV Analysis.
In a retrospective MIMIC-IV cohort of 3335 S-AKI patients, propofol monotherapy was associated with lower 30-day mortality than midazolam or combination therapy. Mediation analyses implicated metabolic acidosis as a key pathway underlying the observed differences.
Impact: This large database study provides actionable comparative effectiveness signals for sedation in S-AKI and a plausible mechanistic mediator, informing trial design and bedside choices.
Clinical Implications: When feasible, favor propofol monotherapy over midazolam for sedation in S-AKI and monitor/treat metabolic acidosis aggressively; prioritize RCTs to confirm causality.
Key Findings
- In adjusted analyses, midazolam monotherapy (HR 1.945, 95% CI 1.519-2.490) and combination therapy (HR 1.573, 95% CI 1.275-1.942) were associated with higher 30-day mortality versus propofol monotherapy.
- Inverse probability of treatment weighting confirmed the pattern (HR 1.742 for midazolam; 1.328 for combination vs propofol).
- Mediation analysis showed metabolic acidosis mediated 15.84% (midazolam alone) and 10.21% (combination) of excess mortality risk.
Methodological Strengths
- Large retrospective cohort (n=3335) with multivariable Cox models, IPTW, and subgroup analyses.
- Mediation analysis providing mechanistic insight into metabolic acidosis as a pathway.
Limitations
- Observational design with potential residual confounding and inability to infer causality.
- Unmeasured factors (e.g., sedation depth, dosing, illness trajectory) may bias associations.
Future Directions: Conduct randomized trials comparing sedatives in S-AKI and mechanistic studies on acid-base effects of benzodiazepines versus propofol; incorporate sedation depth and dosing metrics.
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.