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Sepsis-induced immunosuppression, one of the factors contributing to mortality, is closely mediated by Myeloid-Derived Suppressor Cells (MDSCs). This review first outlines MDSC biology, describing their origin from pathological emergency myelopoiesis and classification into polymorphonuclear (PMN-MDSCs) and monocytic (M-MDSCs) subsets. We then discuss their dual role: an early, protective modulation of cytokine storm that later evolves into harmful immunosuppression associated with secondary infections and death. The underlying mechanisms involve L-arginine depletion (via ARG1/iNOS), oxidative stress (ROS), and induction of regulatory T cells (Tregs). Therapeutic strategies are summarized, including interventions targeting MDSC differentiation (e.g., ATRA), metabolism (e.g., FAO inhibitors), precision depletion (e.g., anti-LOX-1), and microbiome modulation. Finally, we address challenges to clinical translation-phenotypic heterogeneity, metabolic ambiguity, and the "double-edged sword" of MDSC targeting. Deeper insights into MDSC biology may help develop strategies to improve outcomes in this severe syndrome.

Purpose: Although Tetraspanin (TSPAN) plays a crucial physiological role in sepsis, the specific molecular mechanisms underlying its involvemenin sepsis pathophysiology remain incompletely elucidated. This study aimed to identify TSPAN-associated biomarkers in sepsis and clarify their potential molecular mechanisms.

Methods: Sepsis transcriptome data and TSPAN-related genes were from public databases. TSPAN-defined molecular subtypes were identified via consensus clustering. Candidate genes were screened by intersecting TSPAN-related differentially expressed genes (DEGs) with sepsis DEGs; biomarkers were determined using machine learning, ROC curves, and expression validation. Finally, GSEA, immune cell abundance analysis, and compound prediction explored their functions, and RT-qPCR validated prognostic gene expression.

Results: Eleven candidate genes were identified; XK, CA1, AHSP and GYPA were confirmed as biomarkers, significantly upregulated in sepsis vs. controls and enriched in pathways like "martens tretinoin response up". Neutrophil, activated dendritic cell, and macrophage abundance differed between groups and correlated with biomarkers. 52 potential therapeutics (including zanamivir) were screened via biomarker interaction analysis. RT-qPCR confirmed higher expression of the four biomarkers in sepsis.

Conclusion: In conclusion, XK, CA1, AHSP, and GYPA are TSPAN-associated biomarkers in sepsis and are significantly correlated with Type 17 T helper cells. The findings offered a promising theoretical foundation for the development of pharmaceuticals for sepsis.

Background: Sepsis-related coagulopathy is a major driver of organ failure and mortality, yet effective adjuncts remain limited. We therefore examined whether early furosemide use is associated with reduced coagulopathy and improved organ outcomes in real-world practice.

Methods: Using Taiwan's National Health Insurance Research Database (2000-2013), we assembled a population-based matched cohort of 9,934 adults with incident sepsis. Exposure was receipt of furosemide during the index hospitalization; comparators were other diuretics (e.g., acetazolamide, hydrochlorothiazide, spironolactone). The primary outcome was disseminated intravascular coagulation (DIC); secondary outcomes included transfusion, organ failures (cardiac, respiratory, renal, hepatic), and all-cause mortality. Multivariable Cox models estimated adjusted hazard ratios (aHRs).

Results: In the primary analysis (3-month follow-up), furosemide use was associated with a lower risk of DIC (aHR 0.196, 95% CI 0.067-0.516, P < 0.001) and transfusion. These associations remained durable in the exploratory 12-month analysis. Significant protective associations were also observed for cardiac, respiratory, and acute renal failure, alongside lower all-cause mortality. Crucially, active comparator analysis revealed that while associations with lower mortality were shared across diuretic classes, the protective associations with organ failure were specific to furosemide and not observed with other diuretics, supporting a distinct pharmacological effect beyond general diuresis.

Conclusion: Real-world evidence suggests that furosemide use is associated with reduced sepsis-related coagulopathy and favorable organ outcomes. These findings generate a biologically plausible hypothesis consistent with Na +-K +-2Cl - cotransporter 1 (NKCC1)-related antioxidant and anti-inflammatory pathways observed in preclinical models, warranting confirmation in prospective trials.

Background: Sepsis, involving systemic inflammation and organ failure, presents significant challenges due to its complex and heterogeneous immunological reactions. T-helper 17 (Th17) cells contribute significantly to immune regulation, and their dysregulation is implicated in sepsis pathogenesis. Understanding how Th17 cell differentiation-related genes contribute to sepsis heterogeneity and prognosis is crucial for improving patient prognosis.

Methods: We retrieved mRNA expression and clinical datasets (GSE65682, GSE95233, and GSE28750) from the Gene Expression Omnibus repository for subsequent analyses, and Th17 cell differentiation-related genes were obtained from the Molecular Signatures Database (MSigDB). We applied Weighted Gene Coexpression Network Analysis to identify sepsis-connected gene modules. Consensus clustering divided sepsis patients into subtypes. We established a prognostic model using Least Absolute Shrinkage and Selection Operator followed by multivariate Cox proportional hazards analysis. Immune infiltration, gene set enrichment analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were applied to characterize subtypes and risk groups.

Results: Weighted Gene Coexpression Network Analysis identified a sepsis-associated turquoise module. Our analysis revealed the existence of two distinct molecular categories within sepsis, named Cluster 1 and Cluster 2, with Cluster 2 showing significantly poorer survival. We formulated a reliable eight-gene prognostic model, including DENND2D, CD74, BCL11A, FCER1A, LTB, TGFBI, ERAP2, and VSIG4, that effectively categorized patients into high- and low-risk groups. The low-risk group exhibited higher immune cell infiltration and enrichment in immune-related pathways, while the high-risk group showed enrichment in metabolic and stress pathways.

Conclusions: Our investigation identified novel Th17 cell differentiation-connected molecular subtypes of sepsis and established a reliable prognostic model. These observations shed light on the molecular variability associated with sepsis and hold the prospect of personalized therapeutic strategies and improved patient management.

Appendicitis microbial analysis showed no major differences between appendix-rectal flora; however, Escherichia coli was statistically significantly more prevalent in patients with acute appendicitis compared with patients without acute appendicitis.

Trauma-induced coagulopathy represents a serious complication of hemorrhagic shock, associated with increased mortality within the first 6 hours post injury. Evidence of coagulopathy is often observable at the scene or upon hospital admission, emphasizing a critical role of prehospital care. Since studies on the use of various blood products in prehospital care have shown contradictory results, we aimed to investigate the effects of different volume substitutes on the initial immune response within the intravascular immune system. In an ex vivo model of polytrauma, blood from healthy volunteers was subjected to a trauma-characteristic shock scenario, including the addition of danger-associated molecular patterns, blood acidification, and volume resuscitation. Resuscitation strategies involved either sodium chloride, component therapy (a combination of fresh frozen plasma and red blood cells), or leukocyte-reduced fresh whole blood. Samples were analyzed following 60 minutes of incubation at 37°C to determine clinically relevant blood parameters and the activation status of both cellular innate leukocyte and humoral cascade responses. The use of blood products demonstrated superiority over sodium chloride, resulting in less disruption of the hemostatic system, as evidenced by reduced platelet activation. In parallel, neutrophil granulocytes were less affected, showing decreased surface activation marker expression and lower levels of soluble activation markers. Furthermore, activation of the complement system was reduced when the volume was resuscitated with blood products. Taken together, these data indicate that the choice of volume substitution greatly impacts the stability of the intravascular innate immune system, highlighting the potential benefit of leukocyte-reduced whole blood resuscitation.

Background: Sepsis-induced liver injury affects about 34.7% of patients and correlates with prognosis. Although vitamin A (VA) and retinol-binding protein 4 [RBP4] are reduced in sepsis, the protective role of VA remains unknown. This study aimed to explore the effects of VA and mechanisms in septic liver injury.

Methods: Cecal ligation puncture was performed in male C57BL/6 mice. After 24 hours, retinol (ROL, 0.01, 0.1, 1 mg/kg), RBP4 (0.5 mg/kg), or their combinations were administered. Liver/serum samples collected 24 hours later assessed injury (histopathology, function) and retinoic acid (RA)-inducible gene-I (RIG-I)/ receptor-interacting protein kinase (RIPK)1/RIPK3/mixed lineage kinase domain-like protein (MLKL) expression via immunohistochemistry, western blotting, and real-time quantitative polymerase chain reaction. In lipopolysaccharide (LPS)-stimulated alpha mouse liver-12 cells, ROL, RBP4, ROL+RBP4, or RA effects were evaluated.

Results: Cecal ligation puncture mice showed decreased serum ROL at 12 hours (lowest at 24 hours), while RBP4 transiently rose at 3 hours before declining (lowest at 24 hours). ROL (1 mg/kg) + RBP4 significantly improved survival, reduced liver injury (low Knodell scores), and suppressed RIG-I/TNF-α/RIPK1-RIPK3-MLKL necroptosis. LPS reduced RBP4 in alpha mouse liver-12 cells ( P < 0.001); ROL + RBP4 and RA alleviated damage via inhibiting necroptosis.

Conclusions: VA may exert hepatoprotection by suppressing RIG-I, reducing tumor necrosis factor-α, and inhibiting RIPK1/RIPK3/MLKL-mediated necroptosis.

Background: Sepsis in immunosuppressed patients is associated with significantly higher mortality rates, yet predictive models tailored to this high-risk population remain limited. This study aims to develop an interpretable machine learning model to predict 28-day mortality in immunosuppressed sepsis patients, with a focus on model transparency and clinical applicability.

Methods: A retrospective cohort study was conducted using clinical, laboratory, and demographic data from immunosuppressed sepsis patients. Feature selection was performed using LASSO regression, followed by the development of predictive models, including XGBoost. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUROC). To enhance clinical interpretability, Shapley additive explanations (SHAP) were employed to provide insights into the contribution of individual features to mortality predictions.

Results: The final model identified key predictors of 28-day mortality, including lactate levels, red cell distribution width, platelet count, and Sequential Organ Failure Assessment score. XGBoost demonstrated superior predictive accuracy with an AUROC of 0.93 (95% confidence interval: 0.90-0.96), outperforming other models. SHAP analysis revealed that elevated lactate levels and reduced platelet counts were strong risk factors for mortality, while lower lactate and higher platelet counts were protective. The model's interpretability provided clear insights into the role of each predictor, facilitating individualized risk stratification.

Conclusion: The XGBoost model, combined with SHAP analysis, offers an accurate and interpretable tool for predicting 28-day mortality in immunosuppressed sepsis patients. This approach enhances clinical decision-making by providing transparent insights into the factors driving mortality risk, thus supporting personalized and timely interventions aimed at improving patient outcomes.