Daily Sepsis Research Analysis

BACKGROUND: Neutrophils play a key role in sepsis-associated acute kidney injury (SAKI), a common and life-threatening complication of organ failure. High mobility group box 1 (HMGB1) modulates inflammatory responses and the formation of neutrophil extracellular traps (NETs). The present work aimed to explore whether HMGB1 lactylation promotes NET formation and exacerbates SAKI. METHODS: Venous blood samples were collected from healthy volunteers and SAKI patients. A SAKI mouse model was established using the cecal ligation and puncture method. A coculture system of macrophage-derived exosomes and neutrophils was established. Macrophage-derived exosomes were isolated and identified. ELISAs, immunofluorescence staining, coimmunoprecipitation, and Western blotting were utilized to determine protein levels. RESULTS: Elevated blood lactate levels were associated with increased HMGB1 levels in patients with SAKI. In mouse models, lactate increased HMGB1 expression, promoted NET formation, and exacerbated SAKI. Lactate stimulated M1 macrophages to secrete exosomes, leading to the accumulation and release of HMGB1 in the cytoplasm. Additionally, lactate promoted HMGB1 lactylation in macrophages, triggering the release of mitochondrial DNA from neutrophils and activating the cyclic GMP‒AMP synthase/stimulator of interferon genes pathway. CONCLUSION: This study revealed that lactate-induced HMGB1 lactylation in macrophages plays a role in promoting NET formation in SAKI through the cGAS/STING pathway. These findings suggest that HMGB1 could be a potential target for therapeutic intervention in SAKI.

Sepsis diagnosis in preterm neonates is challenging due to symptom overlap with non-infectious inflammatory conditions, and slow, unreliable diagnostic practices. This case-control study aims to elucidate sepsis pathophysiology, and identify metabolic biomarkers for timely, accurate diagnosis, to prevent rapid health deterioration and unnecessary antibiotic use. Liquid chromatography-mass spectrometry was performed on 227 plasma samples, obtained from 94 preterm neonates, to measure 317 metabolites encompassing amines and signaling lipids. Linear mixed-effect modeling, LASSO and logistic regression models were calculated to assess metabolic alterations across control, systemic inflammation-no sepsis (SINS), and sepsis groups. Stratification by sex and pathogen type allowed identification of sex-specific responses and pathogen-driven variations in sepsis. Key findings include (i) shared metabolic changes in SINS and sepsis, (ii) progressive alterations from control to SINS to sepsis, and (iii) sepsis-specific markers. Males exhibited a pro-inflammatory phenotype while females showed an anti-inflammatory phenotype in response to sepsis. Gram-positive and gram-negative bacterial sepsis revealed distinct metabolic profiles. A diagnostic model comprising 5 metabolic features and IL-6 distinguished SINS from sepsis at clinical suspicion (AUC 0.79, sensitivity 0.85, specificity 0.82). These insights highlight the potential of metabolomics to revolutionize neonatal sepsis management with precision diagnostics and personalized treatment strategies.

BACKGROUND: Patients with septic shock frequently require tracheal intubation in the emergency department (ED). However, the criteria for tracheal intubation are subjective, based on physician experience, or require serial evaluations over relatively long intervals to make accurate predictions, which might not be feasible in the ED. We used supervised learning approaches and features routinely available during the initial stages of evaluation and resuscitation to stratify the risks of tracheal intubation within a 24-hour time window. METHODS: We retrospectively analyzed the data of patients diagnosed with septic shock based on the SEPSIS-3 criteria across 21 university hospital EDs in the Republic of Korea. A principal component analysis revealed a complex, non-linear decision boundary with respect to the application of tracheal intubation within a 24-hour time window. Stratified five-fold cross validation and a grid search were used with extreme gradient boost. Shapley values were calculated to explain feature importance and preferences. RESULTS: In total, data for 4,762 patients were analyzed; within that population, 1,486 (31%) were intubated within a 24-hour window, and 3,276 (69%) were not. The area under the receiver operating characteristic curve and F1 scores for intubation within a 24-hour window were 0.829 (95% CI, 0.801-0.878) and 0.654 (95% CI, 0.627-0.681), respectively. The Shapley values identified lactate level after initial fluids, suspected lung infection, initial pH, Sequential Organ Failure Assessment score at enrollment, and respiratory rate at enrollment as important features for prediction. CONCLUSIONS: An extreme gradient boosting machine can moderately discriminate whether intubation is warranted within 24 hours of the recognition of septic shock in the ED.