Daily Sepsis Research Analysis

Sepsis-induced liver injury is common, but the underlying mechanisms remain poorly understood. Given the critical role of gut-liver crosstalk in sepsis, we hypothesize that gut-trained neutrophils, migrating via the portal vein, release neutrophil extracellular traps (NETs) to activate Kupffer cells, thereby exacerbating hepatic injury during sepsis. Here we show that iNOS expression in Kupffer cells increases in septic wild type mice but decreases in PAD4

Bacterial sepsis is a life-threatening immune dysregulation triggered by bacterial infection and propagated by a dysfunctional host response, culminating in systemic tissue damage and multiorgan failure. In the United States, sepsis results in the hospitalization of more than one million patients annually and accounts for nearly one in three hospital deaths. Despite decades of efforts to develop immunoregulatory sepsis therapies, no clinically approved treatments exist. Recent advances in nanotechnology have introduced innovative approaches, including cellular nanodecoys synthesized from natural macrophage membranes coated onto polymeric nanoparticle cores. Here we introduce a human macrophage membrane-derived drug candidate, CTI-111, capable of sequestering soluble microbial toxins, drivers of inflammation, and pro-inflammatory cytokines from multiple sources. Therapeutic administration of CTI-111 reduces inflammation and improves survival in multiple murine sepsis models. We further demonstrate that CTI-111 can bind multiple sepsis-associated human cytokines in the complex environment of septic serum ex vivo. Together, these findings highlight the potential of CTI-111 as a multifaceted therapy for sepsis.

OBJECTIVE: To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a sepsis prediction model case study. MATERIALS AND METHODS: Retrospective patient data from electronic medical records of 7 public hospitals was used to retrain and evaluate a machine learning sepsis prediction model. Four conventional metrics-area under the receiver operating curve (AUROC), sensitivity, positive predictive value, and specificity-were compared with a novel graphical display integrating metrics of predictive accuracy (effectiveness), alert burden (efficiency) and lead time of alerts relative to clinical events (utility) for different alert thresholds. RESULTS: The dataset comprised 977,506 inpatient admissions. The novel methodology produced a plot of four vertically aligned graphs that enables decision-makers to identify an alert threshold that optimally balances effectiveness, efficiency and utility (EEU) at the level of an entire admission, and which differs from that derived using conventional metrics. DISCUSSION: Conventional evaluation metrics do not consider alert timing relative to clinical events and are often applied to different evaluation datasets (sample and admission level), introducing bias and confusion. In contrast, the EEU methodology (i) generates admission level evaluations at different alert thresholds; (ii) measures alert timing relative to clinical events; and (iii) provides a visual display that enables identification of the alert threshold that optimally balances EEU factors. CONCLUSION: Evaluations of prediction models for adverse events in hospitalised patients should incorporate the EEU approach in assessing model suitability and selecting alert thresholds.