Myristica fragrans Extract Inhibits Platelet Desialylation and Activation to Ameliorate Sepsis-Associated Thrombocytopenia in a Murine CLP-Induced Sepsis Model.

Sepsis, characterized by an uncontrolled host inflammatory response to infections, remains a leading cause of death in critically ill patients worldwide. Sepsis-associated thrombocytopenia (SAT), a common disease in patients with sepsis, is an indicator of disease severity. Therefore, alleviating SAT is an important aspect of sepsis treatment; however, platelet transfusion is the only available treatment strategy for SAT. The pathogenesis of SAT involves increased platelet desialylation and activation. In this study, we investigated the effects of Myristica fragrans ethanol extract (MF) on sepsis and SAT. Desialylation and activation of platelets treated with sialidase and adenosine diphosphate (platelet agonist) were assessed using flow cytometry. The extract inhibited platelet desialylation and activation via inhibiting bacterial sialidase activity in washed platelets. Moreover, MF improved survival and reduced organ damage and inflammation in a mouse model of cecal ligation and puncture (CLP)-induced sepsis. It also prevented platelet desialylation and activation via inhibiting circulating sialidase activity, while maintaining platelet count. Inhibition of platelet desialylation reduces hepatic Ashwell-Morell receptor-mediated platelet clearance, thereby reducing hepatic JAK2/STAT3 phosphorylation and thrombopoietin mRNA expression. This study lays a foundation for the development of plant-derived therapeutics for sepsis and SAT and provides insights into sialidase-inhibition-based sepsis treatment strategies.

Prediction Models for Sepsis-Associated Thrombocytopenia Risk in Intensive Care Units Based on a Machine Learning Algorithm.

Sepsis-associated thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU), which significantly increases the mortality rate and leads to poor prognosis of diseases. Machine learning (ML) is widely used in disease prediction in critically ill patients. Here, we aimed to establish prediction models for platelet decrease and severe platelet decrease in ICU patients with sepsis based on four common ML algorithms and identify the best prediction model. The research subjects were 1,455 ICU sepsis patients admitted to Dongyang People's Hospital affiliated with Wenzhou Medical University from January 1, 2015, to October 31, 2019. Basic clinical demographic information, biochemical indicators, and clinical outcomes were recorded. The prediction models were based on four ML algorithms: random forest, neural network, gradient boosting machine, and Bayesian algorithms. Thrombocytopenia was found to occur in 732 patients (49.7%). The mechanical ventilation time and length of ICU stay were longer, and the mortality rate was higher for the thrombocytopenia group than for the non-thrombocytopenia group. The models were validated on an online international database (Medical Information Mart for Intensive Care III). The areas under the receiver operating characteristic curves (AUCs) of the four models for the prediction of thrombocytopenia were between 0.54 and 0.72. The AUCs of the models for the prediction of severe thrombocytopenia were between 0.70 and 0.77. The neural network and gradient boosting machine models effectively predicted the occurrence of SAT, and the Bayesian models had the best performance in predicting severe thrombocytopenia. Therefore, these models can be used to identify such high-risk patients at an early stage and guide individualized clinical treatment, to improve the prognosis of diseases.