High Interleukin-10 Expression in Type 2 T Helper Cells in Septic Patients

Interleukin (IL)-10 response is associated with mortality in patients with sepsis. IL-10 is primarily produced by monocytes and type 2 T helper (Th2) cells. The aim of this study was to investigate differences in IL-10 production between monocytes and Th2 cells in patients with sepsis. Forty patients with sepsis and 35 healthy controls were enrolled. Cytokine expressions in peripheral blood mononuclear cells (PBMCs) were measured by flow cytometry. The IL-10 expression in the Th2 cells of the septic patients was higher than in the healthy controls, but the expression of IL-10 in the monocytes of the septic patients was lower than in the healthy controls. After regression analysis, IL-10 expression in Th2 cells was positively associated with sepsis, but IL-10 expression in monocytes was not associated with sepsis or shock. In conclusion, the production of IL-10 in Th2 cells was higher in the patients with sepsis.     

Correlation of transcutaneous hepatic near-infrared spectroscopy readings with liver surface readings and perfusion parameters in a piglet endotoxemic shock model.

PURPOSE: To determine whether transcutaneous liver near-infrared spectrophotometry (NIRS) measurements correlate with NIRS measurements taken directly from the liver surface, and invasive blood flow measurements. PROCEDURE: Laparotomy was performed in 12 Yorkshire piglets, and ultrasound blood flow probes were placed on the hepatic artery and portal vein. Intravascular catheters were inserted into the hepatic and portal veins for intermittent blood sampling, and a pulmonary artery catheter was inserted via the jugular vein for cardiac output measurements. NIRS optodes were placed on skin overlying the liver and directly across the right hepatic lobe. Endotoxemic shock was induced by continuous infusion of Escherichia coli lipopolysaccharide O55:B5. Pearson's correlations were calculated between the NIRS readings and the perfusion parameters. FINDINGS: After endotoxemic shock induction, liver blood flow, and oxygen delivery decreased significantly. There were statistically significant correlations between the transcutaneous and liver-surface NIRS readings for oxyhemoglobin, deoxyhemoglobin, and cytochrome c oxidase concentrations. There were similar significant correlations of the transcutaneous oxyhemoglobin with both the mixed venous and hepatic vein saturation, and mixed venous and hepatic vein lactate. CONCLUSIONS: Transcutaneous NIRS readings of the liver, in an endotoxemic shock model, correlate with NIRS readings taking directly from the liver surface, as well as with global and specific organ-perfusion parameters.     

G-CSF stimulated donor granulocyte collections for prophylaxis and therapy of neutropenic sepsis

Background: The administration of granulocyte colony-stimulating factor (G-CSF) increases the granulocyte count in normal donors and enables the collection of large numbers of mature myeloid cells by leukapheresis. This has potential value in the treatment of sepsis unresponsive to antibiotics in patients with severe neutropenia. Aim: To evaluate the tolerability of granulocyte collections in normal donors receiving G-CSF, the optimal method of collection and the clinical factors influencing the efficacy of granulocyte infusions. Methods: Analysis of the outcome of 55 granulocyte collections from 26 donors for progressive bacterial or fungal sepsis in neutropenic patients (n-8) or as prophylaxis in patients with recent fungal infections undergoing allogeneic bone marrow transplantation (BMT) (n=3). Results: G-CSF was well tolerated in most donors. Fatigue occurred commonly after the second collection. The median WCC per 200–220 mL bag was 351X10⁹7L. Collections were optimised with the use of a sedimenting agent (dextran) and a deepened interface setting on the cell separator. There was only a weak correlation between the number of granulocytes infused and the increment in the patient, but levels were usually maintained 0.5X 10⁹7L for the next 24 hours. The infusions were successful in three septic patients without multi-organ dysfunction and prophylactically, in two patients with localised fungal infections undergoing MBT The infusions were not beneficial in patients with septicaemia and established organ dysfunction or with extensive pulmonary aspergillosis. Conclusions: G-CSF mobilised granulocyte collections are feasible and the preliminary evidence suggests that the infusion of these cells may be useful early in the prophylaxis or treatment of severe neutropenic sepsis.     

降钙素原、血清淀粉样蛋白及超敏C-反应蛋白水平检验在诊断早期脓毒血症的临床价值

目的：探析降钙素原、血清淀粉样蛋白及超敏C-反应蛋白水平检验在早期脓毒血症临床诊断中的应用价值。方法选择该院2010年1月—2014年8月期间所收治的96例脓毒血症患者进行研究,根据是否为细菌感染分为A、B两组,所有患者均于清晨采集2 mL外周静脉血后测定PCT、hs-CRP、SAA水平,比较两组PCT、hs-CRP、SAA水平及三种检测方法敏感性、特异性。结果A、B两组PCT、hs-CRP、SAA水平均存在较大差异(<0.05),差异有统计学意义。A、B两组中PCT检测敏感性均最高,与hs-CRP、SAA的两两比较均存在显著差异(<0.05),差异有统计学意义。结论与hs-CRP、SAA相比,早期脓毒血症患者的血清PCT水平检测具有更高敏感性与特异性,更利于脓毒血症早期确诊,进而提供治疗指导,临床价值较高,值得借鉴与应用。     

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.