Blocking CXCR1/2 contributes to amelioration of lipopolysaccharide-induced sepsis by downregulating substance P.

OBJECTIVES: C-X-C chemokine receptor types 1/2 (CXCR1/2) is known to be activated in liver damage in acute-on-chronic liver failure; however, the role in lipopolysaccharide (LPS)-induced sepsis is unknown. The current study was designed to determine whether or not CXCR1/2 blockade with reparixin ameliorates acute lung injury (ALI) by affecting neuropeptides in a LPS-induced sepsis mouse model. MATERIALS AND METHODS: Male C57BL/6 mice (10 to 14-week old) were divided into sham, LPS, sham-R, and LPS-R groups. Bronchoalveolar lavage fluid (BALF) was collected and evaluated. The lung histopathology was assessed by immunocytochemistry staining. Western blot analysis was used to measure myeloperoxidase, substance P (SP), and vasoactive intestinal peptide. RESULTS: LPS-induced animal models were ameliorated by cotreatment with a CXCR1/2 antagonist. Moreover, the protective effects of CXCR1/2 antagonists were attributed to the increased secretion of pro-opiomelanocortin and decreased the secretion of SP. Reparixin decreased the expression of necroptosis cell death markers induced by LPS. CONCLUSION: The results of this study indicate that blockade of CXCR1/2 may represent a promising therapeutic strategy for the treatment of sepsis-associated ALI through regulation of neuropeptides and necroptosis.

Risk factors for postoperative delirium in ICU patients with severe illness based on systematic review and meta-analysis.

BACKGROUND: Meta-analysis was performed on risk factors for postoperative delirium in intensive care unit (ICU) patients to provide theoretical guidance for the prevention of postoperative delirium in ICU patients. METHODS: We conducted a search of Chinese databases using a combination of "meta-analysis", "risk factors for delirium", and "ICU patients with severe illness". "Meta analysis", "Risk factors of delirium", and "ICU severe patients" were used as search terms for English databases. The quality of the literature was evaluated using RevMan 5.3 software for Cochrane reviews. RESULTS: Ten literatures were included, and funnel plots were drawn, most of which were asymmetric and might have publication bias. However, the experimental results of each risk factor were relatively stable, so the experimental conclusions were relatively reliable. Of the 10 studies, there were 7 literatures on age factor, 95% confidence interval (CI): 2.29-9.01. There were 9 studies on gender factors, 95% CI: 0.73-1.40. There were 3 studies on drinking factors, 95% CI: -0.04 to 0.08. There were 3 studies on Acute Physiology and Chronic Health Evaluation-II (APACHE- II) scoring factors, 95% CI: 4.54-5.15. There were 4 studies on mechanical ventilation factors, 95% CI: 3.24-11.16. There were 3 studies on mechanical ventilation time factors, 95% CI: -39.92 to 154.97. There were 3 studies on sedative factors, 95% CI: 0.23-15.50. DISCUSSION: Different risk factors can influence the incidence of postoperative delirium in ICU patients with severe illness, which provides theoretical guidance for clinical prevention of delirium incidence.

Appraising the Causal Association between Systemic Iron Status and Heart Failure Risk: A Mendelian Randomisation Study.

Although observational studies have shown that abnormal systemic iron status is associated with an increased risk of heart failure (HF), it remains unclear whether this relationship represents true causality. We aimed to explore the causal relationship between iron status and HF risk. Two-sample Mendelian randomisation (MR) was applied to obtain a causal estimate. Genetic summary statistical data for the associations (p < 5 × 10−8) between single nucleotide polymorphisms (SNPs) and four iron status parameters were obtained from the Genetics of Iron Status Consortium in genome-wide association studies involving 48,972 subjects. Statistical data on the association of SNPs with HF were extracted from the UK biobank consortium (including 1088 HF cases and 360,106 controls). The results were further tested using MR based on the Bayesian model averaging (MR-BMA) and multivariate MR (MVMR). Of the twelve SNPs considered to be valid instrumental variables, three SNPs (rs1800562, rs855791, and rs1799945) were associated with all four iron biomarkers. Genetically predicted iron status biomarkers were not causally associated with HF risk (all p > 0.05). Sensitivity analysis did not show evidence of potential heterogeneity and horizontal pleiotropy. Convincing evidence to support a causal relationship between iron status and HF risk was not found. The strong relationship between abnormal iron status and HF risk may be explained by an indirect mechanism.

Exploring the Causal Effects of Circulating ST2 and Galectin-3 on Heart Failure Risk: A Mendelian Randomization Study.

Background: Heart failure (HF), primarily caused by conditions such as coronary heart disease or cardiomyopathy, is a global health problem with poor prognosis and heavy burden on healthcare systems. As biomarkers of myocardial injury and fibrosis, suppression of tumorigenicity 2 (ST2) and galectin-3 were recommended for prognosis stratification in HF guidelines. However, the causality between these two mediators and HF remains obscure. This study aimed to explore the causal relationship of genetically determined ST2 and galectin-3 with the risk of HF. Methods: We used the two-sample Mendelian randomization (MR) method, incorporating available genome-wide association summary statistics, to investigate the causal association of ST2 and galectin-3 with HF risk. We applied inverse-variance weighted analysis as the main method of analysis. Results: In our final MR analysis, 4 single-nucleotide polymorphisms (SNPs) of ST2 and galectin-3, respectively, were identified as valid instrumental variables. Fixed-effect inverse variance weighted (IVW) analysis indicated that genetically predicted ST2 and galectin-3 were not causally associated with HF risk 3. [odds ratio (OR) = 0.9999, 95% confidence interval [CI] = 0.9994-1.0004, p = 0.73; OR = 1.0002, 95% CI = 0.9994-1.0010, p = 0.60, respectively]. These findings were robust in sensitivity analyses, including MR-Egger regression and leave-one-out analysis. Conclusion: This MR study provided no evidence for the causal effects of ST2 and galectin-3 on HF risk.

Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.

Background: Early prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models. Methods: CCU patients' data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days-1 year, 1-5 years, and ≥5 years. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the curve (AUC) of the receiver operating characteristic curves. Subsequently, Local Interpretable Model-Agnostic Explanations method was performed to improve XGBoost model interpretability. We also constructed sub-models of each model based on the different categories of death time and compared the differences by decision curve analysis. The optimal model was further analyzed using a clinical impact curve. At last, feature ablation curves of the XGBoost model were conducted to obtain the simplified model. Results: Overall, 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model showed better accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841, and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). In subgroup analysis, the XGBoost model had a better predictive performance in acute myocardial infarction subgroup. The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different categories of patients. Finally, we obtained a simplified model with thirty features. Conclusions: For CCU physicians, the ML technique by XGBoost is a potential predictive tool in patients with different conditions, and it may contribute to improvements in prognosis.