Shikonin (SHI) is an anti-inflammatory agent extracted from natural herbs. It is still unknown whether SHI ameliorates lipopolysaccharide (LPS)-induced cardiac dysfunction. This study aims to explore the protective effects of SHI on LPS-induced myocardial injury and its mechanism. The LPS-induced cardiac dysfunction mouse model was employed to investigate the protective effects of SHI. In the present study, we found that SHI treatment improved the survival rate and cardiac function and remarkably ameliorated the release of inflammatory cytokines and macrophage infiltration in heart tissue of LPS-treated mice. SHI also reduced lactate dehydrogenase (LDH) and cardiac troponin (cTn) release, cell inflammation, and apoptosis in LPS plus adenosine triphosphate (ATP)-treated H9c2 cells. In addition, SHI significantly upregulated silent information regulator 1 (SIRT1) expression and suppressed the upregulation of NOD-like receptor protein 3 (NLRP3), cleaved caspase-1, and caspase-1 activity in heart tissues induced by LPS. Meanwhile, we got the same results in LPS plus ATP-treated H9c2 cells in vitro. Further, SIRT1 inhibitor or siRNA partially blocked SHI-mediated upregulation of SIRT1 expression and downregulation of NLRP3, cleaved caspase-1, and caspase-1 activity in heart tissues induced by LPS. Therefore, we conclude that SHI ameliorates LPS-induced cardiac dysfunction by inhibiting SIRT1-dependent activation of NLRP3 inflammasomes and might be a promising therapeutic strategy for the treatment of LPS-induced cardiac dysfunction.

Background. Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in‐hospital mortality in AP. Methods. Patients with AP were identified from the Medical Information Mart for Intensive Care‐III (MIMIC‐III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results. A total of 337 patients with AP were analyzed in the study, and the in‐hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in‐hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion. An ANN predictive model for in‐hospital mortality in patients with AP in MIMIC‐III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.

Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection.Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model.Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860–0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

Serum lactate dehydrogenase level may predict acute respiratory distress syndrome of patients with fever infected by SARS-CoV-2

Background: Although studies have shown that waist circumference (WC) is positively associated with an increased risk of cardiovascular diseases among the normal population, few studies have investigated WC in patients with type-2 diabetes mellitus (T2DM). Methods: This was a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. The Cox proportional hazards models was used to investigate the relationship between WC and major adverse cardiovascular events (MACEs) in T2DM patients with cardiovascular disease (CVD) or high risk factors of CVD. Results: A total of 10,251 T2DM patients (6299 men [61.4%], 3952 women [38.6%]) were included in our analysis. The mean age was 64.0 ± 7.53 years. After a mean follow-up at 9.2 ± 2.4 years later, 1804 patients (event rate of 23 per 1000 person-years) had developed MACEs. MACEs rates in men and women were 18.0 and 26.0 events per 1000 person-years, respectively. After multivariable adjustment, each increase in WC of 1 SD increased the risk of MACEs (HR: 1.10, 95% CI 1.04–1.17; P < 0.01) in men, with a non-significant increase in MACEs (HR: 1.04, 95% CI 0.95–1.13; P = 0.40) in women. Compared with those in the first quartile of WC, male patients in the fourth quartile of WC had a hazard ratio (HR) of 1.24 (95% CI 1.05–1.46) for MACEs; female patients in the fourth quartile of WC had an HR of 1.22 (95% CI 0.96–1.56) for MACEs. Conclusions: Higher WC is associated with increased risks of MACEs in male but not female T2DM patients. Trial registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT00000620)

Background. Evidence regarding the relationship between serum lactate dehydrogenase (LDH) levels and in-hospital mortality in acute aortic dissection (AAD) patients is extremely limited. We aimed to investigate the relationship between LDH and in-hospital mortality in AAD patients. Methods. The present study was a retrospective observational study. A total of 1526 participants with acute aortic dissection were involved in a hospital in China from January 2014 to December 2018. The target-independent variable was LDH measured at baseline, and the dependent was all-cause mortality during hospitalization. Covariates involved in this study included age, gender, body mass index (BMI), hypertension, diabetes, smoking, stroke, atherosclerosis, systolic blood pressure (SBP), diastolic blood pressure (DBP), white blood cell (WBC), hemoglobin (Hb), alanine transaminase (ALT), aspartate aminotransferase (AST), albumin (ALB), creatinine (Cr), symptom, type of AAD (Stanford), and management. Results. The average age of 1526 selected participants was 52.72 ± 11.94 years old, and about 80.41% of them were male. The result of the fully adjusted model showed LDH was positively associated with in-hospital mortality in AAD patients after adjusting confounders (OR = 1.09, 95% CI 1.05 to 1.13). A nonlinear relationship was detected between LDH and in-hospital mortality in AAD patients after adjusting for potential confounders (age, gender, BMI, hypertension, diabetes, stroke, atherosclerosis, smoking, symptom, SBP, DBP, WBC, Hb, ALT, AST, ALB, Cr, type of AAD (Stanford), and management), whose point was 557. The effect sizes and the confidence intervals of the left and right sides of the inflection point were 0.90 (0.74–1.10) and 1.12 (1.06–1.19), respectively. Subgroup analysis in participants showed that the relationship between LDH and in-hospital mortality was stable, and all of the P value for the interaction in different subgroup were more than 0.05. Conclusions. The relationship between LDH and in-hospital mortality in AAD patients is nonlinear. LDH was positively related with in-hospital mortality when LDH is more than 557.