Ethyl pyruvate ameliorates heat stroke-induced multiple organ dysfunction and inflammatory responses by induction of stress proteins and activation of autophagy in rats

Objective: Heat stroke (HS) elicits the systemic inflammatory responses that result in multiple organ dysfunction (MOD). Heat shock response and autophagy are activated during heat stress for removal of damaged organelles and proteins, emerging as a major regulator of cellular homeostasis. Ethyl pyruvate (EP) is a derivative of pyruvic acid and possesses antioxidant and anti-inflammatory effects. This study aims to investigate the effects of EP on MOD in HS rats and explore the possible mechanisms. Method: Anesthetized rats were placed in a heating chamber (42 °C) to elevate the core body temperature attaining to 42.9 °C. Rats were then moved to room temperature and monitored for 6 h. EP (60 mg/kg, i.v.) was administered 30 min prior to heat exposure. Results: Results showed that EP significantly reduced HS-induced increases in plasma levels of LDH, CPK, GPT and CK-MB, reversed the decrease of platelet counts, and alleviated intestinal mucosal and pulmonary damage. Moreover, EP reduced pro-inflammatory protein, including TNF-α, IL-6, IL-1β, HMGB1 and iNOS, and induced stress proteins, heme oxygenase-1 (HO-1), heat shock protein (HSP) 70 and HSP90 in the liver of HS rats. The levels of HS-activated autophagy-regulatory proteins were affected by EP, in which the phosphorylated mTOR and AKT were reduced, and the phosphorylated AMPK increased, accompanied with upregulation in ULK1, Atg7, Atg12 and LC3II, and downregulation of p62. Conclusion: In conclusion, EP ameliorated HS-induced inflammatory responses and MOD, and the underlying mechanism is associated with the induction of the stress proteins HO-1 and HSP70 as well as restorage of autophagy

Clinical and prognostic correlates of ST-elevation myocardial infarction patients with normal coronary angiography

Background: Revascularization within a 90-min door-to-balloon time is a strict policy enacted in Taiwan. Prompt diagnosis is critical to avoid an unnecessary procedure and catheterization laboratory activation. This study was aimed to investigate the clinical and prognostic characteristics of the patients with ST-elevation myocardial infarction (STEMI) referred for primary percutaneous coronary intervention (PCI) and normal coronary arteries found following coronary angiography (CAG). Materials and Methods: From October 2009 to December 2012, 216 consecutive patients with STEMI referred for primary PCI were enrolled. The data of clinical history, physical examination, laboratory results, electrocardiography, echocardiography, CAG findings, diagnosis, and outcomes were collected and analyzed. Results: A total of 17 patients were proved normal coronaries angiographically. The incidence of the conditions mimicking as STEMI is 7.9%. Alternative diagnosis was coronary spasm (n = 7), peri-myocarditis (n = 6), apical ballooning syndrome (n = 3), anaphylactic shock (n = 1). Compared with STEMI group, patients in normal coronaries group were younger, with a less premature family history of coronary artery disease (CAD), and reported angina. The 30-day mortality rate in the normal coronaries group was 5.9%. Conclusions: Cautiously evaluating CAD risk factors and symptoms of angina and awareness of alternative diagnosis are important to make a prompt diagnosis without compromising accuracy in the patients presenting as suspected STEMI

The TVGH-NYCU Thal-Classifier: Development of a Machine-Learning Classifier for Differentiating Thalassemia and Non-Thalassemia Patients

Thalassemia and iron deficiency are the most common etiologies for microcytic anemia and there are indices discriminating both from common laboratory simple automatic counters. In this study a new classifier for discriminating thalassemia and non-thalassemia microcytic anemia was generated via combination of exciting indices with machine-learning techniques. A total of 350 Taiwanese adult patients whose anemia diagnosis, complete blood cell counts, and hemoglobin gene profiles were retrospectively reviewed. Thirteen prior established indices were applied to current cohort and the sensitivity, specificity, positive and negative predictive values were calculated. A support vector machine (SVM) with Monte-Carlo cross-validation procedure was adopted to generate the classifier. The performance of our classifier was compared with original indices by calculating the average classification error rate and area under the curve (AUC) for the sampled datasets. The performance of this SVM model showed average AUC of 0.76 and average error rate of 0.26, which surpassed all other indices. In conclusion, we developed a convenient tool for primary-care physicians when deferential diagnosis contains thalassemia for the Taiwanese adult population. This approach needs to be validated in other studies or bigger database