An open access database for the evaluation of heart sound algorithms.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Influence of nandrolone decanoate administration on serum lipids and liver enzymes in rats.

BACKGROUND: Anabolic-androgenic steroids have been associated with several side effects range. This experimental study was conducted to evaluate the effects of nandrolone decanoate (ND, an anabolic steroid) on lipid profile and liver enzymes in rats in Iran. METHODS: Forty adult male and female of Wistar strain rats were randomly assigned to four groups of 10 animals each: male control, female control, ND-male treated (15 mg/kg b.w./day), and ND-female treated (15 mg/kg b.w./day). Serum concentrations of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were measured in all studied groups. RESULTS: Treating rats with ND (case group) resulted in a significant elevation of TC (69.4 ± 8.7), TG (101.6 ± 32.9) and ALT (72.2 ± 13.8) and significant reduction of LDL (6.4 ± 2.6) and AST (138.7 ± 19.4) as compared to control group in female rats. ND supplementation (case group) significantly increased TC (64.4 ± 6.2), AST (255.0 ± 32.0), and ALT (84.3 ± 3.8) in comparison with the control group in male rats. CONCLUSION: Overall, our result indicated that the ND use can cause a negative effect on lipid profile and liver enzyme in rats.