Biosensor design using an electroactive label-based aptamer to detect bisphenol A in serum samples

Protein–protein interactions (PPIs) are important for the study of protein functions and pathways involved in differentbiological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gapin identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms hasbeen used in conjunction with experimental methods for discovery of novel protein interactions. The two most popularsupervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forestclassifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI datasetconfidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Severalclustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for theprediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have beenwidely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.

Weight Management Effects of a Mixture of Conjugated Linoleic Acid and L-Carnitine in Diet Induced Obese Rats.

Aims: To examine simultaneous effects of Conjugated Linoleic Acid (CLA) and L-carnitine (LC) on weight gain in diet induced obese rats. Study Design: Experimental study. Place and Duration of Study: Department of Nutrition, Faculty of Para-Medical Sciences, Ahvaz Jundishapur University of Medical Sciences (January 2014 to January 2015). Methodology: Forty male Wistar rats were randomly divided into two groups: Normal fat diet (n=8), and High fat diet (HFD) (n=32). After eight weeks, the second group maintained HFD and was subdivided into 4 categories: Corn Oil group, 500 mg CLA, 200 mg LC, and 500 mg CLA+ 200 mg LC (all doses per kg body weight), which were administered by oral gavage for four weeks. Body weights were measured and recorded weekly by means of a digital scale. SPSS Version 16 was used for statistical analysis. Results: At the end of eighth week, a significant difference in weights was observed between HFD (295.43±5.36 gr) and NFD (246.38±6.48 gr) group. After four weeks, LC significantly reduced weight gain by 7.5% (P = .047). Trend of weight gain in CLA and LC + CLA groups were decelerated (24 and 25 gr respectively), but it was statistically insignificant (P = .08, .12 respectively). Conclusion: Findings of this experimental study showed that a high fat diet led to obesity and combined LC and CLA could decelerate weight gain to some extent. However, it needs further work to validate reliability in human.