Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.

Hair, being one of the most important components of the beauty care processes, attracts the use of a variety of hair treating cosmetics. Conventional hair treating cosmetics are not satisfactory for one reason or the other. Commercially used keratins are isolated from wool or chicken feathers. As these lack complete sequence identity with human hair keratin, are likely to be less efficient than the human hair keratin. K31, a type I acidic keratin, is a major protein of human hair keratin complex and it is essential for maintaining the hair tensile strength. In this study keratin K31 (46 kDa) gene was expressed in Escherichia coli at a level of approximately 35% of the total cell proteins. The protein, however, was expressed as insoluble inclusion bodies. The expressed protein was refolded and purified by FPLC using an anion-exchange column. The CD analysis results showed that the K31 was properly refolded. MALDI-TOF mass spectroscopy showed the characteristics expected for human K31 keratin. The refolded and partially purified K31 protein, when applied on chemically damaged hairs, increased the diameter of the hair up to 49%. The mechanical strength of the bleached hair increased by almost 2 fold after a single treatment of K31. The protein also straightened curly hair efficiently on a single treatment for one hour. Application of K31 resulted in marked improvements in smoothness, diameter and mechanical strength of the damaged hair.

Identification of Circulating miRNAs as Non-Invasive Biomarkers of Triple Negative Breast Cancer in the Population of Pakistan