Glucagon and Glucagon-like Peptide-1 Receptors: Promising Therapeutic Targets for an Effective Management of Diabetes Mellitus.

G-protein-coupled receptors (GPCRs) are membrane-bound proteins, which are responsible for the detection of extracellular stimuli and the origination of intracellular responses. Both glucagon and glucagon-like peptide-1 (GLP-1) receptors belong to G protein-coupled receptor (GPCR) superfamily. Along with insulin, glucagon and GLP-1 are critical hormones for maintaining normal serum glucose within the human body. Glucagon generally plays its role in the liver through cyclic adenosine monophosphate (cAMP), where it compensates for the action of insulin. GLP-1 is secreted by the L-cells of the small intestine to stimulate insulin secretion and inhibit glucagon action. Despite extensive research efforts and the multiple approaches adopted, the glycemic control in the case of type-2 diabetes mellitus remains a major challenge. Therefore, a deep understanding of the structure-function relationship of these receptors will have great implications for future therapies in order to maintain a normal glucose level for an extended period of time. The antagonists of glucagon receptors that can effectively block the hepatic glucose production, as a result of glucagon action, are highly desirable for the tuning of the hyperglycemic state in type 2 diabetes mellitus. In the same manner, GLP-1R agonists act as important treatment modalities, thanks to their multiple anti-diabetic actions to attain normal glucose levels. In this review article, the structural diversity of glucagon and GLP-1 receptors along with their signaling pathways, site-directed mutations and significance in drug discovery against type-2 diabetes are illustrated. Moreover, the promising non-peptide antagonists of glucagon receptor and agonists of GLP-1 receptor, for the management of diabetes are presented with elaboration on the structure-activity relationship (SAR).

Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis.

This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm-1, at a resolution of 2 cm-1 and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R2 value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.

Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices.

Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R2 value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R2 value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves.

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.