Ghrelin and leptin are hunger hormones related to type 2 diabetes mellitus (T2DM), and the pathogenesis of T2DM is the abnormality in insulin secretion and insulin resistance (IR). The aim of this study is to evaluate ghrelin and leptin concentrations in blood and to specify the relationship of these hormones as dependent variables with some biochemical and clinical measurements in T2DM patients. In this study, forty one T2DM and forty three non-diabetes mellitus (non-DM) subjects, aged between 40-60 years and with normal weight, were enrolled. Fasting serum ghrelin and leptin were estimated by enzyme-linked immunosorbent assay (ELISA). In our results ghrelin was significantly increased, and leptin was significantly decreased, in T2DM pa

Background. Colorectal cancer, ranking second place in global cancer mortality, arises from diverse causes. There is growing recognition of the substantial involvement of the epigenetic modifications of histones at the DNA level in the occurrence of CRC. Aim. To assess the expression of p53, HDAC1, and HDAC3 proteins in a cohort of CRC patients and to analyze potential relationship between their expression and the stages of CRC progression. Materials and Methods. The retrospective investigation was carried out on 95 paraffin-embedded CRC tissue samples. The expression of p53, HDAC1, and HDAC3 was assessed immunohistochemically. Results. Notably, the expression of the p53 protein in CRC tissue samples exhibited a prominent correlatio

In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and har

The main objective of this paper is to develop and validate flow injection method, a precise, accurate, simple, economic, low cost and specific turbidimetric method for the quantitative determination of mebeverine hydrochloride (MbH) in pharmaceutical preparations.  A homemade NAG Dual & Solo (0-180º) analyser which contains two identical detections units (cell 1 and 2) was applied for turbidity measurements. The developed method was optimized for different chemical and physical parameters such as perception reagent concentrations, aqueous salts solutions, flow rate, the intensity of the sources light, sample volume, mixing coil and purge time. The correlation coefficients (r) of the developed method were 0.9980 and 0.9986 for cell

The main objective of this paper is to develop and validate flow injection method, a precise, accurate, simple, economic, low cost and specific turbidimetric method for the quantitative determination of mebeverine hydrochloride (MbH) in pharmaceutical preparations.  A homemade NAG Dual & Solo (0-180º) analyser which contains two identical detections units (cell 1 and 2) was applied for turbidity measurements. The developed method was optimized for different chemical and physical parameters such as perception reagent concentrations, aqueous salts solutions, flow rate, the intensity of the sources light, sample volume, mixing coil and purge time. The correlation coefficients (r) of the developed method were 0.9980 and 0.9986 for

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin