Insights from the molecular docking analysis of GRP78 with natural compound inhibitors in the management of cancers.

Cancer is regarded as one of the world's most serious health issues. Glucose regulated protein (GRP78) exhibits a vital role in the proliferation, invasion, and metastasis of numerous cancer cells. Based on that, this study screened the 390 natural compounds targeting the GRP78 catalytic site. Among them, corynanthin, toyocamycin, and nanaomycin were found to strongly bind with GRP78 and possess the binding affinities of -8.4, -8.9, and -8.7 kcal/mol, respectively. In addition, these compounds interacted with key residues of GRP78 and have several amino acid residues interaction in common with the cocrystal ligand (ATP). Based on physicochemical parameters and ADME evaluations, these compounds were found to have good drug-like properties. These compounds could be used as possible GRP78 inhibitors in the fight against cancers. Albeit, exhaustive experimental studies would be required to confirm the findings described here.

The variant artificial intelligence easy scoring (VARIES) system.

PURPOSE: Medical artificial intelligence (MAI) is artificial intelligence (AI) applied to the healthcare field. AI can be applied to many different aspects of genetics, such as variant classification. With little or no prior experience in AI coding, we share our experience with variant classification using the Variant Artificial Intelligence Easy Scoring (VARIES), an open-access platform, and the Automatic Machine Learning (AutoML) of the Google Cloud Platform. METHODS: We investigated exome sequencing data from a sample of 1410 individuals. The majority (80%) were used for training and 20% for testing. The user-friendly Google Cloud Platform was used to create the VARIES model, and the TRIPOD checklist to develop and validate the prediction model for the development of the VARIES system. RESULTS: The learning rate of the training dataset reached optimal results at an early stage of iteration, with a loss value near zero in approximately 4 min. For the testing dataset, the results for F1 (micro average) was 0.64, F1 (macro average) 0.34, micro-average area under the curve AUC (one-over-rest) 0.81 and the macro-average AUC (one-over-rest) 0.73. The overall performance characteristics of the VARIES model suggest the classifier has a high predictive ability. CONCLUSION: We present a systematic guideline to create a genomic AI prediction tool with high predictive power, using a graphical user interface provided by Google Cloud Platform, with no prior experience in creating the software programs required.

Assessment of microalbuminuria and albumin creatinine ratio in patients with type 2 diabetes mellitus.

AIM: We aimed to evaluate the levels of urine microalbumin, urine albumin creatinine ratio, plasma creatinine and glycosylated hemoglobin (HbA1c) among type 2 diabetic patients and assessed the correlation between microalbuminuria and plasma creatinine levels. MATERIALS AND METHODS: A retrospective chart review study was conducted at Department of Clinical Chemistry, King Abdulaziz Medical City in Riyadh, Saudi Arabia, during August to December 2014. The study included 100 male and female patients diagnosed with type 2 diabetes mellitus (DM) and excluding patients with type 1 DM. Medical history and biochemical laboratory data were obtained from medical records and from biochemistry laboratory database. RESULTS: Increase in mean level of plasma creatinine (138 µmol/L), urine microalbuminuria (240 mg/L), albumin creatinine ratio (82) and HbA1c (8.7%) was observed among type 2 DM patients. Moderate positive correlation was observed between microalbuminuria and urine albumin creatinine ratio (r = 0.509 P = 0.0006) and between urine albumin creatinine ratio and plasma creatinine (r = 0.553 P = 0.017). CONCLUSION: We concluded that type 2 DM patients who are at risk of developing renal impairment must be regularly monitored for microalbuminuria, urine albumin creatinine ratio, and HbA1c levels.