Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction.

Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.

Synthesis of new arylhydrazide bearing Schiff bases/thiazolidinone: α-Amylase, urease activities and their molecular docking studies.

Alpha-amylase and urease enzyme over expression endorses various complications like rheumatoid arthritis, urinary tract infection, colon cancer, metabolic disorder, cardiovascular risk, and chronic kidney disease. To overcome these complications, we have synthesized new arylhydrazide bearing Schiff bases/thiazolidinone analogues as α-amylase and urease inhibitors. The analogues 1a-r were evaluated for α-amylase inhibitory potential. All analogues were found active and show IC50 value ranging between 0.8 ±â€¯0.05 and 12.50 ±â€¯0.5 µM as compare to standard acarbose (IC50 = 1.70 ±â€¯0.10 µM). Among the synthesized analogs, compound 1j, 1r, 1k, 1e, 1b and 1f having IC50 values 0.8 ±â€¯0.05, 0.9 ±â€¯0.05, 1.00 ±â€¯0.05, 1.10 ±â€¯0.10, 1.20 ±â€¯0.10 and 1.30 ±â€¯0.10 µM respectively showed an excellent inhibitory potential. Analogs 2a-o were evaluated against urease activity. All analogues were found active and show IC50 value ranging between 4.10 ±â€¯0.02 and 38.20 ±â€¯1.10 µM as compare to standard thiourea (IC50 = 21.40 ±â€¯0.21 µM). Among the synthesized analogs, compound 2k, 2a, 2h, 2j, 2f, 2e, 2g, 2b and 2l having IC50 values 4.10 ±â€¯0.02, 4.60 ±â€¯0.02, 4.70 ±â€¯0.03, 5.40 ±â€¯0.02, 6.70 ±â€¯0.05, 8.30 ±â€¯0.3, 11.20 ±â€¯0.04, 16.90 ±â€¯0.8 and 19.80 ±â€¯0.60 µM respectively showed an excellent inhibitory potential. All compounds were characterized through 1H, 13C NMR and HR-EIMS analysis. Structure activity relationship of the synthesized analogs were recognized and confirmed through molecular docking studies.

Synthesis, thymidine phosphorylase, angiogenic inhibition and molecular docking study of isoquinoline derivatives.

Isoquinoline analogues (KA-1 to 16) have been synthesized and evaluated for their E. coli thymidine phosphorylase inhibitory activity. Except compound 11, all other analogs showed outstanding thymidine inhibitory potential ranging in between 4.40 ±â€¯0.20 to 69.30 ±â€¯1.80 µM when compared with standard drug 7-Deazaxanthine (IC50 = 38.68 ±â€¯4.42 µM). Structure Activity Relationships has been established for all compounds, mainly based on substitution pattern on phenyl ring. All analogs were characterized by various spectroscopic techniques such as 1H NMR, 13C NMR and EI-MS. The binding interactions of isoquinoline analogues with the active site of TP enzyme, the molecular docking studies were performed. Furthermore, the angiogenic inhibitory potentials of isoquinoline analogues (KA-1-9, 14, 12 and 16) were determined in the presence of standard drug Dexamethasone based on percentage inhibitions at various concentrations. Herein this work analogue KA-12, 14 and 16 emerged with most potent angiogenic inhibitory potentials among the synthesized analogues.