Discovery of new symmetrical and asymmetrical nitrile-containing 1,4-dihydropyridine derivatives as dual kinases and P-glycoprotein inhibitors: synthesis, in vitro assays, and in silico studies.

Two new series of symmetric (1a-h) and asymmetric (2a-l) 1,4-DHP derivatives were designed, synthesised, and evaluated as anticancer agents. In vitro anticancer screening of target compounds via National cancer institute "NCI" revealed that analogues 1g, 2e, and 2l demonstrated antiproliferative action with mean growth inhibition percentage "GI%" = 41, 28, and 64, respectively. The reversal doxorubicin (DOX) effects of compounds 1g, 2e, and 2l were examined and illustrated better cytotoxic activity with IC50 =1.12, 3.64, and 3.57 µM, respectively. The most active anticancer analogues, 1g, 2e, and 2l, were inspected for their putative mechanism of action by estimating their epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER-2), and Bruton's tyrosine kinase (BTK) inhibitory activities. Furthermore, the antimicrobial activity of target compounds was assessed against six different pathogens, followed by determining the minimum inhibitory concentration "MIC" values for the most active analogues. Molecular docking study was achieved to understand mode of interactions between selected inhibitors and different biological targets.

Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters.

Coronavirus disease (COVID-19) is a worldwide epidemic that poses substantial health hazards. However, COVID-19 diagnostic test sensitivity is still restricted due to abnormalities in specimen processing. Meanwhile, optimizing the highly defined number of convolutional neural network (CNN) hyperparameters (hundreds to thousands) is a useful direction to improve its overall performance and overcome its cons. Hence, this paper proposes an optimization strategy for obtaining the optimal learning rate and momentum of a CNN's hyperparameters using the grid search method to improve the network performance. Therefore, three alternative CNN architectures (GoogleNet, VGG16, and ResNet) were used to optimize hyperparameters utilizing two different COVID-19 radiography data sets (Kaggle (X-ray) and China national center for bio-information (CT)). These architectures were tested with/without optimizing the hyperparameters. The results confirm effective disease classification using the CNN structures with optimized hyperparameters. Experimental findings indicate that the new technique outperformed the previous in terms of accuracy, sensitivity, specificity, recall, F-score, false positive and negative rates, and error rate. At epoch 25, the optimized Resnet obtained high classification accuracy, reaching 98.98% for X-ray images and 98.78% for CT images.