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Background: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods’ labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. Aims: To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. Methods: We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews’ correlation coefficient (MCC), and area under the curve (AUC). Results: The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. Conclusion: This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances.

Background: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods' labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. Aims: To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. Methods: We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews' correlation coefficient (MCC), and area under the curve (AUC). Results: The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. Conclusion: This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances. [ABSTRACT FROM AUTHOR]

OBJECTIVES: This study aims to investigate the acute effects of thymoquinone (TQ) which has been suggested to be a cardioprotective agent, on ventricular myocytes. METHODS: Freshly isolated rat ventricular myocytes were exposed to TQ and using standard whole-cell patch-clamp technique action potential (AP), sodium current (INa), L-type calcium current (ICaL) and transient outward potassium current (Ito) were measured. RESULTS: TQ prolonged the duration of AP and decreased the peak value compared to that of control myocytes. Consistently, it inhibited INa in a concentration-dependent manner and shifted the channel kinetics to more hyperpolarized voltages. Besides, TQ not only inhibited Ito and ICaL but also significantly attenuated the isoproterenol-induced increase in ICaL. CONCLUSION: The effect of TQ on cardiomyocytes has been demonstrated for the first time. TQ changes AP morphology along with ionic currents and alleviates ß-adrenergic response in adult ventricular myocytes. These results indicate that TQ may be considered as a therapeutic agent in cases such as diabetic cardiomyopathy and cardiac hypertrophy, wherein the ß-adrenergic system is over-activated. [ABSTRACT FROM AUTHOR]

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Purpose: Due to the increasing use of wireless technology in developing countries, particularly mobile phones, the influence of electromagnetic fields (EMF) on biologic systems has become the subject of an intense debate. Therefore, in this study we investigated the effect of 2.1 GHz EMF on contractility and beta-adrenergic (β-AR) responsiveness of ventricular myocytes. Materials and methods: Rats were randomized to the following groups: Sham rats (SHAM) and rats exposed to 2.1 GHz EMF for 2 h/day for 10 weeks (EM-10). Sarcomere shortening and Ca2+transients were recorded in isolated myocytes loaded with Fura2-AM and electrically stimulated at 1 Hz, while L-type Ca2+currents (ICaL) were measured using whole-cell patch clamping at 36 ± 1°C. Cardiac nitric oxide (NO) levels were measured in tissue samples using a colorimetric assay kit. Results: Fractional shortening and amplitude of the matched Ca2+transients were not changed in EM-10 rats. Although the isoproterenol-induced (10−6M) ICaLresponse was reduced in rats exposed to EMF, basal ICaLdensity in myocytes was similar between the two groups (p< 0.01). Moreover, EMF exposure led to a significant increase in nitric oxide levels in rat heart (p< 0.02). Conclusions: Long-term exposure to 2.1 GHz EMF decreases β-AR responsiveness of ventricular myocytes through NO signaling. [ABSTRACT FROM AUTHOR]

Background: The purpose of the retrospective study was to identify the impacts of different solutions on the electrocardiogram and cardiovascular changes. Moreover, the differences between these solutions were analyzed by examining their impacts on rat ventricular cardiomyocytes. Methods: Eighty renal transplant patients were evaluated retrospectively. The patients were divided into two groups: Group UW (n =40) used the University of Wisconsin solution, and Group HTK (n =40) used the Histidine-Tryptophan-Ketoglutarate solution. Electrocardiograms of the subjects were obtained three times at different periods; during the preperfusion, intraoperative kidney reperfusion, and postperfusion phase at the end of the surgery. Any Electrocardiogram or cardiovascular alterations were noted and analyzed. Adult male Wistar rats were used for in vitro experiments. Myocyte contractility, action potentials, and membrane current were recorded in enzymatically isolated ventricular myocytes. Results: Sinus bradycardia was detected in 19 patients of Group UW, while there was short-term asystole in eight patients. However, no cardiac changes were observed in Group HTK patients. In both Groups, reperfusion and postperfusion corrected QT (QTc) intervals were different from pre-perfusion QTc intervals. Group UW patients' reperfusion and postperfusion QTc's values were higher than those of the Group HTK patients. In rat myocytes, prominent asystole episodes were observed at specific concentrations of the UW solution compared to the HTK solution. The UW solution depolarized the resting membrane potential significantly and decreased the peak value of action potential, whereas the HTK solution did not elicit a significant change in those parameters. Accordingly, the UW solution elicited a significant inward current at -70 mV, while the HTK solution activated only a modest current, which may not change the membrane potential. Conclusion: Prolongation of QTc intervals was detected with reperfusion in both groups according to electrocardiography analysis. However, the QTc interval was observed to be longer in cases using the UW solution and required intervention intraoperatively. [ABSTRACT FROM AUTHOR]

Background: The purpose of the retrospective study was to identify the impacts of different solutions on the electrocardiogram and cardiovascular changes. Moreover, the differences between these solutions were analyzed by examining their impacts on rat ventricular cardiomyocytes., Methods: Eighty renal transplant patients were evaluated retrospectively. The patients were divided into two groups: Group UW (n =40) used the University of Wisconsin solution, and Group HTK (n =40) used the Histidine-Tryptophan-Ketoglutarate solution. Electrocardiograms of the subjects were obtained three times at different periods; during the pre-perfusion, intraoperative kidney reperfusion, and postperfusion phase at the end of the surgery. Any Electrocardiogram or cardiovascular alterations were noted and analyzed. Adult male Wistar rats were used for in vitro experiments. Myocyte contractility, action potentials, and membrane current were recorded in enzymatically isolated ventricular myocytes., Results: Sinus bradycardia was detected in 19 patients of Group UW, while there was short-term asystole in eight patients. However, no cardiac changes were observed in Group HTK patients. In both Groups, reperfusion and postperfusion corrected QT (QTc) intervals were different from pre-perfusion QTc intervals. Group UW patients' reperfusion and postperfusion QTc's values were higher than those of the Group HTK patients. In rat myocytes, prominent asystole episodes were observed at specific concentrations of the UW solution compared to the HTK solution. The UW solution depolarized the resting membrane potential significantly and decreased the peak value of action potential, whereas the HTK solution did not elicit a significant change in those parameters. Accordingly, the UW solution elicited a significant inward current at -70 mV, while the HTK solution activated only a modest current, which may not change the membrane potential., Conclusion: Prolongation of QTc intervals was detected with reperfusion in both groups according to electrocardiography analysis. However, the QTc interval was observed to be longer in cases using the UW solution and required intervention intraoperatively. HIPPOKRATIA 2021, 25 (1):22-30., Competing Interests: Authors declare no commercial associations, contractual relations, or proprietary considerations that might pose a conflict of interest related or unrelated to the submitted manuscript. Authors have had no involvements that might raise the question of bias in work reported or in the conclusions, implications, or opinions stated., (Copyright 2021, Hippokratio General Hospital of Thessaloniki.)