A Comparative Study of Approved Drugs for SARS-CoV-2 by Molecular Docking

SARS-CoV-2, a new type of Coronavirus, has affected more millions of people worldwide. From the spread of this infection, many studies related to this virus and drug designing for the treatment have been started. Most of the studies target the SARS-CoV-2 main protease, spike protein of SASR-CoV-2, and some are targeting the human furin protease. In the current work, we chose the clinically used drug molecules remdesivir, favipiravir, lopinavir, hydroxychloroquine, and chloroquine onto the target protein SARS-CoV-2 main protease. Docking studies were performed using Arguslab, while Discovery Studio collected 2D and 3D pose views with the crystal structure of COVID-19 main protease in complex with an inhibitor N3 with PDB ID 6LU7. Computational studies reveal that all ligands provided good binding affinities towards the target protein. Among all the chosen drugs, lopinavir showed the highest docking score of -11.75 kcal/mol. The results from this molecular docking study encourage the use of lopinavir as the first-line treatment drug due to its highest binding affinity

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead.

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

727-731In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead

Mobilization of Stem Cells Using G-CSF for Acute Ischemic Stroke: A Randomized Controlled, Pilot Study

Background. There is emerging evidence to support the use of granulocyte colony-stimulating factor (G-CSF) therapy in patients with acute ischemic stroke. Aims. To explore feasibility, safety, and preliminary efficacy of G-CSF therapy in patients with acute ischemic stroke. Patients and Method. In randomized study, 10 patients with acute ischemic stroke were recruited in 1 : 1 ratio to receive 10 μg/kg G-CSF treatment subcutaneously daily for five days with conventional care or conventional treatment alone. Efficacy outcome measures were assessed at baseline, one month, and after six months of treatment included Barthel Index (BI), National Institute of Health Stroke Scale, and modified Rankin Scale. Results. One patient in G-CSF therapy arm died due to raised intracranial pressure. No severe adverse effects were seen in rest of patients receiving G-CSF therapy arm or control arm. No statistically significant difference between intervention and control was observed in any of the scores though a trend of higher improvement of BI score is seen in the intervention group. Conclusion. Although this study did not have power to examine efficacy, it provides preliminary evidence of potential safety, feasibility, and tolerability of G-CSF therapy. Further studies need to be done on a large sample to confirm the results

The Endocannabinoid System, Aggression, and the Violence of Synthetic Cannabinoid Use, Borderline Personality Disorder, Antisocial Personality Disorder, and Other Psychiatric Disorders

Endogenous and exogenous cannabinoids bind to central cannabinoid receptors to control a multitude of behavioral functions, including aggression. The first main objective of this review is to dissect components of the endocannabinoid system, including cannabinoid 1 and cannabinoid 2 receptors; the endogenous cannabinoids anandamide and 2-arachidonoylglycerol; and the indirect cannabinoid modulators fatty acid amide hydrolase and monoacylglycerol lipase; that have shown abnormalities in basic research studies investigating mechanisms of aggression. While most human research has concluded that the active ingredient of marijuana, Δ9-tetrahydrocannabinol, tends to dampen rather than provoke aggression in acute doses, recent evidence supports a relationship between the ingestion of synthetic cannabinoids and emergence of violent or aggressive behavior. Thus, another objective is to evaluate the emerging clinical data. This paper also discusses the relationship between prenatal and perinatal exposure to cannabis as well as use of cannabis in adolescence on aggressive outcomes. A final objective of the paper is to discuss endocannabinoid abnormalities in psychotic and affective disorders, as well as clinically aggressive populations, such as borderline personality disorder and antisocial personality disorder. With regard to the former condition, decreased anandamide metabolites have been reported in the cerebrospinal fluid, while some preliminary evidence suggests that fatty acid amide hydrolase genetic polymorphisms are linked to antisocial personality disorder and impulsive-antisocial psychopathic traits. To summarize, this paper will draw upon basic and clinical research to explain how the endocannabinoid system may contribute to the genesis of aggressive behavior

Table1.xlsx

<p>Endogenous and exogenous cannabinoids bind to central cannabinoid receptors to control a multitude of behavioral functions, including aggression. The first main objective of this review is to dissect components of the endocannabinoid system, including cannabinoid 1 and cannabinoid 2 receptors; the endogenous cannabinoids anandamide and 2-arachidonoylglycerol; and the indirect cannabinoid modulators fatty acid amide hydrolase and monoacylglycerol lipase; that have shown abnormalities in basic research studies investigating mechanisms of aggression. While most human research has concluded that the active ingredient of marijuana, Δ9-tetrahydrocannabinol, tends to dampen rather than provoke aggression in acute doses, recent evidence supports a relationship between the ingestion of synthetic cannabinoids and emergence of violent or aggressive behavior. Thus, another objective is to evaluate the emerging clinical data. This paper also discusses the relationship between prenatal and perinatal exposure to cannabis as well as use of cannabis in adolescence on aggressive outcomes. A final objective of the paper is to discuss endocannabinoid abnormalities in psychotic and affective disorders, as well as clinically aggressive populations, such as borderline personality disorder and antisocial personality disorder. With regard to the former condition, decreased anandamide metabolites have been reported in the cerebrospinal fluid, while some preliminary evidence suggests that fatty acid amide hydrolase genetic polymorphisms are linked to antisocial personality disorder and impulsive-antisocial psychopathic traits. To summarize, this paper will draw upon basic and clinical research to explain how the endocannabinoid system may contribute to the genesis of aggressive behavior.</p

Empirical Mode Decomposition based Support Vector Regression for Agricultural Price Forecasting

Not AvailablePrice information is a piece of crucial market information for a farmer. The price instability and uncertainty pose a significant challenge to decision-makers in making proper production and marketing plans to minimize risk. Agricultural price series cannot be modelled and predicted accurately by traditional econometric models owing to its nonlinearity and nonstationary behaviour. In the present study, an attempt has been made to model and predict price series using Empirical Mode Decomposition (EMD) based Support Vector Regression (SVR) model. EMD decomposes the original nonlinear and nonstationary dataset into a finite and small number of sub-signals. Then each sub-signal was modelled and forecasted by SVR method. Finally, all the forecasted values of sub-signal were aggregated to make final ensemble forecast. The effectiveness and predictability of the proposed methodology was verified using Chilli wholesale price index (WPI) dataset as a sample. The results indicated that the performance of the proposed model was substantially superior as compared to the standard SVR.Not Availabl

Not Available

Not AvailablePrice information is a crucial market information for a farmer. The price instability and uncertainty pose a significant challenge to decision makers in making proper production and marketing plans to minimize risk. Agricultural price series cannot be modelled and predicted accurately by traditional econometric models owing to its nonlinearity and nonstationary behaviour. In the present study an attempt has been made to model and predict price series using Empirical Mode Decomposition (EMD) based Support Vector Regression (SVR) model. EMD decomposes the original nonlinear and nonstationary dataset into a finite and small number of sub-signals. Then each sub-signal was modelled and forecasted by SVR method. Finally, all the forecasted values of sub-signal were aggregated to make final ensemble forecast. The effectiveness and predictability of the proposed methodology was verified using Chilli wholesale price index (WPI) dataset as sample. The results indicated that the performance of the proposed model was substantially superior as compared to the standard SVR.Not Availabl