Coumarin-based combined computational study to design novel drugs against Candida albicans.

Candida species cause the most prevalent fungal illness, candidiasis. Candida albicans is known to cause bloodstream infections. This species is a commensal bacterium, but it can cause hospital-acquired diseases, particularly in COVID-19 patients with impaired immune systems. Candida infections have increased in patients with acute respiratory distress syndrome. Coumarins are both naturally occurring and synthetically produced. In this study, the biological activity of 40 coumarin derivatives was used to create a three-dimensional quantitative structure activity relationship (3D-QSAR) model. The training and test minimum inhibitory concentration values of C. albicans active compounds were split, and a regression model based on statistical data was established. This model served as a foundation for the creation of coumarin derivative QSARs. This is a unique way to create new therapeutic compounds for various ailments. We constructed novel structural coumarin derivatives using the derived QSAR model, and the models were confirmed using molecular docking and molecular dynamics simulation.

Development of a semi manual approach for extraction of inter-pedestrian interactions at an overpass facility

The present study developed a novel method of semi-manual data extraction technique for pedestrian position (subsequently speed and trajectory) data using front inclined camera angle as many times top-down angle is unavailable due to height restrictions. Vanishing point method was used to extract data for pedestrian body dimensions across vertical and pedestrian-specific trajectory planes. Using the trajectory and body dimension information, the spacings maintained between pedestrians were estimated. Subsequently, JUPedSim software was used for extracting the fundamental macroscopic properties (speed, flow density) from the pedestrian position data, and the results were compared using classical and voronoi approaches.The developed technique was able to extract the pedestrian trajectories with an error (standard deviation) of 0.039 m at the exit section and 0.11 m at the entry section. The average lateral, longitudinal and diagonal clearances maintained by a pedestrian to walk comfortably were observed as 0.25m, 0.90m and 1.07m respectively. The comparison study for voronoi vs classical approach showed that voronoi approach had lower fluctuations in estimation of macroscopic parameters.This semi-manual technique could be helpful in study of pedestrian gap maintaining behavior and establishment of different threshold levels for the crowd management. Such approach can directly be applied to CCTV footage (which mostly gives front inclined views of camera) to ensure the social distancing in the current ongoing pandemic COVID-19. Further, the proposed approach can be made automated using the image processing tools for different applications like determination of pedestrian stream characteristics, level of services, safety evaluation and ensuring social distancing.

Subcutaneous Emphysema and Pneumomediastinum Following Non-invasive Ventilation in a Patient With Severe COVID-19 Disease.

Subcutaneous emphysema (SE) and pneumomediastinum are rare complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While SE is often non-fatal and usually self-remitting, pneumomediastinum can be fatal with high mortality rates depending on the underlying etiology. Here, we present the case of a 39-year-old otherwise healthy male who tested positive for SARS-CoV-2. The patient was treated with non-invasive mechanical ventilation (NIMV) and developed severe SE and pneumomediastinum which resulted in a fatal outcome. Although the exact pathogenesis could not be determined, the extensive lung injury caused by SARS-CoV-2 pneumonia along with possible barotrauma secondary to NIMV could have been the culprits in this case. Early detection through careful observation of these potentially fatal complications in patients with severe coronavirus disease 2019 is crucial. Further studies determining the potential risk factors and incidence of SE and pneumomediastinum, especially in patients receiving invasive mechanical ventilation or NIMV, are needed.

Identification of Severe Acute Respiratory Syndrome Coronavirus-2 inhibitors through in silico structure-based virtual screening and molecular interaction studies.

The novel coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) or COVID-19 has caused a worldwide pandemic. The fatal virus has affected the health of human beings as well as the socio-economic situation all over the world. To date, no concrete medicinal solution has been proposed to combat the viral infection, calling for an urgent, strategic, and cost-effective drug development approach that may be achievable by applying targeted computational and virtual screening protocols. Immunity is the body's natural defense against disease-causing pathogens, which can be boosted by consuming plant-based or natural food products. Active constituents derived from natural sources also scavenge the free radicals and have anti-inflammatory activities. Herbs and spices have been used for various medicinal purposes. In this study, 2,96 365 natural and synthetic derivatives (ligands) belonging to 102 classes of compounds were obtained from PubChem and assessed on Lipinski's parameters for their potential bioavailability. Out of all the derivatives, 3254 obeyed Lipinski's rule and were virtually screened. The 115 top derivatives were docked against SARS-CoV-2, SARS-CoV, MERS-CoV, and HCoV-HKV1 main proteases (Mpro s) as receptors using AutoDock Vina, AutoDock, and iGEMDOCK 2.1. The lowest binding energy was exhibited by ligands 2 and 6 against all the four Mpro s. The molecular dynamic simulation was also performed with ligand 6 using the GROMACS package. Good bioactivity scores, absorption, distribution, metabolism, excretion, and toxicity profile and drug-like pharmacokinetic parameters were also obtained. Hydroxychloroquine was used as the control drug.

Impact of COVID-19: A radical modal shift from public to private transport mode.

The unprecedented shock triggered by the COVID-19 pandemic has caused significant impact on public transportation services, travel behavior and mode choice preferences. Increasing risk of virus contagion in shared travel modes might result in a systemic shift from public transport to car commute. Such a shift causes increased congestions, emissions with a burden on the existing infrastructure. Given the urgent need of reconsideration of transport in a post-COVID world, this study presents insights into the possible shift from public transport to car commute due to the coronavirus crisis, potential factors influencing the mode shift, with emphasis being also laid on suitable strategies for promoting public transport use in the future world. Based on an online questionnaire survey conducted in India, results of logistic regression model indicate that commuters' socio-economic characteristics such as age, gender and monthly income tend to significantly influence mode switch preferences. In addition, trip characteristics including travel time, overcrowding and hygiene are strongly associated with mode shift preferences from public transport to car use. Commuters' perceptions on several strategies for promoting public transport have also been assessed, which will indeed pave the way for the formulation of post-COVID transport policies. In essence, efforts need to be directed towards restoring users' confidence and trust by providing a safe, secure and healthy environment to the public transport users.

Effect of COVID-19 lockdown on noise pollution levels in an Indian city: a case study of Kanpur.

Noise pollution is an emerging environmental threat, prolonged exposure of which can cause annoyance, sleep disturbance, hypertension, psychiatric disorders, and also hormonal dysfunction. Among all the sources of noise pollution, the noise generated by road vehicle traffic significantly affects the quality of urban environments. Concerning the recent imposition of COVID-19 societal lockdown, this study attempts to investigate the impacts of COVID-19 lockdown on the changes in noise pollution levels before, during, and after lockdown phase in different residential, commercial, industrial, and silence zones of the city of Kanpur, India. Utilizing data collected from portable environmental sensors, the average noise levels before lockdown and during lockdown were found to be in the range of 44.85 dB to 79.57 dB and 38.55 dB to 57.79 dB, respectively, for different zones. Although a significant reduction in the noise levels was observed during lockdown, except for commercial zone, all other monitoring stations had reported sound levels quite higher than the recommended noise limits set by the Central Pollution Control Board (CPCB) of India. Results further indicated that the impact of road traffic noise on risk of high annoyance and sleep disturbance was found to be lower during lockdown as compared to that of pre-lockdown and unlock phase. While the annoyance level in residential (86.23%), industrial (87.44%), and silence (84.47%) was higher in pre-lockdown period, it reduced to 41.25, 50.28, and 43.07% in the lockdown phase. Even the risk of sleep disturbance in the residential zone was found to reduce from 37.96% during pre-lockdown to 14.72% during lockdown phase. Several noise mitigation strategies are also proposed, which may indeed pave the way for devising noise control measures in the local and regional level.

In silico validation of coumarin derivatives as potential inhibitors against Main Protease, NSP10/NSP16-Methyltransferase, Phosphatase and Endoribonuclease of SARS CoV-2.

Coronavirus Disease (COVID-19) is recently declared pandemic (WHO) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus was named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), (Coronavirus Disease 2019). Currently, there is no specific drug for the therapy of COVID-19. So, there is a need to develop or find out the new drug from the existing to cure the COVID-19. Identification of a potent inhibitor of Methyltransferase, Endoribonuclease, Phosphatase and Main Protease enzymes of SARS CoV-2 by coumarin derivatives using insilico approach. The in silico studies were performed on maestro 12.0 software (Schrodinger LLC 2019, USA). Two thousand seven hundred fifty-five biologically active coumarin derivative was docked with above receptor proteins of SARS CoV-2. The molecular dynamic simulation of the top one ligand of respected proteins was performed. Top five ligands of each protein were taken for study. Coumarin derivatives actively interact with taken receptors and showed good docking results for Methyltransferase, Endoribonuclease, Phosphatase and Main Protease and top five compounds of each have docking score from -9.00 to -7.97, -8.42 to -6.80, -8.63 to -7.48 and -7.30 to -6.01 kcal/mol, respectively. The docked compounds were showed RMSD and binding stability of simulated ligands are show the potency of ligands against the SARS CoV-2. Our study provides information on drugs that may be a potent inhibitor of COVID-19 infection. Drug repurposing of the available drugs would be great help in the treatment of COVID-19 infection. The combination therapy of the finding may improve inhibitory activity. Communicated by Ramaswamy H. SarmaHighlightsCoronavirus Disease (COVID-19) is recently declared pandemic (WHO) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).In silico virtual screening, docking, ADME, MM-GBSA and MD simulation analysis of coumarin derivatives against Methyltransferase (MTase), Endoribonuclease(endoU), ADP ribose Phosphatase and Main Protease enzyme of SARS CoV-2.All the analysis was performed on Maestro 12.0 Schrodinger software against respective receptors.Top five compounds of coumarin derivatives s docked at the active site of Methyltransferase (MTase), Endoribonuclease(endoU), ADP ribose Phosphatase and protease and top five compounds of each have docking score from -9.00 to -7.97, -8.42 to -6.80, -8.63 to -7.48 and -7.30 to -6.01 kcal/mol, respectively, of SARS CoV-2.These compounds were used to analysis of binding free energy by using the Prime MM-GBSA module.All the compounds showed drug-likeness properties.MD simulation of Proteins and ligands showed binding stability and good RMSD, radius of gyration of protein, coulomb-SR and LJ-SR energy.

Virtual screening, ADME/T, and binding free energy analysis of anti-viral, anti-protease, and anti-infectious compounds against NSP10/NSP16 methyltransferase and main protease of SARS CoV-2.

Recently, a pathogen has been identified as a novel coronavirus (SARS-CoV-2) and found to trigger novel pneumonia (COVID-19) in human beings and some other mammals. The uncontrolled release of cytokines is seen from the primary stages of symptoms to last acute respiratory distress syndrome (ARDS). Thus, it is necessary to find out safe and effective drugs against this deadly coronavirus as soon as possible. Here, we downloaded the three-dimensional model of NSP10/NSP16 methyltransferase (PDB-ID: 6w6l) and main protease (PDB-ID: 6lu7) of COVID-19. Using these molecular models, we performed virtual screening with our anti-viral, inti-infectious, and anti-protease compounds, which are attractive therapeutics to prevent infection of the COVID-19. We found that top screened compound binds with protein molecules with good dock score with the help of hydrophobic interactions and hydrogen bonding. We observed that protease complexed with Cyclocytidine hydrochloride (anti-viral and anti-cancer), Trifluridine (anti-viral), Adonitol, and Meropenem (anti-bacterial), and Penciclovir (anti-viral) bound with a good docking score ranging from -6.8 to -5.1 (Kcal/mol). Further, NSP10/NSP16 methyltransferase complexed with Telbivudine, Oxytetracycline dihydrate (anti-viral), Methylgallate (anti-malarial), 2-deoxyglucose and Daphnetin (anti-cancer) from the docking score of -7.0 to -5.7 (Kcal/mol). In conclusion, the selected compounds may be used as a novel therapeutic agent to combat this deadly pandemic disease, SARS-CoV-2 infection, but needs further experimental research.HighlightsNSP10/NSP16 methyltransferase and main protease complex of SARS CoV-2 bind with selected drugs.NSP10/NSP16 methyltransferase and protease interacted with drugs by hydrophobic interactions.Compounds show good DG binging free energy with protein complexes.Ligands were found to follow the Lipinski rule of five.