In silico screening of potential compounds from begonia genus as 3CL protease (3Cl pro) SARS-CoV-2 inhibitors

Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective(s): This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases "promising" for the anti-SARS-CoV-2 target. Method(s): In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Result(s): Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only beta-sitosterol-3-O-beta-D-glucopyranoside with a lower docking score of -9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 A, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 A and 0.773 A, respectively. Conclusion(s): This result suggests that beta-sitosterol-3-O-beta-Dglucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.Copyright © 2023, Page Press Publications. All rights reserved.

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Background: The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID-19 is well-distributed among African citizens. Objective(s): The aim of this study is to forecast vaccination rate for COVID-19 in Africa Methods: The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy. Result(s): In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model. Conclusion(s): HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives.Copyright © 2023 Dhamodharavadhani S et al.

Repurposing of RdRp Inhibitors against SARS-CoV-2 through Molecular Docking Tools

In the present hour, the COVID-19 pandemic needs no introduction. There is continuous and keen research in progress in order to discover or develop a suitable therapeutic candidate/vaccine against the fatal, severe acute respiratory syndrome causing coronavirus (SARS-CoV-2). Drug repurposing is an approach of utilizing the therapeutic potentials of previously approved drugs against some new targets or pharmacological responses. In the presented work, we have evaluated the RNA dependent RNA polym-erase (RdRp) inhibitory potentials of FDA approved anti-viral drugs remdesivir, ribavirin, sofosbuvir and galidesivir through molecular docking. The studies were carried out using MOE 2019.0102 software against RdRp (PDB ID:7BTF, released on 8th April, 2020). All four drugs displayed good docking scores and significant binding interactions with the amino acids of the receptor. The docking protocol was validated by redocking of the ligands and the root mean square deviation (RMSD) value was found to be less than 2. The 2D and 3D binding patterns of the drugs were studied and evaluated with the help of poses. The drugs displayed excellent hydrogen bonding interactions within the cavity of the receptor and displayed comparable docking scores. These drugs may serve as new therapeutic candidates or leads against SARS-CoV-2.Copyright © 2020 Bentham Science Publishers.

Ai-Driven Covid-19 Mrna Vaccine Degradation Prediction

Background/Objective: Messenger RNA (mRNA) vaccines have emerged as a promising treatment for the coronavirus disease-2019 (COVID-19) pandemic, but such a solution has its challenges. One such issue is the mRNA vaccine's molecular stability, which requires that it be kept under certain environmental conditions that restrict its global outreach in packages, such as disposable syringes, distributed worldwide using refrigeration. Designing an environmentally stable mRNA vaccine that can withstand shipment worldwide is a challenge, since a single slit or puncture can render the complete dose of the vaccine useless. If not kept under certain environmental conditions and left unmonitored, mRNA vaccines tend to degrade rapidly. To address this problem, agencies currently store mRNA vaccines under strict refrigeration, thus limiting their global reach. The objective was to develop a hybrid deep learning model that can efficiently predict mRNA vaccine degradation rate from RNA sequences, thus aiding researchers and scientists in designing and developing a more stable mRNA vaccine in the future Results: Research presented here discusses the capability of the in-house developed hybrid deep learning model. Conclusion(s): The model was developed with a performance of 0.2430 mean columnwise root-mean-squared error (MCRMSE) score on the test data.

Evaluating the Factorial Validation of Coronavirus 2019 Phobia Scale in a Sample of Nigerian Medical Students

Background/Objective: Coronavirus-2019 (Covid-19) emerged in December 2019, causing major changes in people's social lives and other human activities. In Nigeria, there is no scale for evaluating Covid-19 fear. The 20-item Covid-19 phobia scale (C19PS) created by Arpaci et al. was validated in this study (2020). Method: The validation of the C19PS was done using 347 Nigeran medical students from universities in southeast Nigeria, and the data gathered were subjected to principal factoring axis (PFA) analysis with varimax rotation to explore its exploratory factor analysis. Besides, the confirmatory factor analysis of C19PS was done using structural equation modelling. The root mean square error of approximation (RMSEA) and comparative fit index (CFI) were used to assess the model's fit to the data. Results: In a sample of Nigerian medical students, C19PS was shown to have strong overall reliability (α =.817) and model fit (RMSEA =.040, CFI =.968). Conclusion: The C19PS is a reliable tool for determining whether or not someone has Covid-19 phobia.

Mutation Pattern in the Receptor Binding Motif of SARS-Cov-2 Variants and the Effect on Molecular Interactions in Docked Ligand Complexes

The spike glycoprotein of SARS-Cov-2 is a therapeutic target for Covid-19 and mutations in the Receptor Binding Motif (RBM) may alter the binding properties of ligands proposed to inhibit viral entry. This study aimed to identify the existence of a mutation pattern in the RBMs of SARS-Cov-2 variants and study the effect on ligand binding interactions. RBM sequences were obtained using NCBI BLASTP and subjected to multiple and pairwise sequence alignment analysis. Hypothetical generations were drawn from the phylogenetic tree. The effect of mutation on ligand binding was studied by docking zafirlukast on selected RBMs. Molecular dynamics simulations were conducted to explain molecular interactions. The sequences at the same phylogenetic level showed higher similarity with the observed differences defined by the crystallized chain length. 6XDG_E, a leaf node sequence was 76% similar to 7NXA_E, a branch from the root, and had the highest mutation. Differences in sequence similarity across successive generations were based on mutations and crystallized chain length and the amino acid substitution is not predictable. Different bond types and binding affinities were observed as well as varying Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and Region of Gyration (RoG) values for the RBMs in different variants. The RMSD, RMSF, and RoG did not differ significantly in the bound and free states of RBM from specific variants suggesting that the observed differences are attributable to amino acid substitutions. This information is crucial for drug development intended to block SARS-Cov-2 entry.

FLAVONOIDS AS POTENTIAL DRUG AGAINST POST-COVID-19 MUCORMYCOSIS: AN IN-SILICO STUDY

Covid-19 associated mucormycosis rose sharply during India’s 2nd wave of coronavirus infections. The administration of immunosuppressive drugs led to increased susceptibility of patients to oppurtunistic diseases like mucormycosis. One of the causative species of mucormycosis is Rhizopus microsporus. For this study, we choose two chalcones and examined their ability to act as potential anti-mucormycosis agents by inhibiting the R. microsporus endo β-1,4-Mannanase protein. We studied their possibility to inhibit the SARSCoV-2 main protease and RNA dependent RNA polymerase. The chalcones were docked against the proteins of interest using Autodock 4.0 followed by Molecular dynamics simulation. Our study revealed that 2’, 4’-dihydroxychalcone had the best docking with the endo β-1,4-Mannanase protein with steady root mean square deviation values and showed favourable docking with the SARS-CoV-2 proteins while passing all the drug likeliness filters. Thus 2’, 4’-dihydroxychalcone can be put through further verification to test its efficacy against the causative agents of mucormycosis and the Covid-19 pandemic.

Improved Performance Analysis for Cerebellar Ataxia disease Classification using AdaBoost

Artificial intelligence (AI) is the emerging field to diagnose and analyze chronic illnesses like Cerebellar Ataxia (CA), Spinocerebellar Ataxia (SCA), and Parkinson's disease. AI technologies such as machine learning and deep learning assist many doctors, diagnosis departments, and medical personnel in identifying and analyzing neurological disorders. Nowadays, AI used in most of the health care applications. Our research paper proffers an innovative approach to classify neurological disorders with various Machine learning algorithms. Existing research works experimented with machine learning algorithms like Support Vector machine and KNN, the performance of these algorithm is good, when the data is less and binary classified. In the proposed work, we have applied SVM, KNN, Decision tree and AdaBoost algorithms on the CA Data set. The performance of proposed methods exhibit improved accuracy when compared with the existing works. The results of the proposed work are tabulated for comparative analysis. We found that the AdaBoost algorithm shows the better classification result for Cerebellar Ataxia disease severity.

COVID-19: some simple filtering algorithms, November 2021

Introduction/ Background: COVID-19 was declared a global pandemic on March 11, 2020 by the World Health Organization. Susceptible-Exposed- Infected-Recovered-Dead (SEIRD) has been used to predict its outbreak. However, it should be handled with precaution when it comes to predicting the end of the waves. Methods: In this study, we use some basic filters to show how bad the prediction of COVID-19 data can be. This study uses the publicly available data from the Center for Systems Science and Engineering (CSSE) through their github repository. Reinforcement learning, machine learning, exponential fitting, exponential smoothing and ARIMA are used on the same COVID data set and same time window. Their root mean square errors as well as their l2 errors are investigated as performance criteria. Results: Using the time horizon of 605 days, the RMSE are 0.6619 for reinforcement learning, 5.7549 for exponential smoothing, 274.3350 for machine learning, 274.3350 for single exponential and 137.5769 for ARIMA for short-term. On a longer-term basis, machine learning, exponential smoothing and single exponential were evaluated using RMSE and the results are 173.2891 for machine learning, 909.5221 for exponential smoothing and 289.2051 for single exponential. l2 errors were plotted on a graph as well. Impact: The filters used in this study do not allow us to estimate unreported cases, unreported deaths, hospitalized cases etc. “S+E+I+R+D=N” does not hold in the filter. The use of improved filtering techniques is to be investigated. Conclusion: The methods above can be reasonably good enough for short-term tracking and filtering by designing the parameters properly. For long-term forecasts, however, the trend is different. The basic machine learning method appears to be progressively performant as the training data size increases. The l1-norm needs to be investigated.

The Psychometric Evaluation of the Immune Status Questionnaire in Indonesia

Immunity is one of the key factors in Covid-19 transmission, thus, assessments of immune status are essential for evaluating transmission risks. This study aimed to assess the validity and reliability of the Immune Status Questionnaire (ISQ), a recently developed immune status measure, among Indonesian adults, during COVID-19 Pandemic. Online Indonesian translated version of the ISQ and the Short Form 12 (SF-12) for measuring health-related quality of life were completed by 296 Indonesian adults (58% female, mean age=45±19 years old). Out of those, 102 (34%) completed a second survey one week later for the test-retest reliability assessment. The internal consistency reliability was assessed in both surveys. Confirmatory factor analysis was conducted to assess the construct validity. Correlations among ISQ items and between ISQ with SF-12 component summary were computed to assess the instruments’ convergent and divergent validities. Acceptable internal consistency reliabilities for the ISQ were found in the first and second surveys ( a=0.87 and 0.82, respectively). Each ISQ item demonstrated excellent test-retest reliability, with intraclass correlations ranging from 0.70 to 0.88. A good fit of the data was found with a root mean square error of approximation of 0.069, after a model modification. Correlations among ISQ components and between ISQ with SF-12 components provided sufficient evidence for convergent validity of the scale while divergent validity was partially supported. The validity and reliability of the Indonesian translated version of the ISQ for use in Indonesian adults are sufficiently demonstrated. The algorithm for computing ISQ in Indonesian adults, however, warrants further investigation.

Application of Near-infrared Spectroscopy and Multivariate Methods for the Estimation of Isopropyl Alcohol Content in Hand Sanitizer Formulation

To address the need of alcohol-based hand sanitizers during COVID-19, U.S. FDA has issued a guidance for the preparation of hand sanitizers that recommends 80% v/v ethanol or 75%v/v isopropyl alcohol (IPA) along with other ingredients. The aim of this study was to develop a new method to estimate IPA content in hand sanitizers by using Near-infrared (NIR) spectroscopy with a multivariate chemo-metric approach. Calibration samples containing 10-90% of IPA were used for model development. NIR data was mathematically pretreated with multiple scattering correction before development of partial least squares (PLSR) and principal component regressions (PCR) model. Both models showed good linearity over the selected range of IPA content with high R2 (>0.993), low root mean squared error (<2.163), minimum difference between standard errors between calibration and validation models (0.0009). The proposed NIR with multivariate methods provide rapid analysis of IPA content in the hand sanitizer.

Long Short-Term Memory Prediction for COVID19 Time Series

Entire world has been dealing with the number of new Coronavirus 2 or COVID-19 cases. The spread of thissevere acute respiratory syndrome has produced manyconcerns worldwide. Having data related to coronavirusavailable for tests, novel models for forecasting the number ofnew cases can be developed. In this paper, a long short-termmemory (LSTM) based methodology is applied for suchprediction. Here, experimental analysis is performed with theparameters, such as the number of layers and units of thenetwork. The root mean squared error is calculated for datacorresponding to the Republic of Serbia, as well as perdifferent continents. The results show that LSTM model canbe useful for further analysis and time series prediction © 2021, Telfor Journal. All Rights Reserved.

Utility of the Stress and Anxiety to Viral Epidemic-3 Items as a Tool for Assessing Work-Related Stress, and Relationship With Insomnia and Burnout of Healthcare Workers During the COVID-19 Pandemic

Background and ObjectiveaaThe aim of this study is to explore the usefulness of the Stress and Anxiety to Viral Epidemic-3 items (SAVE-3) scale as a tool for assessing work-related stress inhealthcare workers.MethodsaaThere were 389 participants and all remained anonymous. The SAVE-9, the PatientHealth Questionnaire-4, the Maslach Burnout Inventory-Human Services Survey for Medical Personnel(MBI-HSS-MP), the perceived stress scale (PSS), and single item insomnia measure wereused. After checking whether the SAVE-3 scale is clustered into a sole factor from SAVE-9 scalebased on principal component analysis with promax rotation, confirmatory factor analysis (CFA)was done on the 3 items of the SAVE-3 to examine the factorial validity for a unidimensionalstructure.ResultsaaThe SAVE-3 was clustered with factor loadings from 0.664–0.752, and a CFA revealedthat 3 items of the SAVE-3 cohered together into a unidimensional construct with fit for all of indices(comparative fit index = 1.00;Tucker Lewis index = 1.031;standardized root-mean-square residual= 0.001;root-mean-square-error of approximation = 0.00). The SAVE-3 scale showed acceptablereliability (Cronbach’s α = 0.56 and McDonald’s ω = 0.57) in this sample. A high SAVE-3score correlated significantly with younger age (r = -0.12, p = 0.02), a high PSS score (r = 0.24, p <0.001), a high total score for the MBI-HSS-MP (r = 0.35, p < 0.001) and all of its subscales (emotionalexhaustion, r = 0.40, p < 0.001;personal accomplishment, r = -0.14, p < 0.005;depersonalization,r = 0.39, p < 0.001), and poor sleep quality (r = 0.15, p < 0.001).ConclusionsaaTaken together, the data suggest that SAVE-3 is a reliable, valid, and usable scalefor measuring work-related stress in healthcare workers during the COVID-19 epidemic