Structure-based drug design, molecular dynamics simulation, ADMET, and quantum chemical studies of some thiazolinones targeting influenza neuraminidase.

The genetic mutability of the influenza virus leads to the existence of drug-resistant strains which is dangerous, particularly with the lingering coronavirus disease (COVID-19). This necessitated the need for the search and discovery of more potential anti-influenza agents to avert future outbreaks. In furtherance of our previous in-silico studies on 5-benzyl-4-thiazolinones as anti-influenza neuraminidase (NA) inhibitors, molecule 11 was selected as the template scaffold for the structure-based drug design due to its good binding, pharmacokinetic profiling, and better NA inhibitory activity. As such, eighteen (18) new molecules (11a-r) were designed with better MolDock scores as compared with the template scaffold and the zanamivir reference drug. However, the dynamic stability of molecule 11a in the binding cavity of the NA target (3TI5) showed water-mediated hydrogen and hydrophobic bondings with the active residues such as Arg118, Ile149, Arg152, Ile222, Trp403, and Ile427 after the MD simulation for 100 ns. The drug-likeness and ADMET assessment of all designed molecules predicted non-violation of the stipulated thresholds of Lipinski's rule and good pharmacokinetic properties respectively. In addition, the quantum chemical calculations also suggested the significant chemical reactivity of molecules with their smaller band energy gap, high electrophilicity, high softness, and low hardness. The results obtained in this study proposed a reliable in-silico viewpoint for anti-influenza drug discovery and development.Communicated by Ramaswamy H. Sarma.

In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents.

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R2train = 0.9158, Q2=0.8475) and GFA-ANN (R2train = 0.9264, Q2=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R2train =0.977, Q2=0.509) and CoMSIA_S (R2train =0.976, Q2=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski's criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.Communicated by Ramaswamy H. Sarma.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Background: Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results: The 2D-QSAR modeling results showed GFA-MLR ( R train 2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, R test 2 = 0.8943, R pred 2 = 0.7745) and GFA-ANN ( R train 2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, R test 2 = 0.8831, R pred 2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( R train 2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( R train 2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> - 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents. Conclusion: The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Machine Learning-Driven Approach for a COVID-19 Warning System

The emergency of the pandemic and the absence of treatment have motivated researchers in all the fields to deal with the pandemic situation. In the field of computer science, major contributions include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Since the emergence of information technology, data science and machine learning have become the most widely used techniques to detect, diagnose, and predict the positive cases of COVID-19. This paper presents the prediction of confirmed cases of COVID-19 and its mortality rate and then a COVID-19 warning system is proposed based on the machine learning time series model. We have used the date and country-wise confirmed, detected, recovered, and death cases features for training of the model based on the COVID-19 dataset. Finally, we compared the performance of time series models on the current study dataset, and we observed that PROPHET and Auto-Regressive (AR) models predicted the COVID-19 positive cases with a low error rate. Moreover, death cases are positively correlated with the confirmed detected cases, mainly based on different regions' populations. The proposed forecasting system, driven by machine learning approaches, will help the health departments of underdeveloped countries to monitor the deaths and confirm detected cases of COVID-19. It will also help make futuristic decisions on testing and developing more health facilities, mostly to avoid spreading diseases.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results The 2D-QSAR modeling results showed GFA-MLR ( Conclusion The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Managing Post COVID-19 Patient with Breathlessness.

Background: Chronic respiratory disease may be associated with severity of coronavirus disease 2019 (COVID-19) infection. We review a case of chronic obstructive pulmonary disease (COPD) patient who developed acute breathlessness post COVID-19 infection, also focusing on the diagnostic approach. Case: A 69-year-old gentleman with background history of COPD GOLD D and ischemic heart disease was admitted with severe COVID-19 infection. He required high-flow nasal cannula upon presentation. A computed tomography pulmonary angiography (CTPA) thorax at day 10 of illness revealed moderate organizing pneumonia (OP) with emphysematous changes, without pulmonary embolism. He received oral baricitinib and intravenous methylprednisolone for 3 days, which was then followed by tapering prednisolone starting dose of 1 mg/kg/day (60 mg/day) with reduction of 10 mg prednisolone every 3 days. COPD pharmacotherapy was optimized with early utilization of dual bronchodilators and inhaled corticosteroid was withheld. He underwent inpatient pulmonary rehabilitation and was discharged with home oxygen therapy. Unfortunately, he was re-admitted after 2 weeks with shortness of breath and fever for 3 days. Blood results revealed leucocytosis with raised C-reactive protein. A repeat CTPA showed increase reticulations and crazy paving pattern with reduction in lung volume. Multidisciplinary team discussion concluded it as interstitial pneumonia with COVID-19 OP and fibrosis progression. Prednisolone was stopped and he responded well with antibiotics. A follow-up at 3 months post COVID-19 infection showed improvement of clinical symptoms with radiological resolution of ground glass changes. Conclusion: Corticosteroid inhaler should be cautioned in this case, in view of recent pneumonia and non-elevated serum eosinophil count. These groups of patients should be closely followed up to unmask interstitial lung disease that may present prior to COVID-19 and worsen post-infection. Optimizing pre-existing medical conditions should be the paramount intervention.

Development of bullous lung disease with pneumothorax following SARS-CoV-2 infection.

Cystic lung formation secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was described during coronavirus disease pandemic, but with relatively low prevalence. A rare yet under-recognized complication is that these cystic areas may progress to bullae, cavities and pneumothorax. We reported two cases of ruptured bullae with pneumothorax following SARS-CoV-2 infection. Two patients were discharged following SARS-CoV-2 pneumonia, which did not require invasive mechanical ventilation (IMV). However, both patients presented again a month later with shortness of breath. Repeated computed tomography (CT) thorax showed development of bullous lung disease and pneumothorax. The first patient underwent surgical intervention whilst the second patient was treated conservatively. Development of bullous lung disease following SARS-CoV-2 infection is rare but may be associated with serious morbidity. Patients whose general condition permits should be offered surgical intervention whilst conservative management is reserved for non-surgical candidates.

Identifying Lockdown Relaxation Strategies and Policy Implications to Fight against COVID-19: Medical Experts Perspective from Pakistan.

The COVID-19 pandemic, was first identified in Wuhan, China, has had a drastic effect on the world economy and compelled governments to enforce lockdown in territories. However, lockdown is destroying the world economy badly as well as the physical and mental health of people. Therefore, governments must develop Lockdown Relaxation Strategies (LRS) to overcome the negative consequences of lockdown in Pakistan. Identifying LRS is important for public health and economic restoration. Therefore, this work is an initial attempt to develop LRS in a developing country - Pakistan, and prioritize LRS through a novel ISM-MICMAC approach. By taking response from experts, results show that implementation of smart lockdowns in affected areas, meeting minimum criteria of public health standards, limited operations of public transportation are the most important strategies. Results provide a strategic guideline for governments to take necessary measures and allocate resources appropriately.

Knowledge, Attitude, and Practices Among the General Population During the Later Stage of the COVID-19 Pandemic in Malaysia: A Cross-Sectional Study

Purpose A different pattern of mental health issues was reported during the later stage of the COVID-19 pandemic;however, few studies have examined Malaysians’ knowledge, attitudes, and practices (KAP) prevalent during this time. Patients and Methods A nationwide online cross-sectional study was conducted in Malaysia from June 1, 2021 to June 14, 2021, ie, 18-months from the first reported COVID-19 case in the country. Citizens aged 18 years and above were recruited by means of the snowball sampling method. ANOVA, Pearson correlation, and linear regression tests were used. Results Of the 2168 respondents, most were young adults (62.7%), females (62.4%), tertiary educated individuals (84%), non-health care workers (85.9%), and individuals who knew someone diagnosed with COVID-19 (75.2%). The mean score for knowledge was 10.0 ± 1.52 (maximum score = 12);correct response rate for each question ranged from 54.2% to 99%. The mean score in terms of attitude was 1.3 ± 0.85 (maximum score = 2);68.7% respondents agreed that control over COVID-19 would finally be achieved;and 62.3% believed that Malaysia could conquer COVID-19. The mean score for practices was 5.1 ± 1.10 (maximum score = 6);81.5%, 88.1%, and 74.1% respondents avoided crowded places, confined spaces, and conversations in close physical proximity, respectively. Furthermore, 94.2% wore masks when leaving home;89.0% practiced hand hygiene;and 83.8% adhering to COVID-19 warnings. Small but significant correlations were found between knowledge and attitude (r = 0.078, p < 0.001) as well as between knowledge and practices (r = 0.070, p = 0.001). Conclusion Malaysians exhibited sound knowledge but negative attitudes and inadequate practices pertaining to COVID-19 during the pandemic’s later stage. At this phase, unlike at the early stage, the public’s sound knowledge ensured little improvement in their attitudes and practices. Therefore, health education at the later pandemic stage should focus on promoting positive attitudes and developing better practices.

Intelligent Load-Balancing Framework for Fog-Enabled Communication in Healthcare

The present technological era significantly makes use of Internet-of-Things (IoT) devices for offering and implementing healthcare services. Post COVID-19, the future of the healthcare system is highly reliant upon the inculcation of Artificial-Intelligence (AI) mechanisms in its day-to-day procedures, and this is realized in its implementation using sensor-enabled smart and intelligent IoT devices for providing extensive care to patients relative to the symmetric concept. The offerings of such AI-enabled services include handling the huge amount of data processed and sensed by smart medical sensors without compromising the performance parameters, such as the response time, latency, availability, cost and processing time. This has resulted in a need to balance the load of the smart operational devices to avoid any failure of responsiveness. Thus, in this paper, a fog-based framework is proposed that can balance the load among fog nodes for handling the challenging communication and processing requirements of intelligent real-time applications.

The asymmetric volatility of the Islamic capital market during the COVID-19 pandemic

This study attempts to identify the existence of asymmetric volatility in the Islamic capital market in Indonesia during the COVID-19 pandemic. The paper employs the symmetric analysis of the GARCH (1,1) model and the asymmetric analysis of the TGARCH (1,1) model in order to identify Islamic capital market behaviour during the first 200 days after the first COVID-19 cases were confirmed. We used the daily closing prices of the Indonesia Sharia Stock Index (ISSI). The symmetric analysis of the GARCH (1,1) model revealed that the current value of return on the ISSI does not have a significant impact on its future value. On the other hand, the TGARCH (1,1) model showed that the asymmetric parameter coefficient was positive and statistically significant. Good news and bad news does not have the same level of impact on the volatility of returns on the ISSI. Furthermore, coefficients αi and γi in the variance equation indicate that good news has a higher volatility impact than bad news. The results indicate that investors should not to worry about the bad news effect of the COVID-19 pandemic, while the government should continue the mitigation of the spread of the Coronavirus along with its economic recovery policy.

Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model

The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system.

Fractal-fractional order mathematical vaccine model of COVID-19 under non-singular kernel.

In this paper, the severe acute respiratory syndrome coronavirus (SARS-CoV-2) or COVID-19 is researched by employing mathematical analysis under modern calculus. In this context, the dynamical behavior of an arbitrary order p and fractal dimensional q problem of COVID-19 under Atangana Bleanu Capute (ABC) operator for the three cities, namely, Santos, Campinas, and Sao Paulo of Brazil are investigated as a case-study. The considered problem is analyzed for at least one solution and unique solution by the applications of the theorems of fixed point and non-linear functional analysis. The Ulam-Hyres stability condition via nonlinear functional analysis for the given system is derived. In order to perform the numerical simulation, a two-step fractional type, Lagrange plynomial (Adams Bashforth technique) is utilized for the present system. MATLAB simulation tools have been used for testing different fractal fractional orders considering the data of aforementioned three regions. The analysis of the results finally infer that, for all these three regions, the smaller order values provide better constraints than the larger order values.

A fractional order Covid-19 epidemic model with Mittag-Leffler kernel.

In this article, we are studying fractional-order COVID-19 model for the analytical and computational aspects. The model consists of five compartments including; ` ` S c ″ which denotes susceptible class, ` ` E c ″ represents exposed population, ` ` I c ″ is the class for infected people who have been developed with COVID-19 and can cause spread in the population. The recovered class is denoted by ` ` R c ″ and ` ` V c ″ is the concentration of COVID-19 virus in the area. The computational study shows us that the spread will be continued for long time and the recovery reduces the infection rate. The numerical scheme is based on the Lagrange's interpolation polynomial and the numerical results for the suggested model are similar to the integer order which gives us the applicability of the numerical scheme and effectiveness of the fractional order derivative.