Seroprevalence of SARS-CoV-2: Insights into the epidemiology of the pandemic.

AIM: This study aimed to assess the seroprevalence of SARS-CoV-2 and disease symptoms in Malakand, Pakistan. MATERIALS & METHOD: 623 samples with suspected SARS-CoV-2 were collected from different regions of Malakand and analyzed to detect SARS-CoV-2 IgG antibodies using ELISA. RESULTS: 306 (49.1%) 0 f 623 patients were anti-SARS-CoV-2 IgG reactive, with a higher prevalence in males (75%) than females (25%). In this study, we enrolled two groups, subjects working in a non-medical setting and subjects working in a medical setting. Clinical symptoms were statistically linked with SARS-CoV-2. Four weeks of follow-up analysis of IgG titers in health care workers showed an increase in IgG antibodies titer. CONCLUSION: This study gives insights into the community-based spread of SARS-CoV-2 infection, associated immunity, and herd immunity in the studied population. This study can provide insights to the government about early vaccination of this population as most of the population is not yet vaccinated.

Identifying non-nucleoside inhibitors of RNA-dependent RNA-polymerase of SARS-CoV-2 through per-residue energy decomposition-based pharmacophore modeling, molecular docking, and molecular dynamics simulation.

BACKGROUND AND OBJECTIVE: The current coronavirus disease-2019 (COVID-19) pandemic has triggered a worldwide health and economic crisis. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes the disease and completes its life cycle using the RNA-dependent RNA-polymerase (RdRp) enzyme, a prominent target for antivirals. In this study, we have computationally screened ∼690 million compounds from the ZINC20 database and 11,698 small molecule inhibitors from DrugBank to find existing and novel non-nucleoside inhibitors for SARS-CoV-2 RdRp. METHODS: Herein, a combination of the structure-based pharmacophore modeling and hybrid virtual screening methods, including per-residue energy decomposition-based pharmacophore screening, molecular docking, pharmacokinetics, and toxicity evaluation were employed to retrieve novel as well as existing RdRp non-nucleoside inhibitors from large chemical databases. Besides, molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method were used to investigate the binding stability and calculate the binding free energy of RdRp-inhibitor complexes. RESULTS: Based on docking scores and significant binding interactions with crucial residues (Lys553, Arg557, Lys623, Cys815, and Ser816) in the RNA binding site of RdRp, three existing drugs, ZINC285540154, ZINC98208626, ZINC28467879, and five compounds from ZINC20 (ZINC739681614, ZINC1166211307, ZINC611516532, ZINC1602963057, and ZINC1398350200) were selected, and the conformational stability of RdRp due to their binding was confirmed through molecular dynamics simulation. The free energy calculations revealed these compounds possess strong binding affinities for RdRp. In addition, these novel inhibitors exhibited drug-like features, good absorption, distribution, metabolism, and excretion profile and were found to be non-toxic. CONCLUSION: The compounds identified in the study by multifold computational strategy can be validated in vitro as potential non-nucleoside inhibitors of SARS-CoV-2 RdRp and holds promise for the discovery of novel drugs against COVID-19 in future.

Study of fractional order dynamics of nonlinear mathematical model

This manuscript is devoted to establishing some theoretical and numerical results for a nonlinear dynamical system under Caputo fractional order derivative. Further, the said system addresses an infectious disease like COVID-19. The proposed system involves natural death rates of susceptible, infected and recovered classes respectively. By using nonlinear analysis feasible region and boundedness have been established first in this study. Global and Local stability analysis along with basic reproduction number have also addressed by using the next generation matrix method. Upon using the fixed point approach, existence and uniqueness of the approximate solution for the mentioned problem has also investigated. Some stability results of Hyers-Ulam (H-U) type have also discussed. Further for numerical treatment, we have exercised two numerical schemes including modified Euler method (MEM) and nonstandard finite difference (NSFD) method. Further the two numerical schemes have also compared with respect to CPU time. Graphical presentations have been displayed corresponding to different fractional order by using some real data.

Estimation of emerging diagnostic parameters for Coronavirus Disease 2019 patients severity and fatality.

OBJECTIVE: To identify various emerging diagnostics parameters of coronavirus disease 2019 related to disease progression and fatality. METHODS: The cross-sectional study was conducted at Mardan Medical Complex, Khyber Pakhtunkhwah, Pakistan, from February 9, 2021, to April 21, 2021, and comprised patients of either gender aged >18 years diagnosed with coronavirus disease 2019 on the basis of polymerase chain reaction testing and who were admitted to the hospital using the World Health Organisation interim guidelines. Disease progression was categorised as mild, moderate, severe and critical, and they were monitored closely till the final outcome. Data was analysed using SPSS 26. RESULTS: Of the 408 patients, 215(52.69%) were male and 193(47.30%) were female. The overall median age of the sample was 55 years (interquartile range: 18-84 years). Symptoms included cough 92(22.54%), fever 80(19.60%), shortness of breath 78(19.60%), fatigue 60(14.70%) and loss of smell and test 52(12.74%), while 46(11.27%) were asymptomatic. Azithromycin was the most used drug 304(74.50%), while antiviral Remdesivir was given to 279(68.38%) patients and hydrocortisone to 143(35.04%). Plasma treatment was given to 55(13.48%) patients and mechanical ventilation to 87(21.32%). Compared to baseline, disease progression was mild in 72(17.64%) patients, moderate 96(23.52%), severe 98(24.01%) and critical in 89(21.81%), while no change was seen 53(12.99%) cases. Severity level was significantly associated with liver and renal function parameters (p<0.05). Overall, 47(11.51%) patients died. CONCLUSIONS: Different severity levels during hospitalisation among patients of coronavirus disease 2019 were noted, and severity level was significantly associated with liver and renal function parameters.

The Impact of COVID-19 on the Psychological Well-Being of Surgeons in Pakistan: A Multicenter Cross-Sectional Study

Introduction The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic left a profound and pervasive impact on the healthcare infrastructure on a global scale. Since its onset, the pattern of reported cases and its associated mortality had shown variability with intermittent peaks causing a significant effect on the psychological well-being of the surgeons of Pakistan. The aim of this study was to assess the effects of the COVID-19 pandemic on the mental well-being of surgeons in Pakistan. Methods This multicenter cross-sectional study was carried out to assess the impact of COVID-19 on the psychological well-being of surgeons in Pakistan. The validated Self-Reporting Questionnaire-20 (SRQ-20) tool was circulated electronically via Google Forms (Google, Inc., Mountain View, CA, USA) in the practicing surgical fraternity across all five regions of Pakistan, i.e., Sindh, Punjab, Baluchistan, Khyber Pakhtunkhwa (KPK), and Azad Jammu and Kashmir (AJK). Results This study showed that the female gender, having fewer years of working experience, non-satisfaction with the available personal protective equipment (PPE), and working in the public sector were the factors affecting the psychological well-being of surgeons during the pandemic. Conclusion Considering the continuous rise in new cases during the ongoing pandemic, the mental health of surgeons working in low- and middle-income countries (LMIC) such as Pakistan has been significantly affected. There is an undeniable need to pay close attention to their psychological well-being. Measures need to be undertaken to ensure their physical and mental health and wellness.

Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach

Neo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by NeoCoV associated with human angiotensin-converting enzyme 2 (ACE2) usage, which could lead to a high death rate and faster transmission rate in humans. The development of a NeoCoV vaccine could provide a promising option for the future control of the virus in case of human infection. In silico predictions can decrease the number of experiments required, making the immunoinformatics approaches cost-effective and convenient. Herein, with the aid of immunoinformatics and reverse vaccinology, we aimed to formulate a multi-epitope vaccine that may be used to prevent and treat NeoCoV infection. Based on the NeoCoV proteins, B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were shortlisted. Four vaccines (Neo-1–4) were devised by fusing shortlisted epitopes with appropriate adjuvants and linkers. The secondary and three-dimensional structures of final vaccines were then predicted. The binding interactions of these potential vaccines with toll-like immune receptors (TLR-2, TLR-3, and TLR-4) and major histocompatibility complex molecules (MHC-I and II) reveal that they properly fit into the receptors’ binding domains. Besides, Neo-1 and Neo-4 vaccines exhibited better docking energies of -101.08 kcal/mol and -114.47 kcal/mol, respectively, with TLR-3 as compared to other vaccine constructs. The constructed vaccines are highly antigenic, non-allergenic, soluble, non-toxic, and topologically assessable with good physiochemical characteristics. Codon optimization and in-silico cloning confirmed efficient expression of the designed vaccines in Escherichia coli strain K12. In-silico immune simulation indicated that Neo-1 and Neo-4 vaccines could induce a strong immune response against NeoCoV. Lastly, the binding stability and strong binding affinity of Neo-1 and Neo-4 with TLR-3 receptor were validated using molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). The final vaccines require experimental validation to establish their safety and effectiveness in preventing NeoCoV infections.

The Mathematical Analysis of the New Fractional Order Ebola Model

This research study focuses on the analytical behavior and numerical computation of the fractional order Ebola model. In this study we have calculated the conditions for the existence, uniqueness, and stability of the solution with the help of the fixed point results. In addition to this, we calculated the numerical solution of the fractional order smoke model with the help two-step fractional Adam’s Bashforth method using the Caputo’s fractional derivative of order μ. Furthermore, the results obtained for different orders of the fractional derivative μ have been shown graphically with the help of Matlab.

Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models.

BACKGROUND: COVID-19 is currently on full flow in Pakistan. Given the health facilities in the country, there are serious threats in the upcoming months which could be very testing for all the stakeholders. Therefore, there is a need to analyze and forecast the trends of COVID-19 in Pakistan. METHODS: We have analyzed and forecasted the patterns of this pandemic in the country, for next 30 days, using Bayesian structural time series models. The causal impacts of lifting lockdown have also been investigated using intervention analysis under Bayesian structural time series models. The forecasting accuracy of the proposed models has been compared with frequently used autoregressive integrated moving average models. The validity of the proposed model has been investigated using similar datasets from neighboring countries including Iran and India. RESULTS: We observed the improved forecasting accuracy of Bayesian structural time series models as compared to frequently used autoregressive integrated moving average models. As far as the forecasts are concerned, on August 10, 2020, the country is expected to have 333,308 positive cases with 95% prediction interval [275,034-391,077]. Similarly, the number of deaths in the country is expected to reach 7,187 [5,978-8,390] and recoveries may grow to 279,602 [208,420-295,740]. The lifting of lockdown has caused an absolute increase of 98,768 confirmed cases with 95% interval [85,544-111,018], during the post-lockdown period. The positive aspect of the forecasts is that the number of active cases is expected to decrease to 63,706 [18,614-95,337], on August 10, 2020. This is the time for the concerned authorities to further restrict the active cases so that the recession of the outbreak continues in the next month.

Designing of Potential Polyvalent Vaccine Model for Respiratory Syncytial Virus by System Level Immunoinformatics Approaches.

BACKGROUND: Respiratory syncytial virus (RSV) infection is a public health epidemic, leading to around 3 million hospitalization and about 66,000 deaths each year. It is a life-threatening condition exclusive to children with no effective treatment. METHODS: In this study, we used system-level and vaccinomics approaches to design a polyvalent vaccine for RSV, which could stimulate the immune components of the host to manage this infection. Our framework involves data accession, antigenicity and subcellular localization analysis, T cell epitope prediction, proteasomal and conservancy evaluation, host-pathogen-protein interactions, pathway studies, and in silico binding affinity analysis. RESULTS: We found glycoprotein (G), fusion protein (F), and small hydrophobic protein (SH) of RSV as potential vaccine candidates. Of these proteins (G, F, and SH), we found 9 epitopes for multiple alleles of MHC classes I and II bear significant binding affinity. These potential epitopes were linked to form a polyvalent construct using AAY, GPGPG linkers, and cholera toxin B adjuvant at N-terminal with a 23.9 kDa molecular weight of 224 amino acid residues. The final construct was a stable, immunogenic, and nonallergenic protein containing cleavage sites, TAP transport efficiency, posttranslation shifts, and CTL epitopes. The molecular docking indicated the optimum binding affinity of RSV polyvalent construct with MHC molecules (-12.49 and -10.48 kcal/mol for MHC classes I and II, respectively). This interaction showed that a polyvalent construct could manage and control this disease. CONCLUSION: Our vaccinomics and system-level investigation could be appropriate to trigger the host immune system to prevent RSV infection.

Tele-Gastroenterology Midst COVID-19 Pandemic: Patients' Perspective.

Objective The aim of this study was to assess patient satisfaction and experiences of teleconsultation in gastroenterology. Methodology Patients who had telephone consultations for gastroenterology and hepatology conditions were contacted, and their responses to questions in a structured questionnaire were recorded. The survey responses were compiled into a Microsoft Excel spreadsheet (2016 version) and formatted using qualitative representation. Results A total of 98 patients participated in this survey. The majority of the survey participants were less than 70 years of age (n=69) and more males than females (51 versus 47). Of the patients, 76 (77.5%) were satisfied with their consultations. The positive experiences, as perceived by the participants, were cost, avoidance of travel time and not having to take time off work. The negative experience perceived by the patients was lack of information regarding their condition. Conclusion Our study discovered that our patients perceived telemedicine as an effective way of catering for their healthcare needs with good satisfaction rates. This can be used as an adjunct to the traditional face-to-face appointment system to provide uninterrupted healthcare to gastroenterology and hepatology patients during and after the COVID-19 pandemic.

Stability analysis and optimal control of Covid-19 pandemic SEIQR fractional mathematical model with harmonic mean type incidence rate and treatment.

In the present work, we investigated the transmission dynamics of fractional order SARS-CoV-2 mathematical model with the help of Susceptible S ( t ) , Exposed E ( t ) , Infected I ( t ) , Quarantine Q ( t ) , and Recovered R ( t ) . The aims of this work is to investigate the stability and optimal control of the concerned mathematical model for both local and global stability by third additive compound matrix approach and we also obtained threshold value by the next generation approach. The author's visualized the desired results graphically. We also control each of the population of underlying model with control variables by optimal control strategies with Pontryagin's maximum Principle and obtained the desired numerical results by using the homotopy perturbation method. The proposed model is locally asymptotically unstable, while stable globally asymptotically on endemic equilibrium. We also explored the results graphically in numerical section for better understanding of transmission dynamics.

Theoretical and numerical analysis of novel COVID-19 via fractional order mathematical model.

In the work, author's presents a very significant and important issues related to the health of mankind's. Which is extremely important to realize the complex dynamic of inflected disease. With the help of Caputo fractional derivative, We capture the epidemiological system for the transmission of Novel Coronavirus-19 Infectious Disease (nCOVID-19). We constructed the model in four compartments susceptible, exposed, infected and recovered. We obtained the conditions for existence and Ulam's type stability for proposed system by using the tools of non-linear analysis. The author's thoroughly discussed the local and global asymptotical stabilities of underling model upon the disease free, endemic equilibrium and reproductive number. We used the techniques of Laplace Adomian decomposition method for the approximate solution of consider system. Furthermore, author's interpret the dynamics of proposed system graphically via Mathematica, from which we observed that disease can be either controlled to a large extent or eliminate, if transmission rate is reduced and increase the rate of treatment.

INDIGENOUS KNOWLEDGE BASED HERBAL MEDICINE FOR CORONA (COVID-19) TREATMENT

Current Corona virus Covid-19 crisis has infected more than 1.8 million humans in last three months and it is increasing exponentially every day. Some of the allopathic medicines (Cholroquine, Lopinover, Oseltamivir and Retenover etc.) used for other purposes like Malaria have shown good results in Corona. Plants species were reported to be effective in coronavirus (SARS-CoV), which causes a serious type of pneumonia. In similar way, Plant materials are a good source of bioactive compounds/phytochemicals that can be utilized not only for strengthening our immune system but also killing the pathogens. Due to chemical diversity and natural affability of natural products, either in the form of standardized extract or as a pure compound based on ethnopharmacological properties plays a s significant role in new drug development. Though, we have summarized medicinal plants that have a role in anti-viral activities, but there is an immediate need to find out more and more medicinal plants for anti-viral activities.

Multiepitope Subunit Vaccine Design against COVID-19 Based on the Spike Protein of SARS-CoV-2: An In Silico Analysis.

The global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID-19, has resulted in a negative impact on human health and on social and economic activities worldwide. Researchers around the globe need to design and develop successful therapeutics as well as vaccines against the novel COVID-19 disease. In the present study, we conducted comprehensive computer-assisted analysis on the spike glycoprotein of SARS-CoV-2 in order to design a safe and potent multiepitope vaccine. In silico epitope prioritization shortlisted six HLA I epitopes and six B-cell-derived HLA II epitopes. These high-ranked epitopes were all connected to each other via flexible GPGPG linkers, and at the N-terminus side, the sequence of Cholera Toxin ß subunit was attached via an EAAAK linker. Structural modeling of the vaccine was performed, and molecular docking analysis strongly suggested a positive association of a multiepitope vaccine with Toll-like Receptor 3. The structural investigations of the vaccine-TLR3 complex revealed the formation of fifteen interchain hydrogen bonds, thus validating its integrity and stability. Moreover, it was found that this interaction was thermodynamically feasible. In conclusion, our data supports the proposition that a multiepitope vaccine will provide protective immunity against COVID-19. However, further in vivo and in vitro experiments are needed to validate the immunogenicity and safety of the candidate vaccine.

Haar wavelet collocation approach for the solution of fractional order COVID-19 model using Caputo derivative

This article is devoted to study a compartmental mathematical model for the transmission dynamics of the novel Coronavirus-19 under Caputo fractional order derivative. By using fixed point theory of Schauder’s and Banach we establish some necessary conditions for existence of at least one solution to model under investigation and its uniqueness. After the existence a general numerical algorithm based on Haar collocation method is established to compute the approximate solution of the model. Using some real data we simulate the results for various fractional order using Matlab to reveal the transmission dynamics of the current disease due to Coronavirus-19 through graphs.

Anti-COVID-19 multi-epitope vaccine designs employing global viral genome sequences.

BACKGROUND: The coronavirus SARS-CoV-2 is a member of the Coronaviridae family that has caused a global public health emergency. Currently, there is no approved treatment or vaccine available against it. The current study aimed to cover the diversity of SARS-CoV-2 strains reported from all over the world and to design a broad-spectrum multi-epitope vaccine using an immunoinformatics approach. METHODS: For this purpose, all available complete genomes were retrieved from GISAID and NGDC followed by genome multiple alignments to develop a global consensus sequence to compare with the reference genome. Fortunately, comparative genomics and phylogeny revealed a significantly high level of conservation between the viral strains. All the Open Reading Frames (ORFs) of the reference sequence NC_045512.2 were subjected to epitope mapping using CTLpred and HLApred, respectively. The predicted CTL epitopes were then screened for antigenicity, immunogenicity and strong binding affinity with HLA superfamily alleles. HTL predicted epitopes were screened for antigenicity, interferon induction potential, overlapping B cell epitopes and strong HLA DR binding potential. The shortlisted epitopes were arranged into two multi-epitope sequences, Cov-I-Vac and Cov-II-Vac, and molecular docking was performed with Toll-Like Receptor 8 (TLR8). RESULTS: The designed multi-epitopes were found to be antigenic and non-allergenic. Both multi-epitopes were stable and predicted to be soluble in an Escherichia coli expression system. The molecular docking with TLR8 also demonstrated that they have a strong binding affinity and immunogenic potential. These in silico analyses suggest that the proposed multi-epitope vaccine can effectively evoke an immune response.

Designing Multi-Epitope Vaccines to Combat Emerging Coronavirus Disease 2019 (COVID-19) by Employing Immuno-Informatics Approach.

A recent pandemic caused by a single-stranded RNA virus, COVID-19, initially discovered in China, is now spreading globally. This poses a serious threat that needs to be addressed immediately. Genome analysis of SARS-CoV-2 has revealed its close relation to SARS-coronavirus along with few changes in its spike protein. The spike protein aids in receptor binding and viral entry within the host and therefore represents a potential target for vaccine and therapeutic development. In the current study, the spike protein of SARS-CoV-2 was explored for potential immunogenic epitopes to design multi-epitope vaccine constructs. The S1 and S2 domains of spike proteins were analyzed, and two vaccine constructs were prioritized with T-cell and B-cell epitopes. We adapted a comprehensive predictive framework to provide novel insights into immunogenic epitopes of spike proteins, which can further be evaluated as potential vaccine candidates against COVID-19. Prioritized epitopes were then modeled using linkers and adjuvants, and respective 3D models were constructed to evaluate their physiochemical properties and their possible interactions with ACE2, HLA Superfamily alleles, TLR2, and TLR4.

Socioeconomic impact of COVID-19 pandemic: Evidence from rural mountain community in Pakistan

This study examines the trend of the spread of coronavirus disease (COVID-19) pandemic in Pakistan and to explore the community perception of the socioeconomic impact of COVID-19 pandemic in a rural mountain area of Pakistan. An online survey was conducted through snowball sampling technique and total 367 people participated in the survey. The results of the study show that COVID-19 cases spiked amid ease of lockdown in the country and the spread of novel coronavirus pandemic has significant socioeconomic impact on the lives of mountain communities in Gilgit-Baltistan. Financial uncertainty, decrease in income, fear of job loss, and food insecurity are some major challenges that mountain communities face due to outbreak of coronavirus in the region. The results further show that lack of community cooperation with government agencies, lack of awareness about the severity of coronavirus, and insufficient COVID-19 testing kits are the major factors that caused the spread of coronavirus cases. This study suggests that the short-, medium-, and long-term policies are required to mitigate the consequences of this pandemic and to revitalize the mountain economy of Gilgit-Baltistan, and in this regard, this study provides baseline information for policy-makers and practitioners to devise such demanding policies.