Are findings from prognostic models that are derived from randomized clinical trial (RCT) data generalizable to the real-world setting?In this cohort study of 2113 patients with metastatic castration-resistant prostate cancer, predictions from models trained on data from RCTs could not be generalized to the real-world setting with data from electronic health records (EHRs). Patient populations and feature availability differed substantially between the RCT–derived models and the EHR–derived models; the best-performing EHR–derived model included only 25 of the 101 variables included in the RCT–derived model and achieved adequate performance, as measured by integrated area under the curve.This study’s findings indicate that prognostic models derived from RCT data provide insights into factors associated with mortality; however, the direct application of these models to real-world data requires local optimization, including feature reduction strategies and evaluation of algorithmic bias. Randomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings.To assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs).The RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020.Patients who received treatment for metastatic CRPC.The primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics.P < .001).Among 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities  compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of which were among the top 10 features in the DREAM challenge RCT model. The best-performing EHR-trained model included only 25 of the 101 variables included in the RCT-trained model. The performance of the RCT-trained and EHR-trained models was adequate in the EHR cohort ; model optimization was associated with improved performance of the best-performing EHR model . The EHR-trained model classified 256 patients as having a high risk of mortality and 256 patients as having a low risk of mortality (hazard ratio, 2.7; 95% CI, 2.0-3.7; log-rank In this study, although the RCT-trained models did not perform well when applied to real-world EHR data, retraining the models using real-world EHR data and optimizing variable selection was beneficial for model performance. As clinical evidence evolves to include more real-world data, both industry and academia will likely search for ways to balance model optimization with generalizability. This study provides a pragmatic approach to applying RCT-trained models to real-world data. This cohort study assesses the performance of a prognostic survival model derived from randomized clinical trial data when the model is applied to real-world data from the electronic health records of patients with metastatic castration-resistant prostate cancer. To address this need, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer was held from March 16 to July 27, 2015. For this challenge, data from 4 phase 3 RCTs were made available in a crowdsourced contest to build the best survival model for patients with metastatic CRPC.18 The best-performing team used an ensemble of penalized Cox proportional hazards regression models to predict survival with a time-dependent integrated area under the curve (iAUC) of 0.768.Although a number of strategies exist for managing metastatic CRPC, the optimal selection and sequencing of these therapies is an active area of research.10 there is an increasing need to understand the extent to which RCT evidence can be applied to routine clinical care. To this end, we developed a pragmatic approach to implement and evaluate a prognostic model for survival among patients with metastatic CRPC that was based on the DREAM challenge RCT-trained model and applied to real-world EHR data from patients who received treatment at a comprehensive cancer center. Our hypothesis was that the RCT model would have lower performance with real-world data and that local optimization of the RCT-trained model could improve performance.As regulatory changes require better use of real-world evidence for clinical assertions,18 This data set included the 3 RCTs of patients with metastatic CRPC that were used to train the models in the competition. Data from the fourth RCT, which was used to validate the models in the competition, were not available. Data included patient demographic characteristics, laboratory values, procedures, diagnoses, and drug prescriptions. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies. The study was approved by the institutional review board of Stanford University with a waiver of informed consent because of its retrospective nature.The DREAM challenge RCT data were obtained through the Project Data Sphere online platform.19 Patients with prostate cancer between January 1, 2008, and December 31, 2019, were identified using diagnostic codes for prostate cancer from the International Classification of Diseases, Ninth Revision, Clinical Modification (codes 185 and 233.4) and the International Classification of Diseases, Tenth Revision, Clinical Modification (code C61). A total of 513 patients with metastatic CRPC were identified using 2 methods (20 Patients were excluded if they did not have a recorded period during which they received hormone therapy (which is an indication of recurrent or metastatic disease) or if they did not have at least 3 recorded PSA values during receipt of hormone therapy. Hormone therapy was defined for every patient as the period of intake of specific drugs.Real-world data were obtained from the EHRs of a comprehensive cancer center at a tertiary care academic medical center; the process is described in detail elsewhere. methods . For met methods . PatientIn the EHR data set, we identified 91 of the 101 variables included in the DREAM challenge RCT-derived model. Seven of the 91 variables were excluded because of missing data .18 We then trained the model on 90% of the EHR data set.To predict survival, we developed a penalized Cox proportional hazards regression model with elastic-net regularization based on the DREAM challenge RCT-derived model using 10-fold cross-validation. We first trained the model on the RCT data using the variables selected for the DREAM challenge model. Twelve skewed variables were log-transformed, and 0 values were replaced with the smallest non-0 value to ensure finite transformed values . We explored the performance of models containing different numbers of top variables. In addition, we explored the accuracy of the model with the optimal features detected by the recursive feature elimination method. Based on results from the best-performing model, we divided the patients with the median model-predicted risk score into low-risk and high-risk mortality groups and assessed survival.To identify the variables most predictive of survival, we developed a correlation-based feature reduction strategy.To evaluate the models’ performance, we compared their mean 10-fold time-dependent iAUC between 6 and 30 months on 90% of the EHR data set. We also used the 10% of the data set (test set) that was not previously used for model development to assess the best-performing EHR model and compare it with the RCT-trained model.21For the 2 models trained on different data sets, we excluded patient race from the feature set to assess the impact of race disparities with regard to risk scores. For the DREAM challenge RCT model and the best-performing EHR model, we obtained risk scores generated for each patient-year. We plotted the function of mean comorbidity scores or PSA values against model risk score by different races.t tests for parametric data and analysis of variance for parametric data, and χ2 and Fisher exact tests were used for categorical variables. The Wilcoxon rank sum test for dependent samples was used to calculate the significance between the iAUCs. Kaplan-Meier estimators and log-rank statistics were used to assess survival. All statistical tests were 2-sided with a significance threshold of P ≤ .05. Analyses were performed using R software, version 4.0.3 . Data were analyzed from March 23, 2018, to October 22, 2020.Patient demographic characteristics were compared using unpaired Of the 17 322 patients with prostate cancer in the EHR, 513 patients (3.0%) with CRPC were identified , and no P = .46), primary care encounters , or oncological encounters  , Asian (41 patients [2.6%] vs 88 patients [17.2%]), and older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities  compared with those in the EHR cohort. The number of encounters did not differ by race for all face-to-face health care encounters . Among the remaining 91 variables, 7 were not consistently captured in the EHRs and were removed because of missingness (eTable 3 in the P < .001).For local optimization, the model was retrained using the 84 available variables in the EHR data set, achieving a mean (SD) iAUC of 0.762 (0.106). Using our feature reduction strategy, we removed 24 correlated variables to develop a model with 60 variables. Additional feature optimization using filtering feature selection produced multiple models containing between 10 and 60 features, with mean (SD) iAUC values between 0.775 (0.098) and 0.792 (0.097). Using recursive feature elimination, our best-performing EHR model included 25 variables and achieved a mean (SD) iAUC of 0.792 (0.097) . To valiAmong the top 10 predictive features (eTable 5 in the P < .001).Using the best-performing EHR-trained model, we classified patients into high-risk and low-risk mortality groups using a median-split method . The difPatient race was not significant as a predictor of survival in either the RCT or EHR cohort (eFigure in the 10 In this study, we applied an RCT-trained prognostic model for metastatic CRPC to real-world data and provided a framework for evidence translation. The direct application of the RCT-trained model to real-world data did not perform well; substantial differences in patient demographic characteristics and disease severity are likely factors underlying the model’s low performance. However, through local model optimization, including feature reduction strategies, we highlighted the ways in which the RCT findings can produce a model using real-world data that has good discrimination abilities. These successful results have the potential to change the outlook on the secondary use of both RCT and EHR data, increasing the ability to understand the impact of clinical therapies for a broader and more generalizable population seeking routine clinical care for the disease being studied.Translating RCT-derived clinical data to real-world settings is a priority for advancing clinical practice, as exemplified by the 21st Century Cures Act.18 While many of these variables have been previously reported to have an association with survival in laboratory and clinical trials, our work highlights the complexity of translating RCT-derived models to the real-world setting, as data on many of the prognostic factors used in the RCT-derived model are not routinely collected in real-world settings.We developed a prognostic model based on the DREAM challenge RCT-trained model, which accurately discriminated between low-risk and high-risk patients. The top predictive features from our model differed slightly from the RCT model based on the availability of specific variables in the EHRs. In our model, the presence of high alkaline phosphatase levels, high aspartate aminotransferase levels, and high PSA values at the time of CRPC diagnosis were predictive of a worse prognosis. These variables were also identified by the DREAM challenge’s best-performing team.25 Hence, the accuracy of the EHR model improved as feature reduction strategies were applied; the optimal model included only 25 of the 101 features included in the RCT-derived model. These feature reduction strategies are important when using real-world data because data collection is expensive for the health care system, requiring time as well as computational and financial resources, particularly if many of the identified features do not have substantial implications for model performance. Therefore, developing a model with a minimal number of features allows us to be more realistic about its implementation in a hospital setting.26 The extent of EHR data missingness highlights the importance of feature reduction strategies, and this study provides a framework to account for the missingness and inaccuracy of real-world data to produce relevant prognostic models at the point of care.The EHR-trained models that were developed using all available features had good discrimination in identifying patients with metastatic CRPC with a high risk of mortality within 5 years. Data collected in RCTs are likely superior to data collected in real-world settings with regard to statistical noise, accuracy, and missingness, as trained administrators collected these data using protocol guidelines. Furthermore, RCT data may better capture patient-reported outcomes and survival, whereas advanced technologies are needed to identify and extract this information in EHRs.8 we found substantial differences in racial characteristics between the RCT and EHR cohorts. Most patients in the RCT cohort were White, and few patients from racial or ethnic minority groups were included. This composition differed from the population seeking care in the routine setting, which had substantially higher proportions of Black, Hispanic, and Asian patients. Across the 4 RCTs, fewer than 1% of study participants were Hispanic or Latino. This finding suggests that the RCT population is not representative of the average patient seeking care at a comprehensive cancer center, which has been noted in other RCTs of patients with prostate cancer.8 However, because potential biases in the collection of race information could exist, both in the original RCT data and in our EHR data, robust examination of bias and algorithmic fairness is warranted when either type of data is used for predictive models.27 By extending the results of RCT models to EHRs, one can optimize the inclusion of diverse populations who are eligible for an intervention but may not meet the strict inclusion criteria of an RCT. This expansion to diverse populations may help mitigate potential bias.28Consistent with existing literature,4 Patients included in the RCT cohort also had a lower number of comorbidities compared with patients in the EHR cohort. Because increasing age and the complexity of a patient’s condition are associated with survival,29 our study highlights the importance of optimizing RCT-derived models for real-world settings, which include patients with more complex conditions.The RCT data also differed from real-world data with regard to other patient demographic and clinical characteristics. The RCT populations in the DREAM challenge included men who were predominantly aged 65 to 75 years, with substantially fewer patients older than 75 years compared with the real-world setting, which is similar to other findings.21 This finding is particularly true when training data are not representative of the population to which the models will be applied. To investigate bias in the metastatic CRPC prognostic models, we evaluated the association between the model-derived risk scores, Charlson Comorbidity Index scores, and PSA values. In both data sets, our results indicated that Black patients needed higher Charlson Comorbidity Index scores and PSA values to receive a risk score that was equivalent to that of non-Hispanic White patients. In the EHR cohort, race and ethnicity were not associated with a higher number of health care encounters, so other factors are likely associated with these biases. When models are deployed at the point of care, such biases can inform resource allocation. This study’s findings highlight the necessity of evaluating population representativeness and potential biases.30 The transparency in such information provides the end user with an opportunity to mitigate such biases and appropriately deploy models across populations.The goal of all clinical predictive models is to help clinical decision-making; hence, it is important to investigate potential bias before deploying models at the point of care. Racial bias in predictive models is an emerging issue, and research suggests that some models may be associated with perpetuating racial disparities in health care.10 Emerging findings highlight the need to understand the challenges and opportunities of using these data for evidence generation,31 which is important for future pragmatic approaches toward clinical trials. We present a timely and innovative approach to validate criterion-standard evidence from RCTs using routinely collected clinical data that may augment the implementation of data-driven decisions at the point of care. Identifying the appropriate treatment for each patient is important, particularly as patients’ values and shared decision-making become a greater focus in clinical care; providing tools to better counsel patients on their potential individual risk is needed. While RCTs are critiqued for being nongeneralizable and expensive, they are still and will likely remain the criterion standard for clinical assertions. Real-world data can be used to augment and validate RCT findings, as we have done in this study, and can expand the application of RCT-based models to broader patient populations.In the shifting landscape of clinical criterion standards, the increasing use of real-world data is likely.This study has several limitations. First, in real-world settings, patients seek care at their convenience; this process may differ substantially from those used in RCTs, which adhere to strict protocols, and may impact patient outcomes. However, real-world data better reflect practice standards and the ways in which the general patient population seeks care. Second, there are gaps of information regarding the receipt of intermittent hormone therapy, and the exact dates of a given prescription may not always be complete. However, linking EHRs to registry data can fill these gaps. Despite these shortcomings, this study provides guidance for the use of RCT-derived data in a real-world setting.In this study, we present a framework to apply clinical trial findings more broadly to patients with the disease being studied; this framework focuses on local optimization of an RCT-based predictive model for real-world data. We found that a machine learning model trained on an RCT data set did not perform well when applied directly to real-world data. However, through feature reduction strategies, we developed a model that was more attuned to our health care setting and that had better diversity and a feasible selection of top features. The findings of any prognostic model are only useful to the patient and clinician if they can be implemented at the bedside. While RCTs may be superior for model building with regard to data quality and integrity, it is feasible to streamline the translational research pipeline using real-world data to better inform decision-making. More research is warranted to understand the ways in which our findings can be translated into a clinical decision aid that is personalized to an individual patient’s health record.


Before the emergence of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the causative agent of the current COVID-19 (coronavirus disease 2019) pandemic . The sciCells, Zika virus and host interactions, thirteen articles that address diverse aspects of ZIKV–host interactions from basic science to clinical research are presented  in E protein that is associated with ZIKV infectivity. To illustrate the importance of these E protein residues in ZIKV infection, the authors swapped them between the Brazilian epidemic strain BR15 and placed them with the corresponding amino acids from the pre-epidemic African strain MR766. The BR15 virus containing the E-152/156/158 residues from MR766 was less infectious in vitro than the parental virus. In contrast, the MR766 virus containing amino acid residues 152/156/158 from the Brazilian strain was more infectious. As changes to this region of E protein did not affect virus binding to host cells, virus-induced cell death, or the interferon response, the authors speculated that they may be important for infectivity by promoting viral membrane fusion inside infected cells. These findings may contribute to the design of innovative strategies to control ZIKV infection at early stages.Bos et al.  showed tFinally, regarding T-cell immunity to ZIKV, the pre-existing immunity to the related flavivirus, dengue virus (DENV), has been discussed as a risk factor for immunopathological responses . Pardy aWe hope that this Special Issue will be of wide interest, particularly to those researchers focused on different aspects of ZIKV–host interactions. It is expected that findings from these studies will contribute to a better understanding of the vector and human host cells interacting with ZIKV, and will serve as the basis for novel diagnostics, antiviral therapeutics, and vaccines.


Health care professionals exposed to coronavirus disease 2019 (COVID-19) are facing high levels of stress.The aim was to evaluate the quality of sleep (QoS) and health-related quality of life (HRQoL), among health care professionals treating patients with COVID-19, as well as quantifying the magnitude of symptoms of depression and levels of anxiety.We included 201 health care professionals in a cross-sectional, web-based study by applying 7-item Generalized Anxiety Disorder (GAD-7) Scale, Zung Self-rating Depression Scale, 36-item Health Survey of the Medical Outcomes Study Short Form (SF36), Pittsburgh Sleep Quality Index (PSQI) and additional survey constructed for the purpose of the study.p < .01) and lower scores on mental health (MH) subscale on SF36 questionnaire  were independent predictors of the higher PSQI score . Higher scores on GAD-7  and worse self-perceived mental status  were independent predictors of the lower SF36 scores .Health care workers who treated COVID-19 patients were more afraid of becoming infected or of transmitting the infection to a family member with a significantly low self-assessment of their mental status. Poor QoS and HRQoL correlated with high health anxiety and severe depressive symptoms and several demographic characteristics. Multiple linear regression analysis showed that higher scores on GAD-7 (beta = .71, The major MH burden of health care professionals treating infected patients during the COVID-19 pandemic indicates that they need psychological support. In March 2020, a novel infective disease caused by a coronavirus disease 2019 (COVID-19) was first reported in Serbia, after becoming a global health emergency. Series of restrictive measures were introduced, and one of them was the overall organization of work in the form of ‘Work From Home’, which could not be applied to health care professionals.Health care professionals reported having the moral obligation to treat patients and save lives . They weThe aim of the current study was to evaluate HRQoL and QoS among health care professionals treating patients with COVID-19, as well as quantifying the magnitude of symptoms of depression and levels of anxiety and by analyzing potential risk factors associated with these symptoms.To prevent the spread of infection, we performed a cross-sectional, web-based study after 20 days of the establishment of Temporary Hospitals to combat the COVID-19 pandemic, to collect data. Verbal informed consent was provided by all survey participants before their enrollment. The survey was anonymous, and confidentiality of information was assured. The study began by uploading the electronic version of the survey to the participants’ emails with a brief explanation of the survey and its purpose. Clicking the link automatically opened the survey. They were allowed to terminate the survey at any time. This web-based survey was completely voluntary and non-commercial.The study was conducted in accordance with the approval from the clinical research ethics committee of the local review board and all applicable guidelines, including the Basics of Good Clinical Practice, the Declaration of Helsinki and the Law on Health Care of the Republic of Serbia.Health care professionals who worked in Clinical Center Nis, Serbia, were asked to participate. During the COVID-19 pandemic in this Institution, four wards were designed to work with COVID-19 positive patients. Health care professionals assigned to work in a Temporary Hospital worked in a ward with mild to moderate COVID-19 positive patients with the highest level of protection and with full equipment in shifts to a maximum of 4 hours and a maximum of eight shifts per week. By the time the article was written, more than 1,000 patients were treated in Temporary Hospitals in Nis, Serbia, a larger number were in wards, where health care professionals in our sample were deployed. Health care professionals working on other departments during the COVID-19 pandemic in Clinical Center Nis and not dealing with COVID-19 patients were also asked to participate. They were sex- and age-matched, and were used as a control group. Excluding factors were the existence of sleep–wake disorders or any other psychiatric disorders prior to COVID-19, which included any changes in mental status due to the need for psychiatric care before the pandemic, as well as the use of therapy for the psychiatric disorders. We used random sampling to reduce risk of bias. Approximately, 300 health care professionals worked with COVID-19 positive patients during the pandemic, and to 181, the online survey was sent. Total number of 118 was included in the study; 49 were excluded due to exclusionary factors and others refused to participate or did not answer on our call. In other departments, there were approximately 1,200 health care workers, a survey form was sent to those who had an email address available to us at that time (138 of them). Total number of 83 health care workers was included in the study; others were excluded due to exclusion factors or did not answer on our call.The survey consists of the following parts: part incorporating demographic characteristics, part of COVID-19-related data constructed for the purposes of this study and part consisting of standardized and validated questionnaires in Serbia.Basic demographic data included occupation (doctor or nurse), gender , age (years), marital status , living area , an education level , presence of other chronic diseases that demand chronic use of certain drug therapies, number of children, partner’s employment status, presence of psychiatric disorders in the family and so on. COVID-19-related data included questions about attitudes toward current needs, overall satisfaction with support provided and importance of psychosocial support, whether they or their family members or close friends were diagnosed with COVID-19, questions about the attitude and respect toward restrictive measures and the attitude of others toward them given their exposure to the virus, including being stigmatized.Self-reported participants’ MH status was measured by 7-item Generalized Anxiety Disorder (GAD-7) Scale, Zung Self-rating Depression Scale (SDS), 36-item Health Survey of the Medical Outcomes Study Short Form (SF36) and Pittsburgh Sleep Quality Index (PSQI).The GAD-7 scale (range 0–21) is used to assess the severity of symptoms of anxiety. GAD-7 results are interpreted as follows: normal (0–4), mild (5–9), moderate (10–14) and severe (15–21) anxiety. Using the threshold score of ⩾10 (high anxiety), the GAD-7 has a sensitivity and specificity of several anxiety and stress-related disorders, but further evaluation is recommended . The GADThe SDS is a 20-item self-report questionnaire used as a screening tool for depression, and the total score indicated the existence and intensity of existing depressive symptoms . The totThe SF36 Health Survey is a 36-item self-report survey that assesses eight domains of physical health and MH, ranging from 0 to 100, where the highest score indicates the optimal HRQoL and the lowest score indicates the poorest HRQoL. The eight domains are physical functioning (PF), role limitations because of physical health problems ), bodily pain (BP), general health (GH) perceptions, vitality (VT), social functioning (SF), role limitations because of emotional problems ) and general MH . The firPSQI QoS questionnaire consists of 19 self-rated questions. These 19 questions are grouped into seven groups, each scored from 0 to 3. Obtained global PSQI score is from 0 to 21, where a higher score indicates the lower QoS. Components of the questionnaire measure QoS, duration and latency of sleep, common efficacy of sleep and functionality during the day. It assesses a 1-month interval and provides data useful in both clinical and scientific works. In addition to assessing the QoS, it provides a clinically useful evaluation of a variety of factors that might affect the QoS. It could be used as a tool for measuring the interaction of sleep disturbances and levels of anxiety, the intensity of depressive symptomatology, as well as the relationship between sleep quality and other demographic characteristics . It is ap < .05 were considered statistically significant (two-sided tests). Numerical data are presented as medians and interquartile range (IQR) for nonparametric data and as mean ± standard deviation (SD) for parametric data. The multivariate logistic regression models were performed to explore potential influence factors for worse QoS and quality of life (QoL).All data were statistically processed by the IBM SPSS statistical software (version 21) for the Windows operative system. First, descriptive analyses were conducted to describe the demographic characteristics and COVID-19-related data in health care workers who treated COVID-19 patients. The Mann–Whitney test was used to compare continuous variables between two groups, and the Kruskal–Wallis test was used to compare more than two groups. Correlations were assessed using Pearson’s correlation coefficients or Spearman’s correlation coefficients. Values of The demographic characteristics of our participants are presented in p < .01). Also, the self-assessment of their mental status was significantly worse than in the other group of health workers (p < .05). There was no significant difference compared with other examined parameters.Scores on obtained questionnaires are presented in p < .01) and lower scores on MH subscale on SF36 questionnaire  were independent predictors of the higher PSQI score . Regarding lower total score on SF36, higher scores on GAD-7  and worse self-perceived mental status  were independent predictors of the lower SF36 scores .A statistically significant correlation between variables and scores on SF36 and PSQI is presented in This cross-sectional survey enrolled 201 health care professionals and revealed various reasons for concern about the MH of physicians and nurses working in Temporary Hospitals and treating infested patients during the COVID-19 pandemic. Our concern was also based on previous research data about the most prevalent long-term psychological condition among those at the front line in previous epidemics like SARS and MERS, as well as during COVID-19 pandemic in China .High-stress situations like pandemic are followed by psychological responses. Although almost 70% assessed the restrictive measures during the pandemic as restrictive, the vast majority adhered to them. Facts and concerns about the mode of transmission and spread of potentially fatal virus bring people into a state of heightened vulnerability. Health care workers, in particular, are not feeling comfortable whenever they are not able to recognize, explain, predict and control disease processes, which have an impact on their MH . In moreHealth care professionals who treated COVID-19 patients had poorer QoS which correlated with high health anxiety and severe depressive symptoms, female gender, being a nurse as well as poor HRQoL. Higher health anxiety and lower mental HRQoL were independent predictors of the poorer QoS. Previous findings support our results, that health care professionals were anxious because of close and frequent contact with positive patients and consequently had lower QoS when committing themselves to provide high-quality medical care for patients . Female High-exposure physicians and nurses had lower QoS, although their work engagement provided them with rest, which indicates that they were not able to rest adequately due to the chronic stress and psychological distress, as previously described . Some stAlthough health care workers around the world have received an updated guideline on how to handle the patients with COVID-19 and an adequate supply of medical protective items , they worked with enormous pressure . They woBesides the fact that less than 20% of health care professionals who treated COVID-19 patients assessed their current mental state as excellent, and more than 50% reported significant worsening with the negative impact of a pandemic, only 7.5% indicated the need for professional psychological support. Therefore, we believe that it is necessary to organize and to provide adequate psychological support to health care providers during the future life-threatening infectious disease outbreaks, as well as recovery programs aimed at empowering resilience and psychological well-being.This study has several limitations. First, data and relevant analyses presented here were derived from a cross-sectional study and by self-report tools, limited to the COVID-19 outbreak and voluntary basis. Second, it was not possible to assess the participation rate, since it is unclear how many subjects received the link for the survey. Third, due to the sudden occurrence of the disaster, we were unable to assess an individual’s psychological conditions before the outbreak; we were just sticking to the exclusionary factors. Our main focus was on their subjective assessment of QoS and HRQoL in whom no sleep–wake disorder or any psychiatric disorder has been diagnosed until then. Fourth, larger sample size is needed to verify the results.In summary, our study revealed a major MH burden of health care professionals treating infected patients in Serbia during the COVID-19 pandemic. High risk of displaying psychological issues followed by low QoS and HRQoL was observed among female health care professional nurses, who are married and have children, working in the front line at Temporary Hospitals dealing with positive patients. Better personalized MH protection, support and care should be provided to high-exposed health care professionals during future life-threatening infectious disease outbreaks from psychotherapists and psychiatrists.


From the begging months of 2020 a severe acute respiratory syndrome coronavirus  caused a devastating global outbreak. At present, the diagnosis of coronavirus disease 2019 (COVID-19) is made through a nasopharyngeal swab based on reverse transcription polymerase chain reaction (RT-PCR) technique. However, some recent studies suggested the possible role of oral fluids and saliva in the detection of SARS-CoV-2. The purpose of this scoping review is evaluating the available evidence regarding the efficacy of saliva as a diagnostic specimen in COVID-19 patients.A systematic literature review of six databases  was carried out without any restrictions on date of publication to identify the reliability of saliva as a diagnostic specimen for detection of SARS-CoV-2 in suspected patients.Nine eligible articles were included in this review based on our described method. All the included studies are based on clinical surveys among patients with confirmed SARS-CoV-2 infection. Most of studies included in this review, reported that there is no statistically significant difference between nasopharyngeal or sputum specimens and saliva samples regarding viral load.Despite limitations of this study, the findings of this review suggest that the use of self-collected saliva as a non-invasive specimen has proper accuracy and reliability regarding detection of SARS-CoV-2 based on RT-PCR technique. Betacoronavirus genus lineage B [During December 2019, a SARS-CoV-like coronavirus, the 2019-novel-coronavirus (2019-nCoV) was recognized in a cluster of patients with community acquired pneumonia in Wuhan, Hubei Province, China [ineage B .Previous studies showed that the SARS-CoV-2 can be efficiently transmitted between people. In this regard cases of familial clustering have been documented . As of MReferring to current emergency situation, preparing accurate and fast diagnostic testing methods of SARS-CoV-2 is very important with the aim of controlling the outbreak in the community and in hospitals . At the Based on these issues, finding a safe alternative method is crucial. One of the non-invasive methods for collecting the specimens is asking patients to spit into a sterile bottle . It shouPrevious studies showed that saliva has a high concordance rate of > 90% with nasopharyngeal specimens in the detection of respiratory viruses, including coronaviruses , 12. It This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations for transparent reporting of systematic reviews and meta-analyses. We did not register the review protocol because we anticipated the very limited available evidence on the topic and due to the urgency of the matter.Following the PRISMA guidelines , a focusStudies selected for review included original, full-text articles published in English, evaluating saliva as diagnostic specimen for detecting COVID-19 patients. All letters, narrative reviews, animal studies, and duplicate articles were excluded. The search strategy was not restricted by the publication date. Hence, all of the related evidence up to May 3, 2020, that met the inclusion criteria was assessed.A search strategy was developed to collect all scientific papers. MEDLINE (PubMed), Scopus, The Cochrane Central Register of Controlled Trials (CENTRAL), Science Direct, Web of Science and Google scholar were systematically searched up to May 3, 2020 without any restrictions on language or date of publication.The structured search strategy used was as follows:  OR salivary)) AND ((((((((((Novel coronavirus) OR Novel-coronavirus) OR nCoV) OR 2019 nCoV) OR 2019-nCoV) OR COVID 19) OR COVID-19) OR Wuhan coronavirus) OR Wuhan pneumonia) OR SARS-CoV-2).http://clinicaltrials.gov, http://www.centerwatch.com/, and http://www.clinicalconnection.com). Non-scientific commentaries, reports, letters and news articles were excluded from the analysis.Following the completion of search, the references in the papers that were selected, and also reviewed to include additional articles that were not found in the original electronic search. A number of websites that list ongoing clinical trials were also searched (Two authors (OF and MD) independently searched through the literature. The two sets of papers were then compared. Disagreements were resolved by discussion or, if necessary, by including a third researcher (AK) to make the final decision. Duplicate articles were excluded.One investigator (OF) extracted the data, and a second investigator (AK) checked the retrieved data independently for completeness and accuracy. The final set of selected papers and the relevant data based on our main question were summarized in Table A total of 305 publications were found as search results in six databases. By screening titles and abstracts and removing duplicates, 18 papers were retrieved, for which full text versions were obtained for detailed assessment. Manual examination of the reference lists in the 18 retrieved papers didn’t add any paper. Finally, nine eligible articles were included in the current review. More details of the data search are described in the flow chart  with some detailed differences based on the relative commercial test kit instructions. Among all, just one of these studies used viral culture technique based on saliva specimens .6 copies per ml and 3.3 × 106 in pooled nasopharyngeal and throat swab. In another study, 12 patient with laboratory-confirmed SARS-CoV-2 infection  were included [6 copies per ml. It is interesting that among these SARS-CoV-2 positive cases, viral cultures were positive for three patients. Later in another article, this research team published the complementary results of their cohort study. In this paper they reported the results of investigation among 23 COVID-19 patients. The results were in accordance with the previous study and showed that the SARS-CoV-2 was detected in saliva specimens of 87% of included subjects [One included studies collected the main specimens from nasopharyngeal and throat of 42 confirmed patients. However, they assessed the possibility of detection of SARS-CoV-2 from saliva specimen in just one confirmed case . The resincluded . The ressubjects .P < 0.001, 20). The authors also reported no significant difference between sputum and saliva samples regarding viral load (P < 0.05).Based on the results of included studies, three of them were performed among the Chinese participants. One of these studies included 65 cases and the other one recruited 31 confirmed COVID-19 patients , 19. TheThe study from Chen et al. showed that among the 13 patients whose oropharyngeal swab tests were positive, 4 cases were also positive for their saliva specimens . The latThe other two studies conducted in Australia and Italy among confirmed COVID-19 patients. These studies reported a detection rate of 84.6 and 100% respectively, based on saliva specimens , 24. OneOne of the main concerns regarding epidemic prevention and control of any infectious disease is rapid and accurate screening of suspected patients. Apart from the level of sensitivity and specificity of laboratory techniques, selecting the appropriate sites to collect samples is very important. Selection of proper sampling method should be based on the tissue affinity of targeted virus, cost-effectiveness of method and also safety of patients and clinicians , 25. In Most of the studies included in this review, reported that there is no statistically significant difference between nasopharyngeal or sputum specimens and saliva samples regarding viral load. These studies suggested saliva as a non-invasive specimen type for the diagnosis and viral load monitoring of SARS-CoV-2 –22, 24. Based on these studies, the method of collection of saliva and collection device types are critical issues in the way of using saliva as diagnostic specimen. In this regard there are three main types of human saliva  and the method of collection of each type varies accordingly . When thThe low concordance rate of saliva with nasopharyngeal specimens reported in the research of Chen et al. might be explained by the differences in the method of obtaining the samples . This stThe use of saliva samples for diagnosis of SARS-CoV-2 has many advantages in clinical practice. First, collecting saliva is a non-invasive procedure and rather than nasal or throat swabs avoids patient discomfort. The second advantage of using saliva as specimen is related to possibility of collecting samples outside the hospitals. This sampling method doesn’t require the intervention of healthcare personnel and the suspected patients can provide it by themselves. Therefore this method can decrease the risk of nosocomial SARS-CoV-2 transmission.Furthermore, because there is not necessary for presence of trained healthcare workers for collecting saliva specimen, the waiting time for suspected patients will be reduced. This is crucial in busy clinical settings where a large number of individuals require screening.The results of viral culture in one of the included studies showed that saliva collected from COVID-19 patients, may contain live viruses which may allow transmission of virus from person to person [It should be mentioned that this study has several limitations. Firstly, the outbreak and detection of SARS-CoV-2 has begun very recently; therefore the available data in this regard is very scarce. Secondly the included studies of this review didn’t evaluate other factors such as severity of disease or disease progression that may impact on detection rate of the virus. Finally as all of the selected studies only included hospitalized confirmed COVID-19 patients, further studies should be performed in outpatient settings.In conclusion, although further research is warranted as the weight of the evidence increases, saliva can be considered as a non-invasive specimen for screening SARS-CoV-2 suspected patients. This method of sampling has proper accuracy and reliability regarding viral load monitoring of SARS-CoV-2 based on RT-PCR technique. Since oropharyngeal samples may cause discomfort to patients, saliva sampling after deep cough, could be recommended as an appropriate alternative.


Multiple Biomarkers have recently been shown to be elevated in COVID-19, a respiratory infection with multi-organ dysfunction; however, information regarding the prognostic value of cardiac biomarkers as it relates to disease severity and cardiac injury are inconsistent. The goal of this meta-analysis was to summarize the evidence regarding the prognostic relevance of cardiac biomarkers from data available in published reports. PubMed, Embase and Web of Science were searched from inception through April 2020 for studies comparing median values of cardiac biomarkers in critically ill versus non-critically ill COVID-19 patients, or patients who died versus those who survived. The weighted mean differences (WMD) and 95% confidence interval (CI) between the groups were calculated for each study and combined using a random effects meta-analysis model. The odds ratio (OR) for mortality based on cardiac injury was combined from studies reporting it. Troponin levels were significantly higher in COVID-19 patients who died or were critically ill versus those who were alive or not critically ill . Additionally, BNP levels were also significantly higher in patients who died or were critically ill . Cardiac injury was independently associated with significantly increased odds of mortality . A significant difference in levels of D-dimer was seen in those who died or were critically ill. CK levels were only significantly higher in those who died versus those who were alive . Cardiac biomarkers add prognostic value to the determination of the severity of COVID-19 and can predict mortality. Lung injury and acute respiratory distress syndrome are the most common presentation of COVID-19, but cardiac injury is another grim consequence of this multisystem viral disease. Evidence of an association between other systemic viral infections such as influenza2 and SARS3 and cardiovascular adverse events dates back to the 1930s4. Early observational studies in China and reports from The Centers for Disease Control and Prevention (CDC) suggest that patients with pre-existing heart conditions are more likely to develop severe illness from COVID-195. A recent study6 suggests that patients in the ICU are three-fold more likely to have pre-existing cardiovascular conditions than non-ICU patients. Conversely, newer evidence implies new onset myocardial injury caused by COVID-198. Possible mechanisms of myocardial injury caused by COVID-19, termed acute COVID-19 cardiovascular syndrome (ACovCS), include heightened myocardial demand in response to the stress of infection, inflammatory cytokines creating a thrombogenic environment as the result of platelet activation and endothelial dysfunction, and direct myocardial damage10. However, current evidence linking myocardial injury as measured by cardiac biomarkers to the severity of disease is conflicting, with a few studies13 suggesting an association but several others showing a lack of association15. Given the discrepancy among these reports, it is prudent to investigate the association of cardiac biomarkers with the severity of disease experienced by and the survival or death of COVID-19 patients. Here we present a meta-analysis of 16 observational studies from China to shed further light on this topic.The Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has affected over 2,402,250 people worldwide and caused 163,097 deaths as of April 21, 202016.Our meta-analysis was carried out in accordance with the recommendations of the Meta-analysis of Observational Studies in the Epidemiology Group (MOOSE)We searched MEDLINE/PubMed 1966–2020) on 4/15/2020 using the following keywords: ("covid-19" OR "COVID-19" OR "SARS-CoV-2" OR "coronavirus" AND "19") AND . We also used the ‘Related article’ feature on PubMed, along with a manual search of references, to identify additional studies. Further, we performed an additional search on Embase and Web of Science, limited to 4/15/2020 and all searches were finalized on 11/10/2020. We reviewed the full text of all relevant articles. English translations, if necessary, were obtained. Titles and abstracts were independently reviewed by three reviewers  and cross-verified for inclusion. Details of the search strategy are reported in Fig. 966–2020 Studies (retrospective and prospective) comparing mean or median troponin and/or BNP in COVID-19 patients who were critically ill versus not critically ill, and those who died versus those who survived, were included. Mean or median levels of other biomarkers included in the studies like CK, D-Dimer, LDH, IL-6, and/or CRP levels in COVID-19 patients were analyzed and compared between patients who were critically ill versus not critically ill, and those who died versus those who survived, only when reported in studies that fit our initial inclusion criteria. Adult respiratory distress syndrome was considered a surrogate for critical illness and studies that compared findings in COVID-19 patients who developed ARDS to patients without ARDS were included. As these were in-hospital outcomes, no specific follow up period was set as an inclusion criteria.Studies reporting the risk of mortality with COVID-19 based on the presence of cardiac injury by event rates or univariate or multivariate logistic regression analysis were included. Cardiac injury, for the purpose of this meta-analysis, was defined as an increase in Troponin I, Troponin T, or CK MB. For both groups of studies, the timing of the blood draw during the hospitalization was not a pre-specified inclusion criteria.Studies were excluded if they (i) only gave descriptive statistics of the biomarkers in one group of patients with COVID-19, (ii) were published only in abstract form, (iii) were non-English studies with no English translation, and/or (iv) were studies that reported measures of central tendency in groups other than those under consideration, i.e., died versus survived and critically ill versus not critically ill.17. The following characteristics were assessed for sources of bias: (1) patient selection including definitions of exposure and representation of the larger population; (2) comparability of study groups and controlling for confounding factors by design or analysis; and (3) assessment and documentation of outcome including duration and loss of follow-up. Studies were graded as ‘poor’ if they met four or fewer of the nine criteria, ‘fair’ if they met five to six criteria, and ‘good’ if they met more than six criteria. For uniformity, all studies, regardless of study design, underwent quality bias assessment using the retrospective cohort scoring tool.For each study included, all data elements reported uniformly across most studies were extracted by two reviewers (AS and MM), cross-verified by a third (DD), and included in Table 2 index. We considered the presence of significant heterogeneity at the 5% level of significance (for the Q test) and values of I2 exceeding 56% as an indicator of significant heterogeneity19. We adopted the random effect model to pool WMD or effect sizes. All analyses were performed using Comprehensive Meta-Analysis version 3 .For the analysis of measures of central tendency of cardiac biomarkers in COVID-19 patients who died or were critically ill, median biomarker values were extracted for patients who died or were critically ill and patients who survived and/or were not critically ill, and the weighted mean differences (WMD) and 95% confidence interval (CI) between these two groups were calculated for each study. For the analysis of the risk of mortality with COVID-19 infection based on cardiac injury, event rates or univariate and multivariate odds ratios (OR) for mortality were extracted. For studies reporting Hazard ratio (HR) only, HR was adopted as the best estimate of OR. OR were transformed logarithmically, then standard error was calculated from Log OR and the corresponding 95% confidence interval (CI). The inverse variance method was used to achieve a weighted estimate of the combined overall effect. For all the analyses, we assessed the results for heterogeneity in our analysis by examining the forest plots and then calculating a Q statistic, which we then compared with the IFor Meta-regression, we adopted a weighted regression random effect model and carried out a multivariate regression of pre-determined factors using comprehensive meta-analysis version 3. These variables were selected based on traditional factors shown to affect cardiovascular health and on data availability for the majority of the studies included. A two-sided P-value < 0.05 was regarded as significant for all analyses. Data were represented as forest plots, and potential publication bias was assessed with the Egger test and represented graphically with Begg funnel plots of the natural log of the OR versus its standard error.21, but reported an OR for mortality based on cardiac injury. 5 studies reported biomarkers based on whether the patients survived or died26, 9 studies reported them based on whether the patient was critically ill or not30. Baseline demographics and comorbidities of patients included in the studies are shown in Table Sixteen studies with 2667 patients were included in the study. Through the initial database search, 974 studies were investigated for compliance with the inclusion criteria, of which 42 studies were ultimately chosen for consideration. Twenty-six studies were excluded from the meta-analysis for the following reasons: 1) 4 studies were descriptive and only reported biomarkers in all patients and did not compare critical/died versus not critical/alive patients. These studies did not define cardiac injury and did not assess OR for mortality with cardiac injury; (2) 19 studies did not report the biomarkers being studied; (3) 2 studies reported biomarkers based on increased or normal D-dimers; and (4) 1 study did not provide official translation to English. Fifteen studies were retrospective, and thirteen were single center . Sub-group analysis of the studies evaluating patients who died compared to patients who were alive showed that there was a significantly higher troponin level in those who died . On the other hand, meta-analysis of studies comparing troponin levels in patients who were critically ill to those in patients who were not critically ill showed no difference in troponin levels .There were 9 studies with 1305 patients that reported NT-proBNP. Six studies compared critically ill versus not critically ill patients, and the other 3 studies compared results from dead and alive patients. Analysis of these 9 studies did show a significant difference in BNP levels in patients who died or were critically ill compared to levels in those who were alive or were not critically ill . Sub-group analysis showed that there was no significant difference in patients who died compared to patients who were alive ; however, there was a significant difference seen in patients who were critically ill and those who were not  . Sub-group analysis showed that there was a significantly higher CK level in patients who died  compared to patients who survived, whereas the patients who were critically ill did not have significantly higher CK levels compared to the patients who were not critically ill . . Sub-group analysis of studies evaluating patients who died compared to patients who were alive showed that there was a significantly higher D-dimer level in those who died . Similarly, critically ill patients had a significantly higher D-dimer level compared to the patients who were not critically ill . . Sub-group analysis showed that there was a significantly higher LDH level in those who died  compared to those who were alive, and that critically ill patients had significantly higher LDH levels compared to patients who were not critically ill . The difference between these two groups of studies, dead versus alive and studies that compared critically ill versus not critically ill patients, was not significantly different. 43. SARS-CoV-2 reduces the expression of ACE2 due to internalization as shown in a study by Oudit et al., which demonstrated a marked decrease in ACE2 expression in the myocardium of mice infected with SARS-CoV-244. ACE2 is responsible for the degradation of AT-2 into Ang 1–7, which is cardioprotective and has anti-inflammatory and anti-fibrotic properties45. The loss of ACE2 shifts the balance towards an accumulation of AT-2, which has inflammatory and pro-thrombotic properties. AT-2 promotes vasoconstriction and increases the levels of Plasminogen Activator Inhibitor-1, which plays an active role in thrombogenesis and myocardial infarction45. This shift in RAAS axis towards AT-2 could possibly explain high D-dimer levels, a biomarker that showed clearly increased levels in both critically ill patients and patients who died compared to levels in not critically ill and alive patients in our meta-analysis  in the plasma of patients infected with COVID-19 showed a linear association with the severity of the diseaseSurprisingly, among the cardiac biomarkers  analyzed in this meta-analysis, only BNP had levels significantly higher in critically ill patients compared to levels in patients who were not critically ill, the levels of CK and troponin were significantly higher in patients who died compared to the levels in those who survived Fig. . SurprisOur study has several limitations. First, the critical and not critical groups included in our analysis are heterogenous, i.e., we defined critical as patients who required ICU admission as well as those who developed severe lung injury and ARDS. Similarly, the criteria for ICU admissions may differ from one hospital to another. Second, most studies were conducted in and published from China and therefore the generalizability of this information to other parts of the world and ethnicities is limited. The absence of details regarding the timing of measurement of biomarkers during the hospital stay limits the use of the results as an aid to the ‘predictability’ of death or critical illness. Last, the studies included in the meta-analysis evaluating the risk of mortality with cardiac injury varied in their definitions of cardiac injury.Supplementary Figures.


With these selected epitopes, a nonallergenic chimeric peptide vaccine incapable of inducing a type II hypersensitivity reaction was constructed. The molecular interaction between the Toll-like receptor-5 (TLR5) which was triggered by the vaccine was analyzed by molecular docking and scrutinized using dynamics simulation. Finally, in silico cloning was performed to ensure the expression and translation efficiency of the vaccine, utilizing the pET-28a vector. This research, therefore, provides a guide for experimental investigation and validation.The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has previously never been identified with humans, thereby creating devastation in public health. The need for an effective vaccine to curb this pandemic cannot be overemphasized. In view of this, we designed a subcomponent antigenic peptide vaccine targeting the N-terminal (NT) and C-terminal (CT) RNA binding domains of the nucleocapsid protein that aid in viral replication. Promising antigenic B cell and T cell epitopes were predicted using computational pipelines. The peptides “RIRGGDGKMKDL” and “AFGRRGPEQTQGNFG” were the B cell linear epitopes with good antigenic index and nonallergenic property. Two CD8 Coronavirus disease 2019 (COVID-19) is the disease associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Characterizing COVID-19 as a pandemic is an acknowledgment that the coronavirus disease 2019 (COVID-19) outbreak, which started in the Hubei province of China in 2019, has now spread to all continents, affecting most countries around the world with differential impacts and peculiarities [Coronaviruses have the largest known genomes up to 32 kb) among +RNA viruses, and they encode four structural and sixteen nonstructural proteins 2 kb amon. StructuThe four structural proteins together with the viral +RNA genome and the envelope constitute the virion , 8. The http://web.expasy.org/protparam/).The protein sequence of the SARS-CoV-2 nucleocapsid phosphoprotein was retrieved in a FASTA format from the NCBI repository with the accession numbers: Wuhan, China (GenBank ID: QHD43423.2). The X-ray crystal structure of the nucleocapsid protein was also retrieved from the protein data bank (PDB: 6m3m). The retrieved structure was subjected to a structural alignment to ascertain the level of homology and probable mutations that have occurred over time during viral replication among the coronavirus family. Bootstrap value and other default parameters were used to fabricate the alignment. The physiochemical properties of the protein sequence were assessed and biocomputed via an online tool ProtParam  (http://http://tools.iedb.org/ellipro/) was adopted for this purpose, while PyMOL was utilized to examine the positions of forecast epitopes on the 3D structure of SARS-CoV-2 nucleocapsid phosphoprotein [The nucleocapsid sequence was analyzed with a view to recognize the antigenic regions that were achieved by predicting epitopic peptides. The promising antigenic linear B cell epitopes were predicted using the BepiPred server from the Immune Epitope Database and Analysis Resource (IEDB) database . BepiPreoprotein .http://tools.iedb.org/mhci). This method integrates the prediction of epitopes restricted to a large number of MHC I alleles and proteasomal C-terminal cleavage, using artificial neural network application. For better predictive accuracy, other software such as artificial neural network (ANN), stabilized matrix method (SMM), MHC-binding energy covariance matrix (SMMPMBEC), NetMHCpan, pickpocket, and NetMHCpan were adopted for this purpose. To predict the HTL cell epitopes, the MHC II binding prediction tool (http://tools.iedb.org/mhcii/) found in the IEDB database was adopted. The antigenic properties of the epitopes were studied using the VaxiJen 2.0 server set at a threshold of 0.6. The peptide toxicity predicted from the ToxinPred server (http://crdd.osdd.net/raghava/toxinpred/), allergenicity predicted from AllergenFP 1.0, and digestion predicted from the Protein Digest server were all considered in selecting the final epitopes.The CTL epitopes were predicted using the IEDB MHC I binding prediction algorithms  which is ranged as −5 to 2. The obtained 3D model of the conjugated antigenic vaccine and the human Toll-like receptor-5 PDB structures were aligned employing the TM-align [The three-dimensional model of the conjugated antigenic vaccine was predicted using the I-TASSER server which generates a 3D model of the query sequence by multiple threading alignments and iterative structural assembly simulation . The I-TTM-align , a quickψ) and phi (ϕ) of an amino acid which is calculated based on the van der Waal radius of the side chain. The corresponding percentage value of both the allowed and disallowed regions of the separate plots of glycine and proline residues of the modeled structure was generated. Qualitative evaluation of 3D models was employed by ProSA [The confirmation of the selected 3D model predicted by I-TASSER was further validated by the Ramachandran plot. RAMPAGE and MolProbity  online sby ProSA . ProSA sIn this study, molecular docking analysis between the vaccine and the human Toll-like receptor-5 was performed using the ClusPro 2.2 protein-protein interaction online server . The shaHere, repulsive, attractive, and electrostatic as well as interactions extracted from the decoys as the reference state are considered for structure-based pairwise potential calculation in docking . The besα Cartesian coordinates [The interacting complex between the vaccine and the Toll-like receptor (PDB: 3J0A) was thoroughly accessed based on the existing coordinates between the docked protein complex. Parameters considered were the deformability, B factor, and eigenvalues associated with the normal mode which represents the motion stiffness. The lower the eigenvalue, the easier the deformation. The covariance matrix was also considered for the simulation. It indicates the coupling between the pairs of residues. The correlation matrix is computed using the Crdinates .E. coli K12. Restriction enzyme cleavage sites, prokaryote ribosomal binding site, and finally rho-independent transcription termination were all avoided during the option selection. Codon adaptation index (CAI) value and GC content of the adapted sequence was obtained and compared with the ideal range. The obtained refined nucleotide was cloned into the pET28a D315A vector.A codon optimization was conducted to ascertain the maximum expression of the vaccine in the host. This was done with the aim of boosting the vaccine translational rate in The protein structure consists of 4 side chains as shown, which suggests a plausible model for RNA binding . A strucIn summary, the alignment construct was less conserved, signifying the rapid rate of mutations that has acted on the protein during viral replications. The protein identification of the PDB IDs is summarized . The aliphatic index is computed to be 78.85, with a GRAVY score of -0.281, signifying its hydrophilic nature. The atomic composition of the vaccine is 376 carbon, 603 hydrogen, 115 nitrogen, 104 oxygen, and 5 sulfur, thereby giving rise to the chemical formula Construct .The predicted model of the vaccine and its three-dimensional coordinate file was successfully obtained from I-TASSER . The resThe Ramachandran plots of the predicted model were obtained to verify the stereochemical parameters of the protein structure. The Ramachandran plot showed 69.7% residues in most favored regions and 26.3% residues in additional allowed regions, i.e., the total of 96% residues in allowed regions which indicates a good quality model ; this waA preliminary docking preparation was conducted by aligning the vaccine construct with the Toll-like receptor to ascertain if both complexes are likely to interact. The TM alignment score obtained was 0.35240, which shows the likelihood of both proteins interacting with stable conformation. The docking structure of the human Toll-like receptor-5 binding with peptide vaccine fragment was finally obtained. A conformational change occurs in the Toll-like receptor-5 protein after binding with the antigenic peptide , with a e-06. The low eigenvalue for the complex signifies easier deformation of the complex, indicating that the docking analysis between the vaccine and the TLR5 will activate immune cascades for destroying the viral antigens.The interaction between the peptide vaccine and the Toll-like receptor was scrutinized to check for their protein stability and deformation. This analysis relies on the associated coordinates of the docked protein complex Figures . The eigE. coli. EagI-NotI and SAlI sites were subsequently cloned into the pET28a D315A vector. The estimated length of the clone was 6.954 kbp  either as whole inactivated or attenuated viruses because of its ability to elicit neutralizing antibodies to block virus-receptor interaction and neutralize the viral infection of cells , 26. UnlObservations from previous studies have shown the conservation level and immunogenic properties of the SARS-CoV-2 N protein. According to their reports, the N protein as a vaccine target has specific advantages over the spike, because of its conservancy, resulting in less mutation of the epitope sequence . Our strin vitro. Also, the discontinuous B cell epitope prediction, which is amino acid residues that were brought into close proximity within the folded protein structure [The humoral immune response plays a clear role in vaccine-mediated defense against infections given their role in maintaining memory cells, prolonged survival, and sentinel against reinfection . Subcomptructure , revealetructure , 38.2 and C3) were selected due to their high antigenicity among the four identified epitopes. The three HTL epitopes  were selected based on their nonallergenicity and high antigenicity. Even though other epitopes had higher antigenicity scores, they were however found to be allergenic, able to elicit a harmful autoimmune response. Therefore, they were not selected.The outcome of disease infection in humans is usually a factor of the strength of the immune response mounted against the infectious agent, and this is usually orchestrated by MHC molecules of the cellular immune system . While BInterestingly, a recent study predicted some T cell epitopes which can be recognized by MHC class II CD4+ HTL alleles of the Asian and Asia-Pacific region populations . While tin silico epitope-based vaccine construct targeting the SARS-COV-2 N protein N-terminal RNA binding domain shows prospects as a potent, safe, and effective candidate with high antigenic properties and a balanced immune response operating through both innate and adaptive pathways.We examined the interaction of the designed vaccine with TLR5 using molecular docking analysis. The binding interfaces between the TLR5 and the peptide vaccine consisted of hydrogen bonding and hydrophobic interactions. Also, the relative binding free energies of the vaccine-TLR5 complex suggests that the linking of the peptide vaccine construct to the receptor elicits conformational changes that favor stimulation of the TLR5 immune molecules and indicate a favorable protein-protein interaction of our vaccine construct with the innate immune receptor. Similarly, the result of the molecular simulation analysis to examine for stability and deformation in the interaction between the vaccine and the TLR5 complex found a low eigenvalue for the complex indicating easier deformation of the complex. This implies that the docking analysis between the vaccine and the TLR5 will activate immune cascades for destroying the viral antigens. We therefore report that the in vitro and in vivo studies to fully validate this research, selected antigenic and nonallergenic peptides that were predicted from this in silico simulation can be synthesized and tested to corroborate our preliminary results.For The lack of an effective therapeutic candidate against the novel coronavirus has created a huge task for biomedical researchers to seek research approaches for overcoming the pandemic. This study was designed in furtherance of steps towards vaccine development. We used the primary amino acid sequence of the SARS-COV-2 to design a subcomponent peptide vaccine construct. The vaccine construct has both adaptive (B and T cell) epitopes and a favorable interaction with the pathogen recognition receptors (PRRs) (via TLR5) of the innate immune system. Each of the predicted epitopes has antigenic properties in the absence of allergenic properties. Generally, this study applied a series of immunoinformatic tools to predict a safe, stable, and effective peptide vaccine that may fight against the SARS-COV-2 viral infection. However, we propose experimental validations to prove this computational work.