SARS-CoV-2 Seroprevalence among Health-Care Workers in Isfahan, Iran.

Background: Health-care workers (HCWs) are in the frontline for fighting the coronavirus disease 2019 (COVID-19) pandemic and are at higher risk of acquiring the infection. Therefore, the defining immunity status among HCWs helps mitigate the exposure risk. In this study, we investigated the anti-SARS-CoV-2 immunoglobulin G (IgG) and immunoglobulin M (IgM) and also the associated risk factors in the HCWs working in Isfahan University of Medical Sciences COVID-19 referral hospitals. Materials and Methods: In a cross-sectional study, demographics, COVID-19 symptoms during the past 2 weeks, and health-care details were collected from 200 consenting health workers of COVID-center-hospitals of Isfahan University of Medical Sciences from 23 October to 21 December 2020. The recombinant SARS-CoV2 nucleocapsid protein enzyme-linked immunosorbent assay-based IgM, and IgG antibody tests were evaluated. Data were analyzed using Chi-square and independent-t-student tests, and P < 0.05 was considered significant. Results: One hundred and forty-one women and 59 men with a mean age of 36.4 ± 7.77 years participated in the study. IgG Ab and IgM Ab were positive in 77 (38.5%) and 12 (6%) of samples, respectively, and both antibodies were detected in 9 (4.5%). Higher ages, direct contact with the patients with COVID-19, muscle pain, loss of taste and smell, fever, and cough were the factors associated with antibody seropositivity against SARS-CoV2. Conclusion: This study demonstrated that the prevalence of HCWs with antibodies against SARS-CoV-2 is relatively high in Isfahan University referral hospitals. The development of safety protocols and screening and vaccination strategies in the frontline HCWs must be implemented to reduce the burden of infection.

The Seroprevalence and Seropositivity of SARS-CoV-2 among Healthcare Workers during the Third Pandemic Wave.

BACKGROUND: Due to the unclear protective role of produced antibodies and the need for seroepidemiologic studies, we surveyed the COVID-19 seroprevalence among healthcare professionals who had direct or indirect contact with COVID-19 patients. METHODS: From 19 October 2020 to 17 February 2021, 300 healthcare workers were enrolled and tested for serum antibodies in this prospective cohort study. Demographic information, risk factors, and infection history were collected. Anti- SARS-CoV-2 IgG and IgM antibody titers were determined to estimate the seroconversion rate. RESULTS: During the first and second phases of the study, the positive seroconversion rates were 31.7 and 26.6%, respectively. In seronegative individuals, sixteen (10.6%) new cases of COVID-19 and five (6.3%) reinfections were identified. Among those with a positive antibody level, forty-one (36.9%) healthcare workers reported no symptoms in the preceding months. There was no association between occupational exposure and an increased probability of seroconversion. CONCLUSIONS: The seropositivity rate and the rate of asymptomatic individuals with seroconversion was remarkable and could be an indicator of a high infection rate among healthcare workers.

Sex Differences in the Relation between Comorbidities and Prognosis in Hospitalized Patients with COVID-19.

Purpose: There is a lack of information of the difference in sex-aggregated prevalence of comorbid noncommunicable disease (NCD) in patients hospitalized with COVID-19 in Iran. This study aimed to evaluate sex differences in the relation between medical comorbidities and subsequent death in patients hospitalized with COVID-19. Methods: All subsequently hospitalized patients with a diagnosis of moderate to severe COVID-19 since February 19th to June 14th, 2020, in Isfahan, Iran, were recruited in the ongoing I-CORE Registry. Real-time reverse-transcription polymerase chain reaction (RT-PCR) testing was done upon admission. Data on preexisting comorbid NCDs including hypertension, coronary heart disease (CHD), diabetes mellitus (DM), cancers, chronic renal disease (CRD), and chronic respiratory disease were collected through self-reported questionnaires. Results: Overall, 12,620 individuals were enrolled in this registry of which 4,356 were positive for the COVID-19 RT-PCR test. In the whole population, in women, DM, hypertension, and CHD, and in men, DM, CHD, and hypertension were, respectively, the most frequent comorbidities. The frequency of at least one NCD did not differ between men and women, but a greater proportion of women had two or more NCDs. Increasing the number of comorbidities was associated with higher death frequency and mortality risk in the unadjusted model but remained no longer significant after adjustment for age. There was no statistically significant difference in this regard between men and women. Conclusion: Overall, we found that DM, hypertension, and CHD were the most frequent comorbidities. Although comorbidities were more frequent among women, mortality risk did not significantly differ between men and women.

Does COVID-19 Escalate Aging Process? A Possible Concern.

A key challenge after the COVID-19 pandemic will be managing the long-term sequelae for the millions of individuals who recover from the disease. Based on the available evidence, our hypothesis is that the SARS-CoV-2 pandemic and its long-term complications will lead to premature aging (in terms of health) of many people in the world. Obviously, to maintain appropriate public health and prevent poor health-care services, countries should think and plan about the health problems and the long-term consequences of SARS-CoV-2 after controlling the COVID-19 pandemic.

Association of Clinical Features with Spike Glycoprotein Mutations in Iranian COVID-19 Patients.

BACKGROUND: Mutations in spike glycoprotein, a critical protein of SARS-CoV-2, could directly impact pathogenicity and virulence. The D614G mutation, a non-synonymous mutation at position 614 of the spike glycoprotein, is a predominant variant circulating worldwide. This study investigated the occurrence of mutations in the crucial zone of the spike gene and the association of clinical symptoms with spike mutations in isolated viruses from Iranian patients infected with SARS-CoV-2 during the second and third waves of the COVID-19 epidemic in Isfahan, the third-largest city in Iran. METHODS: The extracted RNA from 60 nasopharyngeal samples of COVID-19 patients were subjected to cDNA synthesis and RT-PCR (in three overlapping fragments). Each patient's reverse transcriptase polymerase chain reaction (RT-PCR) products were assembled and sequenced. Information and clinical features of all sixty patients were collected, summarized, and analyzed using the GENMOD procedure of SAS 9.4. RESULTS: Analysis of 60 assembled sequences identified nine nonsynonymous mutations. The D614G mutation has the highest frequency among the amino acid changes. In our study, in 31 patients (51.66%), D614G mutation was determined. For all the studied symptoms, no significant relationship was observed with the incidence of D614G mutation. CONCLUSIONS: D614G, a common mutation among several of the variants of SARS-CoV-2, had the highest frequency among the studied sequences and its frequency increased significantly in the samples of the third wave compared to the samples of the second wave of the disease.

Estimated Global Proportions of Individuals With Persistent Fatigue, Cognitive, and Respiratory Symptom Clusters Following Symptomatic COVID-19 in 2020 and 2021.

Importance: Some individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID). Objective: To estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration. Design, Setting, and Participants: Bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), the 10 collaborating cohort studies (10 526 and 1906), and the 2 US electronic medical record databases (250 928 and 846 046). Data collection spanned March 2020 to January 2022. Exposures: Symptomatic SARS-CoV-2 infection. Main Outcomes and Measures: Proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age. Results: A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months. Conclusions and Relevance: This study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.

Screening of potential inhibitors of COVID-19 with repurposing approach via molecular docking.

SARS-CoV-2 (COVID-19) is the causative organism for a pandemic disease with a high rate of infectivity and mortality. In this study, we aimed to assess the affinity between several available small molecule and proteins, including Abl kinase inhibitors, Janus kinase inhibitor, dipeptidyl peptidase 4 inhibitors, RNA-dependent RNA polymerase inhibitors, and Papain-like protease inhibitors, using binding simulation, to test whether they may be effective in inhibiting COVID-19 infection through several mechanisms. The efficiency of inhibitors was evaluated based on docking scores using AutoDock Vina software. Strong ligand-protein interactions were predicted among some of these drugs, that included: Imatinib, Remdesivir, and Telaprevir, and this may render these compounds promising candidates. Some candidate drugs might be efficient in disease control as potential inhibitors or lead compounds against the SARS-CoV-2. It is also worth highlighting the powerful immunomodulatory role of other drugs, such as Abivertinib that inhibits pro-inflammatory cytokine production associated with cytokine release syndrome (CRS) and the progression of COVID-19 infection. The potential role of other Abl kinase inhibitors, including Imatinib in reducing SARS-CoV and MERS-CoV viral titers, immune regulatory function and the development of acute respiratory distress syndrome (ARDS), indicate that this drug may be useful for COVID-19, as the SARS-CoV-2 genome is similar to SARS-CoV.

Cancer Occurrence as the Upcoming Complications of COVID-19.

Previous studies suggested that patients with comorbidities including cancer had a higher risk of mortality or developing more severe forms of COVID-19. The interaction of cancer and COVID-19 is unrecognized and potential long-term effects of COVID-19 on cancer outcome remain to be explored. Furthermore, whether COVID-19 increases the risk of cancer in those without previous history of malignancies, has not yet been studied. Cancer progression, recurrence and metastasis depend on the complex interaction between the tumor and the host inflammatory response. Extreme proinflammatory cytokine release (cytokine storm) and multi-organ failure are hallmarks of severe COVID-19. Besides impaired T-Cell response, elevated levels of cytokines, growth factors and also chemokines in the plasma of patients in the acute phase of COVID-19 as well as tissue damage and chronic low-grade inflammation in "long COVID-19" syndrome may facilitate cancer progression and recurrence. Following a systemic inflammatory response syndrome, some counterbalancing compensatory anti-inflammatory mechanisms will be activated to restore immune homeostasis. On the other hand, there remains the possibility of the integration of SARS- CoV-2 into the host genome, which potentially may cause cancer. These mechanisms have also been shown to be implicated in both tumorigenesis and metastasis. In this review, we are going to focus on potential mechanisms and the molecular interplay, which connect COVID-19, inflammation, and immune-mediated tumor progression that may propose a framework to understand the possible role of COVID-19 infection in tumorgenesis and cancer progression.

Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76-89], diagnostic odds ratio (DOR) of 496 [198-1,245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94-98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96-0.98] and 0.92 [0.91-0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

Red Blood Cell Distribution Width (RDW) as a Predictor of In-Hospital Mortality in COVID-19 Patients; a Cross Sectional Study.

INTRODUCTION: Red blood cell distribution width (RDW) has been introduced as a predictive factor for mortality in several critical illnesses and infectious diseases. This study aimed to assess the possible relationship between RDW on admission and COVID-19 in-hospital mortality. METHOD: This cross-sectional study was performed using the Isfahan COVID-19 registry. Adult confirmed cases of COVID-19 admitted to four hospitals affiliated with Isfahan University of Medical Sciences in Iran were included. Age, sex, O2 saturation, RDW on admission, Intensive Care Unit admission, laboratory data, history of comorbidities, and hospital outcome were extracted from the registry. Cox proportional hazard regression was used to study the independent association of RDW with mortality. RESULTS: 4152 patients with the mean age of 61.1 ± 16.97 years were included (56.2% male). 597 (14.4%) cases were admitted to intensive care unit (ICU) and 477 (11.5%) cases died. The mortality rate of patients with normal and elevated RDW was 7.8% and 21.2%, respectively (OR= 3.1, 95%CI: 2.6-3.8), which remained statistically significant after adjusting for age, O2 saturation, comorbidities, and ICU admission (2.03, 95% CI: 1.68-2.44). Moreover, elevated RDW mortality Hazard Ratio in patients who were not admitted to ICU was higher than ICU-admitted patients (3.10, 95% CI: 2.35-4.09 vs. 1.47, 95% CI: 1.15-1.88, respectively). CONCLUSION: The results support the presence of an association between elevated RDW and mortality in patients with COVID-19, especially those who were not admitted to ICU. It seems that elevated RDW can be used as a predictor of mortality in COVID-19 cases.

Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study.

BACKGROUND: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. METHODS: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. RESULTS: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835-0.910]). CONCLUSIONS: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.

Model Prediction for In-Hospital Mortality in Patients with COVID-19: A Case-Control Study in Isfahan, Iran.

The COVID-19 pandemic has now imposed an enormous global burden as well as a large mortality in a short time period. Although there is no promising treatment, identification of early predictors of in-hospital mortality would be critically important in reducing its worldwide mortality. We aimed to suggest a prediction model for in-hospital mortality of COVID-19. In this case-control study, we recruited 513 confirmed patients with COVID-19 from February 18 to March 26, 2020 from Isfahan COVID-19 registry. Based on extracted laboratory, clinical, and demographic data, we created an in-hospital mortality predictive model using gradient boosting. We also determined the diagnostic performance of the proposed model including sensitivity, specificity, and area under the curve (AUC) as well as their 95% CIs. Of 513 patients, there were 60 (11.7%) in-hospital deaths during the study period. The diagnostic values of the suggested model based on the gradient boosting method with oversampling techniques using all of the original data were specificity of 98.5% (95% CI: 96.8-99.4), sensitivity of 100% (95% CI: 94-100), negative predictive value of 100% (95% CI: 99.2-100), positive predictive value of 89.6% (95% CI: 79.7-95.7), and an AUC of 98.6%. The suggested model may be useful in making decision to patient's hospitalization where the probability of mortality may be more obvious based on the final variable. However, moderate gaps in our knowledge of the predictors of in-hospital mortality suggest further studies aiming at predicting models for in-hospital mortality in patients with COVID-19.

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.

COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R 2. The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R 2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.

A one-year hospital-based prospective COVID-19 open-cohort in the Eastern Mediterranean region: The Khorshid COVID Cohort (KCC) study.

The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising. Thus, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The patient recruitment phase finished at the end of August 2020, and the follow-up continues by the end of August 2021. The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered primary, and impaired pulmonary function and psychotic disorders were considered main secondary outcomes. Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, 490 patients with complete information (39% female; the average age of 57±15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The Cox Proportional-Hazards Model for PCEP indicated the following significant risk factors: Oxygen saturation < 80% (HR = 6.3; [CI 95%: 2.5,15.5]), lymphopenia (HR = 3.5; [CI 95%: 2.2,5.5]), Oxygen saturation 80%-90% (HR = 2.5; [CI 95%: 1.1,5.8]), and thrombocytopenia (HR = 1.6; [CI 95%: 1.1,2.5]). This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.