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Background: Respiratory viruses play important roles in respiratory tract infections;they are the major cause of diseases such as the common cold, bronchiolitis, pneumonia, etc., in humans that circulate more often in the cold seasons. During the COVID-19 pandemic, many strict public health measures, such as hand hygiene, the use of face masks, social distancing, and quarantines, were implemented worldwide to control the pandemic. Besides controlling the COVID-19 pandemic, these introduced measures might change the spread of other common respiratory viruses. Moreover, with COVID-19 vaccination and reducing public health protocols, the circulation of other respiratory viruses probably increases in the community. Objective(s): This study aims to explore changes in the circulation pattern of common respiratory viruses during the COVID-19 pan-demic. Method(s): In the present study, we evaluated the circulation of seven common respiratory viruses (influenza viruses A and B, rhi-novirus, and seasonal human Coronaviruses (229E, NL63, OC43, and HKU1) and their co-infection with SARS-CoV-2 in suspected cases of COVID-19 in two time periods before and after COVID-19 vaccination. Clinical nasopharyngeal swabs of 400 suspected cases of COVID-19 were tested for SARS-CoV-2 and seven common respiratory viruses by reverse transcription real-time polymerase chain reaction. Result(s): Our results showed common respiratory viruses were detected only in 10% and 8% of SARS-CoV-2-positive samples before and after vaccination, respectively, in which there were not any significant differences between them (P-value = 0.14). Moreover, common viral respiratory infections were found only in 12% and 32% of SARS-CoV-2-negative specimens before and after vaccination, respectively, in which there was a significant difference between them (P-value = 0.041). Conclusion(s): Our data showed a low rate of co-infection of other respiratory viruses with SARS-CoV-2 at both durations, before and after COVID-19 vaccination. Moreover, the circulation of common respiratory viruses before the COVID-19 vaccination was lower, probably due to non-pharmaceutical interventions (NPI), while virus activity (especially influenza virus A) was significantly in-creased after COVID-19 vaccination with reducing strict public health measures.Copyright © 2023, Author(s).
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Background: The coronavirus disease 2019 (COVID‐19) was first reported in December 2019 in Wuhan, China. Skin manifestations of COVID-19 have been reported sporadically. Staphylococcus aureus occurs after viral infection due to unregulated IFN-α. We designed this reported case to pay more attention to the rare skin manifestations following COVID-19. Case Report: The patient was a 12-month-old girl who presented with fever and skin rashes. Two days before admission, erythematous rashes spread around the mouth, nose, eyes, and trunk. Erythematous lesions begin to peel the next day. RT-PCR of the nasopharynx was positive for COVID-19. Treatment with vancomycin and clindamycin was started. The patient was discharged with complete recovery of skin lesions. Conclusion: One of the early manifestations of COVID-19 in children can be fever and rash. Clinical suspicion led to more attention to complications of bacterial superinfection such as staphylococcal scalded skin syndrome.
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Fake news detection has become an emerging and critical topic of research in recent years. One of the major complications of fake news detection lies in the fact that news in social networks is multilingual, and therefore developing methods for each and every language in the world is impossible, especially for low resource languages like Persian. In an effort to solve this problem, researchers use machine translation to uniform the data and develop a method for the uniformed data. In this paper, we aim to explore the impacts of machine translation on fake news detection. For this purpose, we extracted and labeled a dataset of Persian Tweets from Twitter on the subject of COVID-19 and developed a method for detecting fake news on the extracted Tweets based on the SVM classifier, then we machine translated the data and applied our proposed method to it. Finally, the result for binary class (only fake and legitimate) fake news detection was 87%, and for multiclass (satire, misinformation, neutral and legitimate) fake news detection was 62%, and our findings demonstrate that machine translation has a 4% negative impact on binary classification accuracy and a 23% negative impact on multiclass classification.
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Background: Despite many studies, it is still unclear how patients with neuromyelitis optica spectrum disorder (NMOSD) would respond to coronavirus disease 2019 (COVID-19). We conducted a research on prevalence of COVID-19 in patients with NMOSD in Isfahan, Iran. We have also reviewed the recent publications on this issue. Methods: 149 patients with NMOSD who were under medications were monitored for confirmed cases of COVID-19. Prevalence of COVID-19 in addition to mean age, mean duration of disease, and mean age of onset of infected patients and uninfected patients were calculated via Microsoft Excel software. Results: The prevalence of COVID-19 in studied patients with NMOSD was 5.37%. Mean age, mean duration of disease, and mean age of onset of eight patients (male to female ratio: 1:3) diagnosed with COVID-19 were 33.62 ± 5.20 years, 6.87 ± 6.05 years, and 26.75 ± 6.94 years, respectively, while they were 39.97 ± 11.37 years, 7.50 ± 3.91 years, and 32.46 ± 11.29 years for uninfected patients with NMOSD (n = 141). No significant association was observed between the type of medications and prevalence of COVID-19 (P > 0.05). Conclusion: There is not a consensus in the literature on the prevalence of COVID-19 in patients with NMOSD and the effect of NMOSD medications on susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The prevalence of COVID-19 in our sample was 5.37%. The impact of the kind of NMOSD medication on the prevalence of COVID-19 in patients with NMOSD was found to be insignificant. Moreover, the infected patients were relatively younger, and their disease started earlier in comparison to uninfected patients. © 2021 Iranian Neurological Association, and Tehran University of Medical Sciences Published by Tehran University of Medical Sciences.
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COVID-19 Pandemic has affected human being without distinguishing the region and religion around the world. Even though vaccine is available now, we should still be use precautionary steps to avoid being affected by this virus. Use of face mask will help to stop the spread of the virus. COVID - 19 face mask detector will help in detecting whether a person is wearing mask or not by the use of its on face mask net with deep learning techniques. With suggested technique of deep learning get an accuracy of 98 percent. Our suggested technique also works with still pictures and give effective results with live video stream captured using the CCTV cameras and able to identify whether person is using face mask or not. It is very useful at the place of the mass gathering and screening like shopping mall and railway station and shopping streets and colleges. By using this tool for the face mask will help in regulating the use of it and if person found not using it will be easily warned by the regulator. © 2021 IEEE.
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Introduction: Pregnant women are one of the main groups that should be considered during the Covid-19 pandemic. By knowing the pregnant women's worrying experiences in this period, we can achieve the strategies to improve the health and quality of life and to plan for improving prenatal care. This study was performed with aim to explore the perception and experience of pregnant women in the face of COVID 19 pandemic. Methods: This qualitative study was carried out in Comprehensive Health Services centers in Urmia-Iran during May to October 2020. Data were collected using 14 individual in-depth semi-structured telephone interviews. Participants were selected among pregnant women through purposeful sampling, until data saturation was reached. Data were analyzed through conventional content analysis approach using the MAXQDA software (version 10). Results: From analyzing data, 580 primary codes and four main categories including: "lifestyle change(change in daily activities, isolation and social distancing), "experienced psychological effects" (pregnancy health concerns, fear of family members getting sick, financial stress and confusion over information dissemination), "seeking support" (need of support from relatives, need of support from health personnel), and "change in the prenatal care process"(not referring to government centers, reduction in number of prenatal care)were emerged. Conclusion: The covid-19 pandemic has significant potential in changing lifestyle, routine prenatal care and psychological status in pregnant women. Therefore, it is necessary for midwives to provide virtually and by telephone the necessary training and counseling about self-care and reducing anxiety and stress for pregnant women.
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Background: The novel coronavirus (2019-nCoV) causes a severe respiratory illness unknown to a human before. Its alarmingly quick transmission to many countries across the world has resulted in a global health emergency. Therefore, an imminent need for drugs to combat this disease has been increased. Worldwide collaborative efforts from scientists are underway to determine a therapy to treat COVID-19 infections and reduce mortality rates. Since herbal medicines and purified natural products have been reported to have antiviral activity against Coronaviruses (CoVs), this in silico evaluation was performed for identifying potential natural compounds with promising inhibitory activities against COVID-19. Methods: In this study, a High Throughput Virtual Screening (HTVS) protocol was used as a fast method for discovering novel drug candidates as potential COVID-19 main protease (Mpro) inhibitors. Over 180,000 natural product-based compounds were obtained from the ZINC database and virtually screened against the COVID-19 Mpro. In this study, the Glide docking program was applied for high throughput virtual screening. Also, Extra precision (XP) has been used following the induced-fit docking (IFD) approach. The ADME properties of all compounds were analyzed and a final selection was made based on the Lipinski rule of five. Also, molecular dynamics (MD) simulations were conducted for a virtual complex of the best scoring compound with COVID-19 protease. Results: Nineteen compounds were introduced as new potential inhibitors. Compound ZINC08765174 (1-[3-(1H-indol-3-yl) propanoyl]-N-(4-phenylbutan-2-yl)piperidine-3-carboxamide showed a strong binding affinity (-11.5 kcal/mol) to the COVID-19 Mpro comparing to peramivir (-9.8 kcal/mol) as a positive control. Conclusion: Based on these findings, nineteen compounds were proposed as possible new COVID-19 inhibitors, of which ZINC08765174 had a high affinity to Mpro. Furthermore, the promising ADME properties of the selected compounds emphasize their potential as attractive candidates for the treatments of COVID-19.
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Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 μM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.
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Background: Despite many studies, it is still unclear how patients with neuromyelitis optica spectrum disorder (NMOSD) would respond to coronavirus disease 2019 (COVID-19). We conducted a research on prevalence of COVID-19 in patients with NMOSD in Isfahan, Iran. We have also reviewed the recent publications on this issue. Methods: 149 patients with NMOSD who were under medications were monitored for confirmed cases of COVID-19. Prevalence of COVID-19 in addition to mean age, mean duration of disease, and mean age of onset of infected patients and uninfected patients were calculated via Microsoft Excel software. Results: The prevalence of COVID-19 in studied patients with NMOSD was 5.37%. Mean age, mean duration of disease, and mean age of onset of eight patients (male to female ratio: 1:3) diagnosed with COVID-19 were 33.62 +/- 5.20 years, 6.87 +/- 6.05 years, and 26.75 +/- 6.94 years, respectively, while they were 39.97 +/- 11.37 years, 7.50 +/- 3.91 years, and 32.46 +/- 11.29 years for uninfected patients with NMOSD (n = 141). No significant association was observed between the type of medications and prevalence of COVID-19 (P > 0.05). Conclusion: There is not a consensus in the literature on the prevalence of COVID-19 in patients with NMOSD and the effect of NMOSD medications on susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The prevalence of COVID-19 in our sample was 5.37%. The impact of the kind of NMOSD medication on the prevalence of COVID-19 in patients with NMOSD was found to be insignificant. Moreover, the infected patients were relatively younger, and their disease started earlier in comparison to uninfected patients.
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Numerous deaths worldwide have been caused by the coronavirus pandemic and are currently progressing with successive mutations and a lack of appropriate and definitive treatment. One of the drug targets to control the replication of the virus and treat this disease is to block the ion channel of the virus. This will lead to its death by disturbing the internal balance of the virus. Natural compounds such as alkaloids are usually known as effective compounds due to their medicinal characteristics and easy access to their sources. To this end, more than 3,200 natural alkaloid structures interacted with pentameric ion channels. Alkaloid compounds established significant and stable interactions with the channel. More clearly and in more detail, six alkaloid compounds with the best pharmacokinetics and binding affinity of less than-10.52 kcal/mol were selected as hit and suitable compounds for virus control. The compound of psammaplysin U (NA-1) with a binding affinity of-13.52 kcal/mol and binding free energy of-82.21 kcal/mol established hydrogen interactions with the amino acid of Val 25 in the B chain of the ion channel, which placed the compound at the top of the selected compounds. The molecular dynamics simulation of the ligand-protein complex in the 100 ps trajectory showed that the principal interactions were hydrogen and halogen bonding with the amino acids of Val 25 and Thr 30 in the B chain and A chain, respectively, which could be a suitable inhibitor to combat the COVID-19. © 2021 by the authors.
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HLA genes with extreme diversity can make a contribution for individual variations to the immune response against SARS-CoV-2 infection. This study aimed to explore the distributions of HLA class II alleles frequencies as well as their relationship with the severity of disease in a group of Iranian COVID-19 patients. This prospective and case-controlled study was conducted on 144 confirmed COVID-19 patients including 46 cases with moderate or mild form, 54 cases with severe and 44 cases with critical disease. HLA-DRB1 and -DQB1 alleles were determined by PCR-SSP methods and compared between the three groups of patients and in comparison, to a group of ethnically matched healthy controls (n = 153). HLA-DRB1∗15 was the only marginally significant allele that showed lower frequency in the patients compared with healthy controls (P = 0.06). Patients with moderate COVID-19 had higher frequencies of HLA-DRB1∗04 (P = 0.03) and HLA-DRB1∗10 (P = 0.05) alleles versus severe and critical patients. We found a higher significantly frequency of HLA-DRB1∗03 alleles in the critical subgroup of the patients than healthy controls (P = 0.01). Moreover, we observed a significantly higher Neutrophil/Lymphocyte ratio in critical cases versus moderate to severe patients (P = 0.002). Our results indicate a possible contribution of some HLA class II alleles in disease severity and clinical features of COVID-19 disease.
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Nowadays, the SARS Coronavirus 2 (SARS-CoV-2) infection is recognized as the primary cause of mortality in humans. SARS-CoV-2 is transmitted through human-to-human contact and is asymptomatic in most patients. In addition to approved vaccines against SARS-CoV-2 infection, miRNAs may also be promising options against this new virus. miRNAs are small and noncoding RNAs 18-25 nucleotides in length that target the mRNAs to degrade them or obstruct their translation miRNAs act as an observer in cells. This study reviewed the literature on the potential role of cellular miRNAs in the SARS-CoV-2-host interplay as a therapeutic option in COVID-19 patients.
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Background: Different aspects of coronavirus disease 2019 (COVID-19) in children have not been well understood so far. Objectives: In this paper, we reported the clinical, Paraclinical, and epidemiological features of the hospitalized children infected with COVID-19 in the southeast of Iran. Methods: This cross-sectional study was conducted in six hospitals affiliated to Kerman University of Medical Sciences. All children who were under the age of 15 years old hospitalized with acute respiratory infection from February 20 to May 14, 2020, were included in this study. Demographic characteristics, past medical history data, and disease-related data such as symptoms, signs, radiologic, and laboratory data were collected. Results: Of 97 hospitalized children with an acute respiratory infection, 13 cases (13.4%) had been diagnosed to be infected by COVID-19. The mean (standard deviation) and median of age of the patients with COVID-19 were 68.0 (55.9) and 60 months, respectively. Fever (n = 11, 84.6%), cough (n = 8, 61.5%), respiratory distress (n = 5, 38.5%), and gastrointestinal symptoms (n = 5, 38.5%) were known as the most common symptoms in patients with COVID-19. Frequency fever (84.6% vs 47. 6%, P = 0.016) and respiratory distress (38.8% vs 13.1%, P = 0.022) were significantly higher in patients with COVID-19 compared to non-COVID individuals. Frequency of admission in the intensive care unit (38.5% vs. 27.4%, P = 0.668) and death rate (15.4% vs. 7.1%, P = 0.291) were higher in patients with COVID-19 compared to non-COVID-19 subjects, but there were no significant differences between the two groups in term of these variables. Conclusions: A low proportion of children hospitalized with acute respiratory syndrome were infected by COVID-19. Most of the children with COVID-19 recovered with supportive care with no need for any specific treatment. © 2020, Author(s).