The prevalence of depressive and anxiety symptoms among first-year and fifth-year medical students during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Medical students have higher risk of psychological disorders due to the relatively stressful environment. Educators are becoming increasingly aware of the impact of stresses on the students general well-being. The objective of the current study was to examine the prevalence of and risk factors for depressive and anxiety symptoms among first-year and fifth-year medical students. Additionally, we aimed to determine whether the COVID-19 pandemic has affected students' mental well-being. METHODS: A cross-sectional study was performed at the College of Medicine at King Saud University between September 2020 and January 2021. The target population was first-year and fifth-year medical students. Depressive symptoms were screened using the 9-item Patient Health Questionnaire (PHQ-9), while anxiety symptoms were screened using the 7-item Generalized Anxiety Disorder assessment (GAD-7). Students were also directly asked about the effect of the COVID-19 pandemic on their mental well-being. Outcomes were compared between groups using the chi-squared test and Student's t test. Multivariate logistic regression analysis was performed to identify factors associated with depressive and anxiety symptoms. RESULTS: A total of 182 medical students were included. Depressive symptoms (52.9% versus 35.8%, p = 0.020) and anxiety symptoms (35.6% versus 26.3%, p = 0.176) were higher in the first-year students than in the fifth-year students. Approximately 19.2% of the students were worried about acquiring COVID-19, 49.4% were worried about academic performance, and 30.8% were feeling sad, depressed or anxious during the COVID-19 pandemic. Independent risk factors for depressive symptoms included having concomitant anxiety, being worried about acquiring COVID-19, being worried about academic performance, and feeling sad, depressed or anxious. Independent risk factors for anxiety included having a lower grade point average and having concomitant depressive symptoms. CONCLUSION: Medical students have an alarmingly high prevalence of depressive and anxiety symptoms, which might have been negatively impacted by the COVID-19 pandemic. There is a need for a special mental health program targeting new and current medical students.

Impact of viral co-infection on clinical outcomes and mortality of COVID-19 patients: a study from Saudi Arabia.

Background: In COVID-19 patients undetected co-infections may have severe clinical implications associated with prolonged hospitalization, ICU admission, and mortality. Therefore, we aimed to investigate the impact of viral coinfections on the outcomes of hospitalized patients with COVID-19 in a large tertiary Saudi Arabian Hospital. Methods: A total of 178 adult patients with confirmed SARS-CoV-2 who were hospitalized at the Armed Forces Hospital Southern Region (AFHSR), Saudi Arabia, from March 1st to June 30th 2022, were enrolled. Real-time PCR for the detection of viral co­infections was carried out. Cases (SARS-CoV-2 with viral coinfections) and control (SARS-CoV-2 mono-infection) groups were compared. Results: 12/178 (7%) of enrolled COVID-19 patients had viral coinfections. 82/178 (46%) of patients were males. 58% of patients had comorbidities. During the study period, 4/12 (33%) and 21/166 (13%) cases and control patients died, p=0.047, respectively. Duration of hospitalization was the only significant independent factor associated with SARS-CoV-2 coinfections, OR 1.140, 95% CI 1.020-1.274, p=0.021. Conclusions: The findings of this study from a large tertiary Saudi Arabian Center revealed a prevalence of 7% for SARS-CoV-2 viral coinfections. SARS-CoV-2 coinfected patients had a significantly prolonged duration of hospitalization and higher mortality than those with SARS-CoV-2 alone. Future studies are needed.

Impact of MERS-CoV and SARS-CoV-2 Viral Infection on Immunoglobulin-IgG Cross-Reactivity.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has posed a considerable threat to public health and global economies. SARS-CoV-2 has largely affected a vast world population and was declared a COVID-19 pandemic outbreak, with a substantial surge of SARS-CoV-2 infection affecting all aspects of the virus' natural course of infection and immunity. The cross-reactivity between the different coronaviruses is still a knowledge gap in the understanding of the SARS-CoV-2 virus. This study aimed to investigate the impact of MERS-CoV and SARS-CoV-2 viral infections on immunoglobulin-IgG cross-reactivity. Our retrospective cohort study hypothesized the possible reactivation of immunity in individuals with a history of infection to Middle East Respiratory Syndrome coronavirus (MERS-CoV) when infected with SARS-CoV-2. The total number of participants included was 34; among them, 22 (64.7%) were males, and 12 (35.29%) were females. The mean age of the participants was 40.3 ± 12.9 years. This study compared immunoglobulin (IgG) levels against SARS-CoV-2 and MERS-CoV across various groups with various histories of infection. The results showed that a reactive borderline IgG against both MERS-CoV and SARS-CoV-2 in participants with past infection to both viruses was 40% compared with 37.5% among those with past infection with MERS-CoV alone. Our study results establish that individuals infected with both SARS-CoV-2 and MERS-CoV showed higher MERS-CoV IgG levels compared with those of individuals infected previously with MERS-CoV alone and compared with those of individuals in the control. The results further highlight cross-adaptive immunity between MERS-CoV and SARS-CoV. Our study concludes that individuals with previous infections with both MERS-CoV and SARS-CoV-2 showed significantly higher MERS-CoV IgG levels compared with those of individuals infected only with MERS-CoV and compared with those of individuals in the control, suggesting cross-adaptive immunity between MERS-CoV and SARS-CoV.

Virtual Behavioral Intervention to Promote Healthy Lifestyle Behaviors: A Feasibility RCT during COVID-19 Pandemic.

BACKGROUND: the COVID-19 pandemic has had a substantial impact on human health, affecting many lifestyle behaviors such as physical activity, sedentary behavior, dietary habits and sleep. PURPOSE: to assess the feasibility of six sessions of a virtual behavioral intervention to promote healthy lifestyle practices during a stay-at-home advisory phase of the COVID-19 pandemic. METHODS: A participant-blinded randomized controlled trial was performed through a virtual platform setting. Participants were randomly assigned into two groups. They were assigned to a motivational interviewing (MI) intervention or attention group, with pre- and postintervention assessments. The MI treatment consisted of six sessions (twice each week). The same number of virtual structured sessions were provided for the attention group, and they provided brief advice to promote healthy lifestyles. The study was conducted from April to June 2020. RESULTS: The feasibility outcomes indicated that 39 of the 50 participants (78%) completed the trial. The dropout rate was 21.7% for the attention group and 22.2% for the intervention group. Participating in MI had a significant positive interventional effect on physical activity level, distress and fear of COVID-19. CONCLUSIONS: It is feasible to deliver behavioral change interventions virtually. Further, MI can be used as a useful strategy for the favorable promotion of a healthy lifestyle. TRIAL REGISTRATION: NCT05392218 (26/05/2022).

PheroxyPyrabenz and Carbopyrropyridin against major proteins of SARS CoV-2: a comprehensive in-silico molecular docking and dynamics simulation studies.

The pandemic that started in 2020 left us with so much information about viruses and respiratory diseases, and the cause behind it was severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). The world is still recovering, which costs so many economic and other indirect disasters; despite that, no medications are available on the market. Although the WHO approved a few vaccines on an emergency basis, the remarks and the reinfection chances are still under investigation, and a few pharmaceutical companies are also claiming that a few medications can be effective. However, there is no situation in control. SARS CoV-2 mutates and comes in different forms, making the situation unpredictable. In this study, we have screened the complete Asinex's BioDesign library, which contains 170,269 compounds, and shorted the data against the docking score that helps in the identification of 4-[5-(3-Ethoxy-4-hydroxyphenyl)-1-(2-hydroxyethyl)-1H-pyrazol-3-yl]-1, 2-benzenediol (PheroxyPyrabenz) and 1-[(3R,4R)-1-(5-Aminopentanoyl)-4-hydroxy-3-pyrrolidinyl]-1H-pyrrolo[2,3-b]pyridine-4-carboxamide (Carbopyrropyridin) as a significant drug candidate that can work against the multiple proteins of the SARS CoV-2 resulting in seizing the complete biological process of the virus. Further, the study extended to Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and molecular dynamics (MD) simulation of both the compounds with their complexity. The complete workflow of the study has shown satisfactory results, and both drug candidates can potentially stop the hunt for drugs against this virus after its experimental validation. Further, we checked both compounds' absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, showing case-proof validatory results.Communicated by Ramaswamy H. Sarma.

Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach's alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.

Attitudes toward Receiving COVID-19 Booster Dose in the Middle East and North Africa (MENA) Region: A Cross-Sectional Study of 3041 Fully Vaccinated Participants.

COVID-19 vaccines are crucial to control the pandemic and avoid COVID-19 severe infections. The rapid evolution of COVID-19 variants such as B.1.1.529 is alarming, especially with the gradual decrease in serum antibody levels in vaccinated individuals. Middle Eastern countries were less likely to accept the initial doses of vaccines. This study was directed to determine COVID-19 vaccine booster acceptance and its associated factors in the general population in the MENA region to attain public herd immunity. We conducted an online survey in five countries (Egypt, Iraq, Palestine, Saudi Arabia, and Sudan) in November and December 2021. The questionnaire included self-reported information about the vaccine type, side effects, fear level, and several demographic factors. Kruskal-Wallis ANOVA was used to associate the fear level with the type of COVID-19 vaccine. Logistic regression was performed to confirm the results and reported as odds ratios (ORs) and 95% confidence intervals. The final analysis included 3041 fully vaccinated participants. Overall, 60.2% of the respondents reported willingness to receive the COVID-19 booster dose, while 20.4% were hesitant. Safety uncertainties and opinions that the booster dose is not necessary were the primary reasons for refusing the booster dose. The willingness to receive the booster dose was in a triangular relationship with the side effects of first and second doses and the fear (p < 0.0001). Females, individuals with normal body mass index, history of COVID-19 infection, and influenza-unvaccinated individuals were significantly associated with declining the booster dose. Higher fear levels were observed in females, rural citizens, and chronic and immunosuppressed patients. Our results suggest that vaccine hesitancy and fear in several highlighted groups continue to be challenges for healthcare providers, necessitating public health intervention, prioritizing the need for targeted awareness campaigns, and facilitating the spread of evidence-based scientific communication.

A pilot study of the moderating effect of gender on the physical activity and fatigue severity among recovered COVID-19 patients.

BACKGROUND: Clinical data point toward gender-based differences in COVID-19 severity. However, there is insufficient research examining whether gender predicts physical activity (PA) and fatigue severity in patients recovering from COVID-19. Therefore, this study aimed to characterize the PA and fatigue severity in a cohort of patients recovering from COVID-19 infection and measure the extent to which gender-based differences moderate the relationship of PA with fatigue. METHOD: A cross-sectional survey was conducted in Riyadh, Saudi Arabia. The sample comprised patients recovering from COVID-19 over at least 3 months. Recovered patients were stratified into two groups based on gender. The survey included items pertaining to sociodemographic, a fatigue severity scale and a self-reported international PA questionnaire. RESULTS: Eighty-seven patients (44 women and 43 men) met the inclusion criteria. Compared with men, women reported sedentary behavior (70%) and high fatigue severity (64%). A significantly higher number of women had a low PA score compared with men (p = .002). The findings indicated that gender significantly moderates the effect of total PA in metabolic equivalents (METs; min/wk) on fatigue severity [F = 4.8, p = .03, ΔR2 = 0.24]. CONCLUSIONS: The current study suggests that women might be at risk of higher fatigue severity, in addition to engaging less in PA. Physical activity may plays a significant role in modulate the fatigue severity. Consequently, interventions aimed at promoting physical activity in women stand high chances of addressing the disparity in the distribution of prevalence of fatigue between men and women.

Effectiveness of Early Favipiravir Therapy in Hospitalised COVID-19 Patients.

COVID-19 is a disease caused by a novel coronavirus with no specific, standard treatment. We investigated the clinical data of COVID-19 patients admitted to King Fahad Specialist Hospital (KFSH) in Buraydah by comparing the patients who were treated early with favipiravir (within 3 days of admission) to patients who were treated after three days of admission or not treated. 165 patients were confirmed with PCR tests and admitted to KFSH for treatment. Comorbidities contributed significantly to increasing the length of stay in hospital at 11.4 ± 0.8 days compared to patients with no comorbidities at 8.6 ± 0.9 days (p=0.041). A total of 103 patients were treated with favipiravir, and we found that early treatment with favipiravir (within 3 days) reduced the length of stay in hospital significantly (8.8 ± 1.4 days) compared to patients who were treated after 3 days (13.3 ± 4.6 days) (p=0.0015). Moreover, patients with comorbidities in both early and late treatment groups had significantly higher average lengths of stay in hospital (11.2 ± 0.9 days) compared to patients with no comorbidities (7.9 ± 0.7 days) (p=0.017). Interestingly, patients treated early with favipiravir (with comorbidities and without) stayed fewer days in hospital compared to those with late treatment (p=0.021; a difference of 4.5 ± 1.9 days; and p=0.018; a difference of 4.2 ± 1.7 days, respectively). In conclusion, our analysis indicates that early treatment with favipiravir can reduce the length of stay in hospital and improve clinical manifestations of COVID-19 patients.

A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.

Clinical Characteristics, Laboratory Findings, Management, and Outcome of Severe Coronavirus Disease 2019 in Children at a Tertiary Care Center in Riyadh, Saudi Arabia: A Retrospective Study.

Background: Numerous studies worldwide have reported COVID-19 in children; however, the clinical symptoms and consequences of COVID-19 in children have only been reported in a few studies in Saudi and gulf region. Therefore, we aimed to investigate the clinical features and outcomes of COVID-19 infection in children and the therapeutic interventions used. Methods: This retrospective cohort study included 96 patients with confirmed severe acute respiratory syndrome coronavirus 2 infection aged ≤14 years who were admitted to a tertiary governmental care hospital in Riyadh, Saudi Arabia between March 2020 and November 2020. Data on children with COVID-19, including demographics, comorbidities, symptoms, imaging and laboratory results, therapies, and clinical outcomes, were analyzed. Results: Of 96 children admitted with a confirmed diagnosis of COVID-19, 63.8% were aged ≤ 3 years, 52.1% were male, 56.2% had an unknown source of infection, and 51% had no comorbidities. Most cases had severe infection (71.88%) as they required oxygen, 10.42% of whom were critical. The most common symptoms were respiratory-related (98%), and the common physical sign was fever (49%). High D-dimer (90.7%) and C-reactive protein (72.09%) levels were found in most cases. Oxygen (71.88%) was the most commonly used treatment. Most patients were discharged home and fully recovered (97.92%). We reported two deaths (2.08%). Conclusions: Our findings showed that the majority of the admitted children with COVID-19 were ≤3 years of age (52.1%) and infected with an unknown source (56.2%). Moreover, the majority of the cases had severe COVID-19 infection as they required oxygen (71.88%), although they had favorable outcomes. However, some cases were critical and resulted in death. Future studies will be crucial to better understand the disease spectrum and potential therapeutic options for COVID-19 in children.

Sociodemographic predictors of confirmed COVID-19 mortality and hospitalization among patients in Saudi Arabia: Analyzing a national COVID-19 database.

BACKGROUND: Even with the widespread availability of vaccines for the COVID-19 disease, there is no sign of decline in the rate of spread of the disease. Based on findings of different studies across the globe, the disease is characterized by poor outcomes in specific sociodemographic categories such as age, gender and presence of symptoms. METHODS: In this study, we carried out a multivariable logistic regression analysis on a national database (HESN+) of confirmed COVID-19 cases in Saudi Arabia to determine predictors of hospitalization and mortality for these patients. RESULTS: Data was extracted for 328,301 confirmed COVID- 19 patients (mean age (SD) = 37.79 (1.68)) with 34.92% females and 65.08% males. Of these, 59.87% were Saudi Arabian citizens and 40.13% were non-Saudi. 68.91% of cases were discovered in Riyadh (n = 67,384), Makkah (n = 72,590) and the Eastern Province (n = 79,666). 72.2% of all cases were diagnosed and treated by the Ministry of Health (MOH). Of all confirmed cases, 95.28% showed one or more symptoms associated with COVID-19. 5.48% of these were hospitalized and 1.11% died. Predictors of mortality and hospitalization, respectively, included age (OR; 1.088 and 1.03), being male (OR; 1.443 and 1.138), nationality (OR; 2.11 and 1.993), presence of symptoms (OR; 1.816 and 4.386), and the health care sector in which patients received treatment (MOH OR; 1.352 and 4.731). CONCLUSION: We found that COVID-19-related hospitalization or mortality was higher among males, older adults, and patients showing one or more symptoms, and mortality likelihood was more than fourfold for patients treated by the MOH. Immigrants were also more likely to be hospitalized or die from COVID-19 infection compared to Saudi nationals.

Impact of the loss of smell on the sleep quality and fatigue level in COVID­19 survivors.

PURPOSE: Patients with Coronavirus disease 2019 (COVID-19) are reported to have symptoms such as shortness of breath, dry cough, headache, fatigue, and diarrhea. Loss of smell is a symptom that some patients have suffered from due to inflammation of olfactory epithelium and neuroinvasion of COVID-19 resulting in damage to the olfactory nerves and olfactory bulb. Losing an important sense such as smell might have unfavorable consequences on the lives of COVID-19 survivors; however, these unfavorable consequences have not been sufficiently investigated. METHODS: This was a cross-sectional descriptive study, 81 COVID-19 survivors (51.85% male) answered the Pittsburgh Sleep Quality Index, Fatigue Severity Scale, and Patient Health Questionnaire. RESULTS: COVID-19 survivors who lost their smell were more likely to have poor sleep quality, high fatigue severity, and depression symptoms compared to others who did not lose their smell. Most COVID-19 survivors who lost their smell were women and had breathing difficulties. CONCLUSION: Our knowledge of this relationship will assist in establishing more efficient treatment regimens that consider both psychological and physiological factors. Future research is needed to investigate the causality relationship between poor sleep quality, increased fatigue, and depression symptoms in COVID-19 survivors who experienced loss of the sense of smell.

Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic.

Soon after the coronavirus disease 2019 pandemic was proclaimed, digital health services were widely adopted to respond to this public health emergency, including comprehensive monitoring technologies, telehealth, creative diagnostic, and therapeutic decision-making methods. The World Health Organization suggested that artificial intelligence might be a valuable way of dealing with the crisis. Artificial intelligence is an essential technology of the fourth industrial revolution that is a critical nonmedical intervention for overcoming the present global health crisis, developing next-generation pandemic preparation, and regaining resilience. While artificial intelligence has much potential, it raises fundamental privacy, transparency, and safety concerns. This study seeks to address these issues and looks forward to an intelligent healthcare future based on best practices and lessons learned by employing telehealth and artificial intelligence during the COVID-19 pandemic.

Novel Molecules derived from 3-O-(6-galloylglucoside) inhibit Main Protease of SARS-CoV 2 In Silico.

The ongoing pandemic caused by the severe acute respiratory syndrome 2 (SARS-CoV 2) has led to more than 168 million confirmed cases with 3.5 million deaths as at 28th May, 2021 across 218 countries. The virus has a cysteine protease called main protease (Mpro) which is significant to it life cycle, tagged as a suitable target for novel antivirals. In this computer-assisted study, we designed 100 novel molecules through an artificial neural network-driven platform called LigDream (https://playmolecule.org/LigDream/) using 3-O-(6-galloylglucoside) as parent molecule for design. Druglikeness screening of the molecules through five (5) different rules was carried out, followed by a virtual screening of those molecules without a single violation of the druglike rules using AutoDock Vina against Mpro. The in silico pharmacokinetic features were predicted and finally, quantum mechanics/molecular mechanics (QM/MM) study was carried out using Molecular Orbital Package 2016 (MOPAC2016) on the overall hit compound with controls to determine the stability and reactivity of the lead molecule. The findings showed that eight (8) novel molecules violated none of the druglikeness rules of which three (3) novel molecules (C33, C35 and C54) showed the utmost binding affinity of -8.3 kcal/mol against Mpro; C33 showed a good in silico pharmacokinetic features with acceptable level of stability and reactively better than our controls based on the quantum chemical descriptors analysis. However, there is an urgent need to carry out more research on these novel molecules for the fight against the disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11696-021-01899-y.

Infection Control Practices at the Dental Clinics in Jeddah, Saudi Arabia.

PURPOSE: The aims of this study were to determine the rate of compliance of infection control and estimate the incidence of COVID-19 in dental clinics in Jeddah, Saudi Arabia. MATERIALS AND METHODS: This is an ecological correlational study of randomly sampled dental clinics in the city of Jeddah. The 32-question survey used in the study was constructed based on the infection control guidelines/protocols during the COVID-19 pandemic released by the ADA, CDC, and SMOH. A sample of clinics from a dental clinic list in Jeddah city, using a simple random technique. RESULTS: Fifty-three dental clinics consented to participate in our study, while ten refused. Most of the dental clinics checked their patients' temperature (30.2%) and strictly required them to wear a surgical mask before entering the clinic (58.5%). Glove, gown, and face mask were the most common methods of infection control used during all dental procedures (98.1%, 96.2%, and 94.3%, respectively). Moreover, the incidence of COVID-19 was the highest among receptions/security (18.70%), followed by nurses (14.3%) and dentists (11.79%). CONCLUSION: As the incidence of COVID-19 among dental staff will continue to increase in the future, it is highly recommended that infection control guidelines are followed in all dental clinics.

Novel Molecules derived from 3-O-(6-galloylglucoside) inhibit Main Protease of SARS-CoV 2 In Silico

The ongoing pandemic caused by the severe acute respiratory syndrome 2 (SARS-CoV 2) has led to more than 168 million confirmed cases with 3.5 million deaths as at 28th May, 2021 across 218 countries. The virus has a cysteine protease called main protease (Mpro) which is significant to it life cycle, tagged as a suitable target for novel antivirals. In this computer-assisted study, we designed 100 novel molecules through an artificial neural network-driven platform called LigDream (https://playmolecule.org/LigDream/) using 3-O-(6-galloylglucoside) as parent molecule for design. Druglikeness screening of the molecules through five (5) different rules was carried out, followed by a virtual screening of those molecules without a single violation of the druglike rules using AutoDock Vina against Mpro. The in silico pharmacokinetic features were predicted and finally, quantum mechanics/molecular mechanics (QM/MM) study was carried out using Molecular Orbital Package 2016 (MOPAC2016) on the overall hit compound with controls to determine the stability and reactivity of the lead molecule. The findings showed that eight (8) novel molecules violated none of the druglikeness rules of which three (3) novel molecules (C33, C35 and C54) showed the utmost binding affinity of −8.3 kcal/mol against Mpro;C33 showed a good in silico pharmacokinetic features with acceptable level of stability and reactively better than our controls based on the quantum chemical descriptors analysis. However, there is an urgent need to carry out more research on these novel molecules for the fight against the disease. <h4>Supplementary Information</h4> The online version contains supplementary material available at 10.1007/s11696-021-01899-y.

Discovery, Development, and Patent Trends on Molnupiravir: A Prospective Oral Treatment for COVID-19.

The COVID-19 pandemic needs no introduction at present. Only a few treatments are available for this disease, including remdesivir and favipiravir. Accordingly, the pharmaceutical industry is striving to develop new treatments for COVID-19. Molnupiravir, an orally active RdRp inhibitor, is in a phase 3 clinical trial against COVID-19. The objective of this review article is to enlighten the researchers working on COVID-19 about the discovery, recent developments, and patents related to molnupiravir. Molnupiravir was originally developed for the treatment of influenza at Emory University, USA. However, this drug has also demonstrated activity against a variety of viruses, including SARS-CoV-2. Now it is being jointly developed by Emory University, Ridgeback Biotherapeutics, and Merck to treat COVID-19. The published clinical data indicate a good safety profile, tolerability, and oral bioavailability of molnupiravir in humans. The patient-compliant oral dosage form of molnupiravir may hit the market in the first or second quarter of 2022. The patent data of molnupiravir revealed its granted compound patent and process-related patent applications. We also anticipate patent filing related to oral dosage forms, inhalers, and a combination of molnupiravir with marketed drugs like remdesivir, favipiravir, and baricitinib. The current pandemic demands a patient compliant, safe, tolerable, and orally effective COVID-19 treatment. The authors believe that molnupiravir meets these requirements and is a breakthrough COVID-19 treatment.

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic.

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV &MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people. Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y-z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y)=100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y-u), exposed-removed (y-v), exposed-virus in the reservoir (y-w), asymptomatically infected-removed (z-v), symptomatically infected-removed (u-v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z-w), at asymptomatically infected (z)=10, the value of viruses in the reservoir (w)=50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario.

Prevalence and mortality of lung comorbidities among patients with COVID-19: A systematic review and meta-analysis.

COVID-19 infections are seen across all age groups, but they have shown to have a predisposition for the elderly and those with underlying comorbidities. Patients with severe COVID-19 infections and comorbidities are more prone to respiratory distress syndrome, mechanical ventilator use, and ultimately succumb to these complications. Little evidence exists of the prevalence of underlying lung comorbidities among COVID-19 patients and associated mortality. We performed a systematic review of the literature including PubMed (Medline), Embase (Ovid), Google Scholar, and Cochrane Library. The last date for our search was April 29, 2020. We included all original research articles on COVID-19 and calculated prevalence of chronic lung disease patients among COVID-19 patients using random effects model. Further, we assessed for mortality rates among COVID-19 patients associated with these lung comorbidities. The authors identified 29 articles that reported prevalence of chronic lung conditions among COVID-19 patients. Among those, 26 were from China and 3 from the United States. The pooled prevalence of lung comorbidities including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer was 3% (95% confidence interval [CI] = 0%-14%), 2.2% (95% CI = 0.02%-0.03%), and 2.1% (95% CI = 0.00%-0.21%), respectively. Mortality rates associated with these comorbidities was 30% (41/137) for COPD and 19% (7/37) for lung cancer respectively. No mortality rates were reported for patients with asthma. This study offers latest evidence of prevalence of chronic lung conditions among patients with COVID-19. Asthma, followed by COPD and lung cancer, was the most common lung comorbidity associated with COVID-19, while the higher mortality rate was found in COPD. Future studies are needed to assess other lung comorbidities and associated mortality among patients diagnosed with COVID-19.