COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

The COVID 19 pandemic is highly contagious disease is wreaking havoc on people's health and well-being around the world. Radiological imaging with chest radiography is one among the key screening procedure. This disease contaminates the respiratory system and impacts the alveoli, which are small air sacs in the lungs. Several artificial intelligence (AI)-based method to detect COVID-19 have been introduced. The recognition of disease patients using features and variation in chest radiography images was demonstrated using this model. In proposed paper presents a model, a deep convolutional neural network (CNN) with ResNet50 configuration, that really is freely-available and accessible to the common people for detecting this infection from chest radiography scans. The introduced model is capable of recognizing coronavirus diseases from CT scan images that identifies the real time condition of covid-19 patients. Furthermore, the database is capable of tracking detected patients and maintaining their database for increasing accuracy of the training model. The proposed model gives approximately 97% accuracy in determining the above-mentioned results related to covid-19 disease by employing the combination of adopted-CNN and ResNet50 algorithms.

Evaluation of altered miRNA expression pattern to predict COVID-19 severity.

Outbreak of COVID-19 pandemic in December 2019 affected millions of people globally. After substantial research, several biomarkers for COVID-19 have been validated however no specific and reliable biomarker for the prognosis of patients with COVID-19 infection exists. Present study was designed to identify specific biomarkers to predict COVID-19 severity and tool for formulating treatment. A small cohort of subjects (n = 43) were enrolled and categorized in four study groups; Dead (n = 16), Severe (n = 10) and Moderate (n = 7) patients and healthy controls (n = 10). Small RNA sequencing was done on Illumina platform after isolation of microRNA from peripheral blood. Differential expression (DE) of miRNA (patients groups compared to control) revealed 118 down-regulated and 103 up-regulated known miRNAs with fold change (FC) expression ≥2 folds and p ≤ 0.05. DE miRNAs were then subjected to functional enrichment and network analysis. Bioinformatic analysis resulted in 31 miRNAs (24 Down-regulated; 7 up-regulated) significantly associated with COVID-19 having AUC>0.8 obtained from ROC curve. Seventeen out of 31 DE miRNAs have been linked to COVID-19 in previous studies. Three miRNAs, hsa-miR-147b-5p and hsa-miR-107 (down-regulated) and hsa-miR-1299 (up-regulated) showed significant unique DE in Dead patients. Another set of 4 miRNAs, hsa-miR-224-5p (down-regulated) and hsa-miR-4659b-3p, hsa-miR-495-3p and hsa-miR-335-3p were differentially up-regulated uniquely in Severe patients. Members of three miRNA families, hsa-miR-20, hsa-miR-32 and hsa-miR-548 were significantly down-regulated in all patients group in comparison to healthy controls. Thus a distinct miRNA expression profile was observed in Dead, Severe and Moderate COVID-19 patients. Present study suggests a panel of miRNAs which identified in COVID-19 patients and could be utilized as potential diagnostic biomarkers for predicting COVID-19 severity.

Orthodontic emergencies and mental health of patients undergoing orthodontic treatment during COVID-19 pandemic: A questionnaire study.

Objectives: To evaluate the impact of COVID-19 pandemic on orthodontic treatment and mental health of patients undergoing orthodontic treatment and to compare their mental health with the age-matched control group. Materials and methods: 484 orthodontic patients (245 males and 239 females) and 200 age-matched control subjects were divided into two age groups. Group 1 had 14-18 years of adolescents (N = 274) and 100 control participants (Group 2) and Group 3 comprised of 19 years above adults (N = 210) and 100 control participants (Group 4). Group 1 and 3 patients filled the 4 sections of the questionnaire related to orthodontic emergencies (Sections 1-3) and mental distress (Kessler Psychological Distress Scale-Section 4), while groups 2 and 4 were asked to fill only Section-4. The comparison of mental distress on high/low Kessler scores was made using the Chi-Square test/Fisher's exact test. The factors which came out to be significant were put to bivariate logistic regression analysis. Results: The percentage of patients with high Kessler scores among Group 1 and Group 3 were 9.9% and 17.2% respectively, and their differences with age-matched control groups were non-significant. The mean differences of Kessler score were significantly higher for Group 3 compared to group 1. The higher Kessler score was associated with age, higher education, a feeling of concern for non-availability of appointments, increased treatment duration, its effect on the quality of treatment, and sabotaging of definitive future plans. Conclusions: The orthodontic treatment and emergencies may not be a significant factor contributing to increased stress among patients during the lockdown.

Medical education in post-pandemic times: Online or offline mode of learning?

Background and Objective: With the advent of the COVID-19 pandemic, face-to-face training was suspended considering social-distancing norms. The training needs of the healthcare workers (HCWs) were being met by the online mode. Initially, the use of the online mode was limited but was eventually popularized with increased use. This would have led to a change in the perception toward the online mode. However, the use of online learning has financial and temporal obstacles. With this objective, a study was conducted among the HCWs to assess the perception, satisfaction, and preference associated with the modes of learning. Methods: A cross-sectional study was conducted from February to April 2021 among the HCWs. An online link to the survey was circulated among the HCWs who attended online or/and offline training. The questionnaire had 38 questions assessing the sociodemographic details, perception, satisfaction level, and preferences of the participants. Univariable and multivariate logistic regression were performed using SPSS v-22. Results: A total of 1,113 responses were received with the mean age of 33.17 ± 8.13 years and approximately 63% of the participants were females. Approximately 54% perceived the online mode of learning as a better mode of learning. Also, 67% preferred and 80.5% recommended the online mode whereas mean satisfaction was found to be more for the offline mode as compared to the online mode. Interpretation and Conclusions: The study concludes that the online mode of learning is the most preferred and recommended mode among the HCWs, whereas there is more dissatisfaction with respect to the online mode. The study also emphasizes that the instructors need to improve the practical knowledge of the learners by integrating technical modalities.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection.

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure-activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01975-3.

Implications of COVID-19 on Thrombotic Profile of Severely Affected Patients.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is a novel viral disease that spread as a global pandemic in 2020 by infecting millions of people across the world. Its clinical prognosis is dependent on various coagulatory parameters since thrombotic events are frequently associated with infection severity. METHODS: A total of 383 COVID-19 patients enrolled in Rajiv Gandhi Super Specialty Hospital, Delhi, India, were included in the present retrospective study. Patients were divided into three categories, severe (n = 141), moderate (n = 138), and mild (n = 104) based on infection severity. Various thrombotic parameters and anticoagulant levels were measured in 70 patients and further analyzed. RESULTS: Coagulopathy is seen in COVID-19 patients (n = 70) with a significant increase in fibrinogen, D-dimer levels, and prothrombin time in patients with severe and moderate disease compared to patients with a mild infection. Approximately, 70% of patients with severe and moderate disease demonstrated fibrinogen levels higher than the standard reference range. 60.41% of patients with severe disease showed significantly higher D-dimer levels. Thrombotic parameters were notably elevated in the nonsurvivors group compared to COVID-19 survivors. Nearly, 91% of patients with severe infection had anticoagulant protein S levels below the reference range. CONCLUSION: COVID-19 infection severely impacts the blood coagulation cascade, which might lead to the manifestation of severe symptoms and increased mortality in patients.

Chemistry and human exposure implications of secondary organic aerosol production from indoor terpene ozonolysis.

Surface cleaning using commercial disinfectants, which has recently increased during the coronavirus disease 2019 pandemic, can generate secondary indoor pollutants both in gas and aerosol phases. It can also affect indoor air quality and health, especially for workers repeatedly exposed to disinfectants. Here, we cleaned the floor of a mechanically ventilated office room using a commercial cleaner while concurrently measuring gas-phase precursors, oxidants, radicals, secondary oxidation products, and aerosols in real-time; these were detected within minutes after cleaner application. During cleaning, indoor monoterpene concentrations exceeded outdoor concentrations by two orders of magnitude, increasing the rate of ozonolysis under low (<10 ppb) ozone levels. High number concentrations of freshly nucleated sub-10-nm particles (≥105 cm-3) resulted in respiratory tract deposited dose rates comparable to or exceeding that of inhalation of vehicle-associated aerosols.

In silico modeling for quick prediction of inhibitory activity against 3CLpro enzyme in SARS CoV diseases.

As of 2 September 2020, the 2019 novel coronavirus or SARS CoV-2 has been responsible for more than 2,56,02,665 infections and 8,52,768 deaths worldwide. There has been an urgent need of newer drug discovery to tackle the situation. Severe acute respiratory syndrome-associated coronavirus 3C-like protease (or 3CLpro) is a potential target as anti-SARS agents as it plays a vital role in the viral life cycle. This study aims at developing a quantitative structure-activity relationship (QSAR) model against a group of 3CLpro inhibitors to study their structural requirements for their inhibitory activity. Further, molecular docking studies were carried out which helped in the justification of the QSAR findings. Moreover, molecular dynamics simulation study was performed for selected compounds to check the stability of interactions as suggested by the docking analysis. The current QSAR model was further used in the prediction and screening of large databases within a short time.Communicated by Ramaswamy H. Sarma.

Quality Infrastructure of Saudi Arabia and Its Importance for Vision 2030

The quality infrastructure of the Kingdom of Saudi Arabia is discussed in this article. The effect of COVID-19 pandemic on the oil-based economy is described in detail. The goals of Vision 2030 in reference to conversion of oil-based economy of KSA to non-oil-based economy have been discussed. The quality infrastructure models for economic growth has been discussed. This article describes details of the three pillars: metrology, accreditation, and standards of economic growth in context to KSA. The role of the existing apex bodies, i.e., Saudi Standards, Metrology and Quality Organization, the National Measurement and Calibration Center, and the Saudi Accreditation Center in growth of KSA has been described. The main requirements to strengthen the QI of KSA are enhancing the apex capabilities, the setting up of new primary and secondary standards, development of strong research and development culture and awareness of quality improvement.

Multicentric evaluation of a novel point of care electrochemical ELISA platform for SARS-CoV-2 specific IgG and IgM antibody assay.

New diagnostics technologies for the efficient detection and quantification of SARS-CoV-2 antibodies are very crucial to manage the COVID-19 pandemic, especially in the context of emerging vaccination paradigms. Herein, we report on a novel point-of-care Electrochemical ELISA platform with disposable screen printed electrodes functionalized with SARS-CoV-2 Spike Glycoprotein S1, to enable fast and accurate quantitative estimation of total antibody concentration (IgG and IgM) in clinical samples. The quantification is performed with a comparison of electrochemical redox current against the current produced by the spiked monoclonal antibodies with known concentration. The assay is validated through multicentric evaluation against 3 different FDA authorized Laboratory standard techniques, using both EDTA whole blood and serum samples. We demonstrate that the proposed assay has excellent sensitivity and specificity, making it a suitable candidate for epidemiological surveys and quantification of antibodies in COVID-19 vaccination programs.

Genome recoding strategies to improve cellular properties: mechanisms and advances.

The genetic code, once believed to be universal and immutable, is now known to contain many variations and is not quite universal. The basis for genome recoding strategy is genetic code variation that can be harnessed to improve cellular properties. Thus, genome recoding is a promising strategy for the enhancement of genome flexibility, allowing for novel functions that are not commonly documented in the organism in its natural environment. Here, the basic concept of genetic code and associated mechanisms for the generation of genetic codon variants, including biased codon usage, codon reassignment, and ambiguous decoding, are extensively discussed. Knowledge of the concept of natural genetic code expansion is also detailed. The generation of recoded organisms and associated mechanisms with basic targeting components, including aminoacyl-tRNA synthetase-tRNA pairs, elongation factor EF-Tu and ribosomes, are highlighted for a comprehensive understanding of this concept. The research associated with the generation of diverse recoded organisms is also discussed. The success of genome recoding in diverse multicellular organisms offers a platform for expanding protein chemistry at the biochemical level with non-canonical amino acids, genetically isolating the synthetic organisms from the natural ones, and fighting viruses, including SARS-CoV2, through the creation of attenuated viruses. In conclusion, genome recoding can offer diverse applications for improving cellular properties in the genome-recoded organisms.

Evaluation of Psychological Impact of COVID-19 on Health-Care Workers.

BACKGROUND: Little information is available from India about the psychological impact of COVID-19 on helath-care workers. AIM: The current study aimed to evaluate the psychological issues among the health-care workers (HCW) during the COVID-19 pandemic. MATERIALS AND METHODS: An online survey using Survey Monkey® platform was carried out to evaluate depression (using Patient Health Questionnaire-9), anxiety (using Generalized Anxiety Disorder Questionnaire-7), and other psychological issues (using a self-designed questionnaire). RESULTS: The study sample comprised 303 participants with a mean age of 41.2 (standard deviation: 11.1) years. A majority of them were male (69%) and married (79.9%). Nearly half (46.2%) of the participants had either anxiety disorder or depression or both and 12.9% of HCW had suicidal behavior. Higher level of anxiety and depression scores were associated with being female, having undergone quarantine, directly involved in the care of COVID-19 patients, and younger age (<30 years). Higher prevalence of depression and anxiety disorder was seen in younger (<30 years) age group, being a doctor (compared to paramedics). In addition, higher prevalence of depression was seen in those who were directly involved in the care of patients with COVID-19 infection. CONCLUSION: About half of the HCWs are suffering from psychiatric morbidity, specifically anxiety, in the wake of the COVID-19 pandemic. There is a need to assess all the HCWs for psychiatric morbidity and provide them with psychological support.

A novel machine learning‐based analytical framework for automatic detection of COVID‐19 using chest X‐ray images

Considering the prevailing scenario of COVID‐19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID‐19 diagnosis is real‐time reverse transcription‐polymerase chain reaction (rRT‐PCR) test. However, the chest radiological (X‐ray) imaging can be used as an alternate method to rRT‐PCR test, and early COVID‐19 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)‐based analytical framework is developed for automatic detection of COVID‐19 using chest X‐ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID‐19. The experimental results pose the proposed framework as a potential candidate for COVID‐19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four‐class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID‐19 detection along with other types of pneumonia. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Potential association of COVID-19 and ABO blood group: An Indian study.

Coronavirus disease 2019 (COVID-19) transmits from person to person mainly through respiratory droplets and coughing. Infection severity ranges from asymptomatic and mild infection to those with moderate and severe symptoms which may lead to multiple organ failure and mortality. Infection severity largely depends on individual's immune response, age and co-morbidities. Present study categorized COVID-19 infected patients based on their infection severity and linked COVID-19 severity with age, gender and ABO blood group types. Clinical details of 383 COVID-19 patients were collected from Rajiv Gandhi Super Specialty hospital (RGSSH), India; divided into three groups; mild, moderate and severe patients, based on their symptoms. Present analysis revealed that age plays major role in infection severity, as the symptoms are more severe in patients above 45 years. Infection rate was higher in males compared to females. Most patients with A(+ve) and B(+ve) blood group were severely affected compared to those of blood group type O(+ve) and AB(+ve). O(+ve) blood group was least represented in severe patients. Present findings could be helpful in generating awareness amongst the population regarding susceptibility towards the COVID-19 infection. This supportive information would help clinicians and health workers to propose new strategies and tactical solution against COVID-19 infection.

COVID-19 Pandemic: Mechanism, Diagnosis and Treatment

Abstract The COVID-19 pandemic has emerged to become a significant global health crisis. The virus has spread to more than 200 countries worldwide and continues to infect more lives and cause more deaths. The second and possibly the third wave have begun, with numbers of those hospitalised and deaths rising continuously. The impact has galvanized research groups around the world, working on novel diagnostics, vaccines, biotherapeutics, personal protective equipment, and epidemiological data analysis. This perspective focuses on the biological aspects of COVID-19 infection,the origin and structure of SARS-CoV-2, its genomic variations, mode of transmission, pathogenesis in the human body, symptoms of the infection, mechanism of entry in the host cell, and techniques for clinical diagnosis. Precautionary measures, different treatment options,vaccine development, importance of multidisciplinary research, and political and global prospects of the pandemic have also been addressed. This article is protected by copyright. All rights reserved.

Impact of the COVID-19 pandemic on psychosocial health and well-being in South-Asian (World Psychiatric Association zone 16) countries: A systematic and advocacy review from the Indian Psychiatric Society.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has emerged as a global health threat. The South-Asian (SA) countries have witnessed both the initial brunt of the outbreak as well as the ongoing rise of cases. Their unique challenges in relation to mental health during the pandemic are worth exploring. MATERIALS AND METHODS: A systematic review was conducted for all the original studies on the impact of COVID-19 and lockdown on psychological health/well-being in the SA countries of the World Psychiatric Association Zone 16. PubMed, Google Scholar, PSYCHINFO, EMBASE, and SCOPUS were searched till June 2020. Studies conducted in the age group of 18-60 years with a minimum sample size of 10, and statistically significant results were included. RESULTS: Thirteen studies were included in the review. They showed increase prevalence in nonpsychotic depression, preanxiety, somatic concerns, alcohol-related disorders, and insomnia in the general population. Psychological symptoms correlated more with physical complaints of fatigue and pain in older adults and were directly related to social media use, misinformation, xenophobia, and social distancing. Frontline workers reported guilt, stigma, anxiety, and poor sleep quality, which were related to the lack of availability of adequate personal protective equipment, increased workload, and discrimination. One study validated the Coronavirus anxiety scale in the Indian population while another explored gaming as a double-edged sword during the lockdown in adolescents. Another study from Bangladesh explored psychosexual health during lockdown. Most studies were cross-sectional online surveys, used screening tools and had limited accessibility. CONCLUSION: The ongoing COVID-19 crisis and its impact serve as an important period for adequate mental healthcare, promotion, research, and holistic biopsychosocial management of psychiatric disorders, especially in vulnerable groups. Mental healthcare and research strategies during the pandemic and preparedness for postpandemic aftermath are advocated subsequently.

In Silico Modeling of Small Molecule Carboxamides as Inhibitors of SARS-CoV 3CL Protease: An Approach Towards Combating COVID-19.

BACKGROUND: The quantitative structure-activity relationship (QSAR) approach is most widely used for the prediction of biological activity of potential medicinal compounds. A QSAR model is developed by correlating the information obtained from chemical structures (numerical descriptors/ independent variables) with the experimental response values (the dependent variable). METHODS: In the current study, we have developed a QSAR model to predict the inhibitory activity of small molecule carboxamides against severe acute respiratory syndrome coronavirus (SARS-- CoV) 3CLpro enzyme. Due to the structural similarity of this enzyme with SARS-CoV-2, the causative organism of the recent pandemic, the former may be used for the development of therapies against coronavirus disease 19 (COVID-19). RESULTS: The final multiple linear regression (MLR) model was based on four two-dimensional descriptors with definite physicochemical meaning. The model was strictly validated using different internal and external quality metrics. The model showed significant statistical quality in terms of determination coefficient (R2=0.748, adjusted R2 or R2 adj = 0.700), cross-validated leave-one-out Q2 (Q2=0.628) and external predicted variance R2 pred = 0.723. The final validated model was used for the prediction of external set compounds as well as to virtually design a new library of small molecules. We have also performed a docking analysis of the most active and least active compounds present in the dataset for comparative analysis and to explain the features obtained from the 2D-QSAR model. CONCLUSION: The derived model may be useful to predict the inhibitory activity of small molecules within the applicability domain of the model only based on the chemical structure information prior to their synthesis and testing.

Psychological impact of COVID-19 lockdown: An online survey from India.

BACKGROUND: The COVID-19 pandemic has led to a complete shut-down of the entire world and almost all the countries are presently in a "lockdown" mode. While the lockdown strategy is an essential step to curb the exponential rise of COVID-19 cases, the impact of the same on mental health is not well known. AIM: This study aimed to evaluate the psychological impact of lockdown due to COVID-19 pandemic on the general public with an objective to assess the prevalence of depression, anxiety, perceived stress, well-being, and other psychological issues. MATERIALS AND METHODS: It was an online survey conducted under the aegis of the Indian Psychiatry Society. Using the Survey Monkey platform, a survey link was circulated using the Whatsapp. The survey questionnaire included perceived stress scale, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Warwick-Edinburgh Mental Well-being Scale to assess perceived stress, anxiety, depression, and mental well-being, respectively. The survey link was circulated starting from April 6, 2020 and was closed on April 24, 2020. RESULTS: During the survey, a total of 1871 responses were collected, of which 1685 (90.05%) responses were analyzed. About two-fifth (38.2%) had anxiety and 10.5% of the participants had depression. Overall, 40.5% of the participants had either anxiety or depression. Moderate level of stress was reported by about three-fourth (74.1%) of the participants and 71.7% reported poor well-being. CONCLUSIONS: The present survey suggests that more than two-fifths of the people are experiencing common mental disorders, due to lockdown and the prevailing COVID-19 pandemic. This finding suggests that there is a need for expanding mental health services to everyone in the society during this pandemic situation.