Safety and efficacy of prophylactic anticoagulation versus therapeutic anticoagulation in hospital-admitted COVID-19 patients: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: COVID-19 disease-related coagulopathy and thromboembolic complication, an important aspect of the disease pathophysiology, are frequent and associated with poor outcomes, particularly significant in hospitalized patients. Undoubtedly, anticoagulation forms a cornerstone for the management of hospitalized COVID-19 patients, but the appropriate dosing has been inconclusive and a subject of research. We aim to review existing literature and compare safety and efficacy outcomes of prophylactic and therapeutic dose anticoagulation in such patients. METHODS: We did a systematic review and meta-analysis to compare the efficacy and safety of prophylactic dose anticoagulation when compared with therapeutic dosing in hospitalized COVID-19 patients. We searched PubMed, Google Scholar, EMBASE and COCHRANE databases from 2019 to 2021, without any restriction by language. We screened records, extracted data and assessed the risk of bias in the studies. RCTs that directly compare therapeutic and prophylactic anticoagulants dosing and are not placebo-controlled trials were included. Analyses of data were conducted using the Mantel-Haenszel random-effects model (DerSimonian-Laird analysis). The study is registered with PROSPERO (CRD42021265948). RESULTS: We included three studies in the final quantitative analysis. The incidence of thromboembolic events in therapeutic anticoagulation was lower in comparison with prophylactic anticoagulation in hospitalized COVID-19 patients and reached statistical significance [RR 1·45, 95% CI (1.07, 1.97) I2 -0%], whereas major bleeding as an adverse event was found lower in prophylactic anticoagulation in comparison with therapeutic anticoagulation that was statistically significant [RR 0·42, 95% CI(0.19, 0.93) I2 -0%]. CONCLUSION: Our study shows that therapeutic dose anticoagulation is more effective in preventing thromboembolic events than prophylactic dose but significantly increases the risk of major bleeding as an adverse event. So, the risk-benefit ratio must be considered while using either of them.

Molecular basis of SARS-CoV-2 Omicron variant evasion from shared neutralizing antibody response (preprint)

A detailed understanding of the molecular features of the neutralizing epitopes developed by viral escape mutants is important for predicting and developing vaccines or therapeutic antibodies against continuously emerging SARS-CoV-2 variants. Here, we report three human monoclonal antibodies (mAbs) generated from COVID-19 recovered individuals during first wave of pandemic in India. These mAbs had publicly shared near germline gene usage and potently neutralized Alpha and Delta, but poorly neutralized Beta and completely failed to neutralize Omicron BA.1 SARS-CoV-2 variants. Structural analysis of these three mAbs in complex with trimeric spike protein showed that all three mAbs are involved in bivalent spike binding with two mAbs targeting class-1 and one targeting class-4 Receptor Binding Domain (RBD) epitope. Comparison of immunogenetic makeup, structure, and function of these three mAbs with our recently reported class-3 RBD binding mAb that potently neutralized all SARS-CoV-2 variants revealed precise antibody footprint, specific molecular interactions associated with the most potent multi-variant binding / neutralization efficacy. This knowledge has timely significance for understanding how a combination of certain mutations affect the binding or neutralization of an antibody and thus have implications for predicting structural features of emerging SARS-CoV-2 escape variants and to develop vaccines or therapeutic antibodies against these.

Incidence, treatments, and outcomes of SARS-CoV-2 and HIV co-infections

Background: We investigated the global threat of co-infection of severe acute respiratory coronavirus 2 (SARS-CoV-2) to patients living with another prevalent viral infection HIV. We have analyzed symptom status, treatment, and outcome of co-infected individuals.

The profiles of first and second SARS-CoV-2 waves in the top ten COVID-19 affected countries

In March 2020, the World Health Organization (WHO) acknowledged the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a "public health emergency of international concern." Within a month, coronavirus disease 2019 (COVID-19) was declared a pandemic. As of 21 July 2021, 192.8 million cases and 4.13 million deaths have been attributed to COVID-19 worldwide. Here we discuss the data from top ten COVID-19 affected countries, with an emphasis on the average strolling period of 6 to 8 months between first and second wave in these nations. Our study ascertains that analysis of the data from countries temporally ahead of others during the pandemic gives policymakers the chance to strategize and postpone or mitigate subsequent COVID-19 waves. With governments throughout the globe continuing their immunisation efforts, a study of the key indicators of COVID-19 waves from the top ten countries is critical to preparing the healthcare system to save millions of lives.

A perspective on SARS-CoV-2 and community transmission in the top COVID-19 affected nations

In most countries, during the initial months of the COVID-19 outbreak resources were directed to mitigation measures that prevented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission from symptomatic individuals. The coverage of the pre-symptomatic and asymptomatic individuals' testing required intensive clinical sampling along with rigorous symptom-based screening. Based on the SARS-CoV-2 transmission, the disease outbreak across nations was divided into four distinct stages: (i) epidemic, (ii) community transmission, (iii) local transmission, and (iv) imported cases. Here we discuss the COVID-19 community transmission stage for the top ten COVID-19 affected nations. Epidemic dynamics and policies implemented to contain the spread of SARS-CoV-2 infection varied globally. Further, the mitigation strategies and related health policies for dealing with the pandemic were based on the stages of transmission of the disease. We suggest that correctly identifying the transmission stage during a pandemic ensures the implementation of mitigation strategies. Thus prompt analysis of the status of COVID-19 transmission stage(s) in any nation seems crucial to direct health policies accordingly.

Structural insights for neutralization of BA.1 and BA.2 Omicron variants by a broadly neutralizing SARS-CoV-2 antibody (preprint)

The SARS-CoV-2 BA.1 and BA.2 (Omicron) variants contain more than 30 mutations within the spike protein and evade therapeutic monoclonal antibodies (mAbs). Here, we report a receptor binding domain (RBD) targeting human antibody (002-S21F2) that effectively neutralizes live viral isolates of SARS-CoV-2 variants of concern (VOCs) including Alpha, Beta, Gamma, Delta, and Omicron (BA.1 and BA.2) with IC50 ranging from 0.02 - 0.05 ug/ml. This near germline antibody 002-S21F2 has unique genetic features that are distinct from any reported SARS-CoV-2 mAbs. Structural studies of the full-length IgG in complex with spike trimers (Omicron and WA.1) reveal that 002-S21F2 recognizes an epitope on the outer face of RBD (class-3 surface), outside the ACE2 binding motif and its unique molecular features enable it to overcome mutations found in the Omicron variants. The discovery and comprehensive structural analysis of 002-S21F2 provide valuable insight for broad and potent neutralization of SARS-CoV-2 Omicron variants BA.1 and BA.2.

JAYA-Optimized Fuzzy Reinforcement Learning Classifier for COVID-19

In the proposed work, Reinforcement Learning has been used to identify Coronavirus disease (COVID-19) amongst viral pneumonia and normal conditions using chest X-ray images. Here, the JAYA-Optimized Fuzzy Reinforcement Learning algorithm, in conjunction with Wavelet Transform-based feature extraction and Principal Component Analysis-based feature reduction technique, has been applied to construct the proposed classifier. Predication accuracy achieved with the proposed approach for COVID-19, viral pneumonia, and normal cases is 87.75%, 95.63%, and 96.89%, respectively. The average classification accuracy is 93.43%. The results show that the JAYA-Optimized Fuzzy Reinforcement Learning approach attains high accuracy and is superior to other contemporary classifiers, i.e. Support Vector Machine, Artificial Neural Network, Tree Algorithm, and K-Nearest Neighbor. The work aims to develop a classifier that can segregate COVID-19 cases from normal and viral pneumonia cases based on the chest X-ray images and is online, adaptive, accurate, and fast. The classifier could serve as a diagnostic tool for healthcare professionals in the current pandemic. [ FROM AUTHOR] Copyright of IETE Journal of Research is the property of Taylor & Francis Ltd 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 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 . (Copyright applies to all s.)

Knowledge, attitude, practice, and post-infection effects regarding COVID-19, and vaccine acceptance among general people of North India -- A multicentred cross-sectional study. (preprint)

Purpose: This study aimed to assess COVID-19 awareness, attitude, practice, and post-infection effects, as well as vaccine acceptance, among general people in North India at the end of the first COVID-19 wave in India. Method: A cross-sectional, multicentred survey took place from 20 January to 28 February 2021 in Northern India, covering 8 states, including Haryana, Punjab, Himachal Pradesh, Uttar Pradesh, Uttarakhand, Jammu and Kashmir, Delhi, and Chandigarh to assess the KAP in the context of COVID-19. A validated questionnaire was distributed via social media (Instagram, WhatsApp), and responses were collected via a Google Form. Result: The total number of participants was n=813, out of which more than half of the responder were male (54.2%). The majority of respondents belong to the age category 18 to 29 years (66.9%) and (85.8%) of respondents come from a nuclear family. Mean (percentage) scores for knowledge, attitude, and practice were 57.91%, 60.98%, and 89.79% respectively. The vaccine acceptance in participants was quite high (70.8%), (67.7%) have a positive belief in vaccine efficacy, and (42.7%) of respondents were willing to take the vaccine at a health centre/clinic. Out of total participants (74.5%) believe that vaccines can control the spread of COVID-19. The prevalence of post-infection effects was more in females with a mean percentage of 52.94, like (94.1%) of females and (70.4%) of males. Conclusion: Although the overall KAP regarding COVID-19 disease was average and balanced in some areas (69.56% overall in all categories of KAP) in our participants. Our result finding shows that there are certain gaps in knowledge, attitude, and practice of participants which can be decreased by efficient and tailor-made health education initiatives. Due to these gaps, India is facing a second wave of COVID-19

SARS-CoV-2 inactivation by human defensin HNP1 and retrocyclin RC-101 (preprint)

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 is an enveloped virus responsible for the COVID-19 respiratory disease pandemic. While induction of adaptive antiviral immunity via vaccination holds promise for combatting the pandemic, the emergence of new potentially more transmissible and vaccine-resistant variants of SARS-CoV-2 is an ever-present threat. Thus, it remains essential to better understand innate immune mechanisms that are active against the virus. One component of the innate immune system with broad anti-pathogen, including antiviral, activity is a group of cationic immune peptides termed defensins. The defensins' ability to neutralize enveloped and non-enveloped viruses and to inactivate numerous bacterial toxins correlate with their ability to promote the unfolding of thermodynamically pliable proteins. Accordingly, we found that human neutrophil -defensin HNP1 and retrocyclin RC-101 destabilize SARS-CoV-2 Spike protein and interfere with Spike-mediated membrane fusion and SARS-CoV-2 infection in cell culture. We show that HNP1 binds to Spike with submicromolar affinity. Although binding of HNP1 to serum albumin is more than 20-fold weaker, serum reduces the anti-SARS-CoV-2 activity of HNP1. At high concentrations of HNP1, its ability to inactivate the virus was preserved even in the presence of serum. These results suggest that specific - and {theta}-defensins may be valuable tools in developing SARS-CoV-2 infection prevention strategies.

Deep Learning Assisted COVID-19 Detection using full CT-scans

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing the computational requirements. This work proposes an automated diagnosis of COVID-19 infection from CT scans of the patients using deep learning technique. The proposed model, ReCOV-101, uses full chest CT scans to detect varying degrees of COVID-19 infection. To improve the detection accuracy, the CT-scans were preprocessed by employing segmentation and interpolation. The proposed scheme is based on the residual network that takes advantage of skip connection, allowing the model to go deeper. The model was trained on a single enterprise-level GPU. It can easily be provided on a network’s edge, reducing communication with the cloud, often required for larger neural networks. This work aims to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can be combined with medical equipment and help ease the examination procedure. With the proposed model, an accuracy of 94.9% was achieved.

HOW TO SPOT COVID-19 PATIENTS: SPEECH & SOUND AUDIO ANALYSIS FOR PRELIMINARY DIAGNOSIS OF SARS-COV-2 CORONA PATIENTS (preprint)

Background: The global cases of Covid-19 increasing day by day. On Nov. 25, 2020, a total of 59,850,910 cases reported globally with a 1,411,216 global death. In India, total cases in the country now stand at 91,77,841 including 86,04,955 recoveries and 4,38,667 active cases as of Nov. 24, 2020, as per data issued by ICMR. A new generation of voice/audio analysis application which can tell whether the person is suffering from COVID-19 or not. Aims: To describe how to establish a new generation of voice/audio analysis applications to identify the suspected covid-19 hidden cases in hotspot areas with the help of an audio sample of the general public. Materials & Methods: The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public. Results: The collection of the audio sample will be done from the already suffered covid-19 patients in (.Wave files) personally or through phone calls. The audio samples like the sound of the cough, the pattern of breathing, respiration rate, and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody, and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample. Discussion: The voice/audio application current project can be merged with a mobile App called “Aarogya Setu” by Govt. of India. The project can be implemented in the high-risk area of Covid-19 in the country. Conclusion: This new method of detecting cases will decrease the workload in the covid-19 laboratory.

Standard Operating Protocol: Operating on COVID-19 patients

Due to lack of scientific evidence, guidelines cannot be formulated. Hence this protocol would serve as STANDARD OPERATING PROCEDURE (SOP) for performing neurosurgical procedures on COVID-19 suspect, probable or positive cases. This SOP is based on recommendations from various societies and experiences of the hospitals involved in care of patients with COVID-19. As the status of COVID-19 in Nepal change along with refinement in knowledge of the disease and its management, SOP would be updated. Objective is to “Serve with caution”. Nepal is presently in phase 2 of Global COVID-19 p The doubling time for active infections is shortening over the last two weeks (Figure 1). The healthcare facilities have to gear up and prepare to face the worst with the most efficient use of available resources. Neurosurgical emergencies may arise in COVID-19 patients (suspect or probable or positive) who are being managed in isolated wards or ICU. Such patients cannot be operated in routine operating rooms nor kept in routine ICU, as they pose threat of transmission of infection to other patients and health care workers (HCW). COVID-19 patients undergoing aerosol generating procedures (AGPs) pose higher risk of transmission of the SARS-CoV-2 virus. Special isolation precautions should be adopted by all health care workers (HCW) with specific protocols to stay safe as well as manage the case efficiently.

Characterization of neutralizing versus binding antibodies and memory B cells in COVID-19 recovered individuals from India (preprint)

India is one of the countries most affected by the recent COVID-19 pandemic. Characterization of humoral responses to SARS-CoV-2 infection, including immunoglobulin isotype usage, neutralizing activity and memory B cell generation, is necessary to provide critical insights on the formation of immune memory in Indian subjects. In this study, we evaluated SARS-CoV-2 receptor-binding domain (RBD)-specific IgG, IgM, and IgA antibody responses, neutralization of live virus, and RBD-specific memory B cell responses in pre-pandemic healthy versus convalescent COVID-19 individuals from India. We observed substantial heterogeneity in the formation of humoral and B cell memory post COVID-19 recovery. While a vast majority (38/42, 90.47%) of COVID-19 recovered individuals developed SARS-CoV-2 RBD-specific IgG responses, only half of them had appreciable neutralizing antibody titers. RBD-specific IgG titers correlated with these neutralizing antibody titers as well as with RBD-specific memory B cell frequencies. In contrast, IgG titers measured against SARS-CoV-2 whole virus preparation, which includes responses to additional viral proteins besides RBD, did not show robust correlation. Our results suggest that assessing RBD-specific IgG titers can serve as a surrogate assay to determine the neutralizing antibody response. These observations have timely implications for identifying potential plasma therapy donors based on RBD-specific IgG in resource-limited settings where routine performance of neutralization assays remains a challenge. ImportanceOur study provides an understanding of SARS-CoV-2-specific neutralizing antibodies, binding antibodies and memory B cells in COVID-19 convalescent subjects from India. Our study highlights that PCR-confirmed convalescent COVID-19 individuals develop SARS-CoV-2 RBD-specific IgG antibodies, which correlate strongly with their neutralizing antibody titers. RBD-specific IgG titers, thus, can serve as a valuable surrogate measurement for neutralizing antibody responses. These finding have timely significance for selection of appropriate individuals as donors for plasma intervention strategies, as well as determining vaccine efficacy.

An Adaptive, Interacting, Cluster-Based Model Accurately Predicts the Transmission Dynamics of COVID-19 (preprint)

The SARS-CoV-2 driven disease, COVID-19, is presently a pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. In order to launch both containment and mitigation measures, each country requires accurate estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India, and show that our approach predicts state-wise COVID-19 spread for each country with high accuracy. We show that R0 as the basic reproduction number exhibits significant spatial and temporal variation in these countries. However, by including a new function for temporal variation of R0 in an adaptive fashion, the predictive model provides highly reliable estimates of asymptomatic and undetected COVID-19 patients, both of which are key players in COVID-19 transmission. Our dynamic modeling approach can be applied widely and will provide a new fillip to infectious disease management strategies worldwide.

Corona Virus (COVID-19) and its Impact on Health Care Workers

Pandemics always come up with various life-threatening issues. COVID-19 outbreak came up with the same issues along with certain other problems involving public, administrative and healthcare sector concerns. It resembled the SARS outbreak but posed such challenges against the world that are uneasy to handle. The disease which started from Wuhan, China has now affected almost every country in a ruthless manner. Healthcare workers are working day and night just to protect the citizens despite being at high-risk exposure and they are being aimed by the virus due to shortage of Personal Protection Equipment kits. Not only this, but they are being brutally harassed by the patients themselves. Social, economic, psychiatric and many other factors are responsible for deteriorating the health of these frontline healthcare workers who are now being allegedly regarded as "Healthcare Warriors".