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The Journal of the Association of Physicians of India ; 69(7):11-12, 2021.
Article in English | Scopus | ID: covidwho-1431386

ABSTRACT

BACKGROUND: Since its first identification in December 2019, in WUHAN (CHINA), SARS-COV-2, causative agent of Corona virus pandemic, has affected millions of people worldwide, causing thousands of death. There is much speculation about the interplay between ACEI/ARB and Corona virus infection, as for internalization into host cell SARS-COV-2 binds through S spike protein to ACE-2, aided TMPRSS2. METHODS: A record based observational study has been conducted (data obtained from the clinics of fourteen physicians) in two worst affected districts of West Bengal, to find out the association of ACEI/ARB on patients, suffering from Corona virus infection. The study-protocol has already been approved by Clinical Research Ethics Committee of Calcutta School of Tropical Medicine. (IEC Ref. No: CREC-STM/2020-AS-37) Results: Increasing age, male sex and presence of co-morbidities (viz. Diabetes, COPD) are significantly associated with the occurrence of moderate and severe disease. Drugs (viz. ACEI/ARB), though are associated with less severe disease, have not achieved statistical significance, in the present study. CONCLUSION: Drugs, like ACEI/ARB, should be continued in patients suffering from COVID-19 infection, (if they are already on these drugs). © Journal of the Association of Physicians of India 2011.

2.
Journal of Association of Physicians of India ; 69(7):28-33, 2021.
Article in English | Scopus | ID: covidwho-1361002

ABSTRACT

Background: Since its first identification in December 2019, in WUHAN (CHINA), SARS-COV-2, causative agent of Corona virus pandemic, has affected millions of people worldwide, causing thousands of death. There is much speculation about the interplay between ACEI/ARB and Corona virus infection, as for internalization into host cell SARS-COV-2 binds through S spike protein to ACE-2, aided TMPRSS2. Methods: A record based observational study has been conducted (data obtained from the clinics of fourteen physicians) in two worst affected districts of West Bengal, to find out the association of ACEI/ARB on patients, suffering from Corona virus infection. The study-protocol has already been approved by Clinical Research Ethics Committee of Calcutta School of Tropical Medicine. (IEC Ref. No: CREC-STM/2020-AS-37) Results: Increasing age, male sex and presence of co-morbidities (viz. Diabetes, COPD) are significantly associated with the occurrence of moderate and severe disease. Drugs (viz. ACEI/ARB), though are associated with less severe disease, have not achieved statistical significance, in the present study. Conclusion: Drugs, like ACEI/ARB, should be continued in patients suffering from COVID-19 infection, (if they are already on these drugs). © 2021 Journal of Association of Physicians of India. All rights reserved.

3.
Advanced Theory and Simulations ; : 10, 2021.
Article in English | Web of Science | ID: covidwho-1224625

ABSTRACT

A machine learning assisted efficient, yet comprehensive characterization of the dynamics of coronaviruses, in conjunction with finite element (FE) approach, is presented. Without affecting the accuracy of prediction in low-frequency vibration analysis, an equivalent model for the FE analysis is proposed, based on which the natural frequencies corresponding to first three non-rigid modes are analyzed. To quantify the inherent system-uncertainty efficiently, Monte Carlo simulation is proposed in conjunction with the machine learning based FE computational framework for obtaining complete probabilistic descriptions considering both individual and compound effect of stochasticity. A variance based sensitivity analysis is carried out to enumerate the relative significance of different material parameters corresponding to various constituting parts of the coronavirus structure. Using the modal characteristics like natural frequencies and mode shapes of the virus structure including their stochastic bounds, it is possible to readily identify coronaviruses by comparing the experimentally measured dynamic responses in terms of the peaks of frequency response function. Results from this first of its kind study on coronaviruses along with the proposed generic machine learning based approach will accelerate the detection of viruses and create efficient pathways toward future inventions leading to cure and containment in the field of virology.

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