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Background. The COVID 19 disease has claimed over 6.3 million lives, globally. Despite such high casualties, the treatment options are limited. Although the FDA issued emergency use authorizations for oral antivirals to treat mild-to-moderate COVID 19 disease, intravenous Remdesivir treatment remains the only fully FDA-approved antiviral. However, many early studies questioned its efficacy. Accordingly, the WHO initially recommended against its use in COVID 19 positive patients. Based on the newly emerging data, as of 22 April 2022, WHO suggests that Remdesivir can be effectively used in mild or moderate COVID 19 cases. This retrospective cohort data analysis was undertaken to evaluate and clarify the effectiveness of Remdesivir use in older US veterans. Methods. The deidentified veterans' data were accessed from the VA COVID 19 Shared Data Resources with local ethical approvals. Propensity matched cohorts with and without Remdesivir treatment were analyzed using Cox regression models, constructed in a way to avoid immortal time and calendar time biases. Limited to hospitalized veterans, patients were followed for 60 days to the outcomes of mechanical ventilation (MV) and death in separate models. The cohort was also limited to those who received low flow without high flow oxygen and a combination of low and high flow oxygen in another set of models. Results. A total of 3,372 veterans were included in this study who were hospitalized between 01 January to 31 December 2021 for COVID 19 disease. Of those, 1,686 received Remdesivir treatment, while their matches never received it. After propensity score matching that included demography, vaccination status, comorbidities, medication use, lab tests, Remdesivir recipients and controls were similar in age (66.8+/-14.1 vs. 67.0+/-13.8 years). Relative risk reductions (1-HR), 53% for MV, and 42% for death (Fig. 1) were observed with low flow oxygen and Remdesivir therapy. In veterans who received high and low flow oxygen, although there was a significant 18% reduction in risk for death, progression to MV was not significant (P=0.22). (Figure Presented) Conclusion. The data showed significant risk reductions of disease progression to MV/death when Remdesivir was used in COVID 19 positive patients with low supplementary oxygen flow, supporting the current NIH recommendation.
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Introduction The novel coronavirus disease 2019 (COVID-19) catastrophe caused significant mental threats to health care workers (HCW), especially during the first wave of the pandemic. India successfully implemented vaccination strategies in January 2021 that is likely to ameliorate the mental health impact of HCWs. The current survey aims to identify the change in impact following vaccination and address the issues affecting mental health. Objective The primary objective is to reevaluate the stress levels of radiation oncology HCWs with vaccine implementation and compare it with the mental health status at the onset of the pandemic. The secondary objective is to identify the current causative factors influencing mental health. Materials and Methods Health care workers who participated in the initial mental health impact survey at the outset of the COVID-19 pandemic from May to July 2020 were included in this study. Two hundred eligible HCWs were reassessed of the total 363 initial assessments. The 7-item Generalised Anxiety Disorder (GAD-7), 9-item Patient Health Questionnaire (PHQ-9), and 22-item Impact of Events Scale-revised (IES-R) was again served for assessing anxiety, depression, and posttraumatic stress disorder. The Mc Nemar test was used to evaluate the change and significance of the mental health impact. Univariate and multivariate analyses were done to identify the causative factors affecting mental health. Results The cohort's median age was 30 years (interquartile range [IQR]: 27-33). The incidence of moderate-to-severe level anxiety, depression, and stress significantly declined to 6.5% ( p = 0.031), 9% ( p = 0.01), and 19% ( p < 0.001) compared with 39.5, 40.5, and 30.5% during the pandemic onset. On further analysis, HCWs with affected family members had higher levels of stress ( p = 0.002). The rest of the parameters did not have significant impact on mental health outcomes. Conclusion With public education, awareness, and vaccination strategies, the second follow-up survey conducted after vaccine implementation demonstrated a significant number of HCWs in the radiation oncology community, exhibiting a decline in the incidence of anxiety, depression, and stress levels compared with the initial wave of the pandemic.
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In early 2020, WHO declared COVID-19, a pandemic disease, which severely infected human inhabitant and health. Researchers, doctors, etc., are finding ways to combat the disease. RT-PCR testing is the initial type of testing that was used to detect whether a patient is COVID (+) or COVID (−).This test kit is costly and the result takes around 6hours. So testing a heavy chunk of the population with RT-PCR is a difficult task. To counter this, X-rays/CT scan-based testing can be used to detect COVID (+) cases to control its spread. X-rays are preferable to CT as they are cheaper and even produce low radiations. The second issue that was noticed during this pandemic period was the availability of doctors. To resolve this issue, a robust automated system for early prediction is essential. Automated systems using machine learning (ML), deep learning (DL) approaches are giving promising results in the detection of COVID (+) cases. In this chapter, we propose a framework for automatic recognition of COVID (+), normal, and pneumonia cases (i.e., multiclassification) over X-ray images. In the proposed method, a dataset of COVID (+), normal, and pneumonia images is used. Initially, the dataset is preprocessed, followed by feature extraction using gray level cooccurrence matrix (GLCM), gray level difference method (GLDM), wavelet transform (WT), and fast Fourier transform (FFT) methods. Features extracted are concatenated to construct a feature pool and these features are used for multiclassification using ML algorithms: support vector machines (SVM) and XG Boost. XG Boost performs better than SVM. © 2022 Elsevier Inc. All rights reserved.
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Introduction: The emergence of the COVID-19 pandemic in 2020, have similar effect on pregnant women as influenza or other coronavirus infections. The impact of the COVID-19 pandemic is likely to be context specific and differ depending on a variety of country-specific factors. A global pandemic is likely to only reveal its consequences after significant time passes, and literature published before or immediately after policies are implemented may not capture all relevant outcomes. Material(s) and Method(s): The study was conducted in the Department of Obstetrics and Gynaecology, Gandhi Medical College, Bhopal. It included all antenatal COVID 19 patients which reported to the hospital during April 2020 to May 2021, 1st wave from April 2020 to December 2020 and second wave from Jan 2021 to May 2021 after taking due informed consent. The detailed history and full clinical and general examination were performed using a predesigned proforma. The antenatal patients were categorized into mild, moderate and severe COVID. Data on clinical manifestations, laboratory tests, maternal and perinatal outcomes were extracted and analysed. The comparisons of 1st wave and second wave was done. Result(s): There were 210 confirmed pregnant women with coronavirus disease (COVID-19). 26 maternal deaths occurred from these confirmed cases. Compared to pregnant women without COVID-19, pregnant women with a confirmed COVID-19 diagnosis had an increased risk of maternal complications and caesarean section. In initial months (April 20 to December 20) there were 89 confirmed cases of covid 19 and 4 maternal mortality and from January 21 to May 21 there were 121 cases and 22 maternal deaths. The second wave has taken greater toll on life of pregnant women. Conclusion(s): In the second wave, pregnant women with severe or critical coronavirus disease were admitted to the ICU, intubated if they require mechanical ventilation, and were at increased risk of composite morbidity. Thus, the second wave affected the pregnant women in a much serious way and the maternal as well as fetal outcome were very poor. Copyright © 2022, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.
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Teamwork skills is an essential component of engineering professional skills. Engineering colleges rely on team projects to develop students' teamwork skills. Traditionally, this is done in an in-person setting where students hold meetings, discussion, and design activities related to their projects. In this context, data has suggested an association between some personality traits and team performance, and between specific learning styles and team performance. It is unclear how this is affected in a semi-virtual environment. This research leverages the restrictions due to COVID-19 to study, in the context of a semi-virtual team-based first-year engineering course, the association between team personality diversity and team project performance. Three aspects of personality traits are considered: biogenic, sociogenic and idiogenic. © American Society for Engineering Education, 2021
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We provide the South Asian Declaration, containing the consensus guidelines for coronavirus disease 2019 (COVID-19) vaccination in cancer patients.
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Introduction: During the pandemic of COVID 19, the traditional teaching of MBBS students has been shifted to online teaching. We conduct an online survey to know and record the impact of COVID 19 lockdown on the study of medical students of GDMC, Dehradun. The present study aimed to investigate the MBBS student’s perception of online teaching. The results of this study may provide further inputs which might be of help to the students and faculty for further informed decisions. Materials and Methods: A cross sectional online survey during July 1–7, 2020 was applied to 334 medical students to evaluate the perception of online teaching among medical students. A questionnaire was prepared in Google form and divided into two sections. The first part covered demographics information of the respondent and the second part assessed with behavior and attitude toward online teaching. Results: The mean assessment, behavior, and attitude scores have significantly differed across age groups and previous experience. The medical students who had no exposure to online teaching their assessment score is higher than who had little exposure also found to be significant. The participants were agreed with the teaching way of the course (59.3%) and with the content of the course (56.9%). Majority of participants (58.4%) also agree that there are barriers in online learning. Most of the students agree (62.3%) and strongly agree (61%) that the course allowed them to take responsibility for their learning. Conclusions: We can conclude that the online learning program is a good alternative to classroom teaching in this era of the COVID 19 pandemic. This study can provide the basic architecture for making further strategy of course content.