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Small bowel malignant tumours make only 2% of all gastrointestinal (GI) malignancies. Small bowel leiomyosarcoma (LMS) is further rare, accounts for only 0.1%–3% fraction of these tumours. These cases can present as asymptomatic intra-abdominal mass, anaemia due to GI bleed or acute abdomen such as perforation peritonitis, intussusception and bowel ischaemia. Standard of care is surgical resection. Our case presented as large lobulated exophytic ileal LMS measuring 10.8×11×14.7 cm involving multiple small bowel loops and abutting right iliac vessels and uterus. Patient’s clinical course was complicated with COVID-19 positivity, deep vein thrombosis and pulmonary thromboembolism. She was managed by preoperative anticoagulation followed by resection of the tumour with end ileostomy.

: A highly infectious and life-threatening virus was first reported in Wuhan, China, in late 2019, and it rapidly spread all over the world. This novel virus belongs to the coronavirus family and is associated with severe acute respiratory syndrome (SARS), causing respiratory disease known as COVID-19. In March 2020, WHO has declared the COVID-19 outbreak a global pandemic. Its morbidity and mortality rates are swiftly rising day by day, with the situation becoming more severe and fatal for the comorbid population. Many COVID-19 patients are asymptomatic, but they silently spread the infection. There is a need for proper screening of infected patients to prevent the epidemic transmission of disease and for early curative interventions to reduce the risk of developing severe complications from COVID-19. To date, the diagnostic assays are of two categories, molecular detection of viral genetic material by real-time RTpolymerase chain reaction and serological test, which relies on detecting antiviral antibodies. Unfortunately, there are no effective prophylactics and therapeutics available against COVID-19. However, a few drugs have shown promising antiviral activity against it, and these presently are being referred for clinical trials, albeit FDA has issued an Emergency Use Authorization (EUA) for the emergency use of a few drugs for SARSCoV- 2 infection. This review provides an insight into current progress, challenges and future prospects of laboratory detection methods of COVID-19, and highlights the clinical stage of the major evidence-based drugs/vaccines recommended against the novel SARS-CoV-2 pandemic virus.

Abstract This research work assesses the volatility and leverage effect of the selected stock markets before and during the outburst of the Covid-19 of the selected international stock markets. This study is based on daily time-series data obtained from yahoo.finance database. The period of the study has been considered from 1 September 2019 to 31 December 2019, before the period of Covid-19, and from 1 January 2020 to 30 April 2020, during the period of Covid-19. In the course of analysis, descriptive statistics, the GARCH model, the EGARCH model, and the TGARCH model have been used. The GARCH test results show that volatility existed before and during the period of Covid-19. Both E-GARCH and T-GARCH test results show that the leverage effect existed before and during the period of Covid-19. So, the impact of the Covid-19 pandemic in terms of bad news has a bigger influence on international stock markets.

Abstract The COVID-19 pandemic has drastically altered every aspect of the global economy and has presented HR leaders with the most pressing demand of balancing the interest of employees and their respective organizations. It has also played the role of a catalyst by expediting the adoption of technology at an unprecedented pace and scale. Organizations world over have turned to technology for finding innovative solutions to some of these global challenges. In this reflective paper we have discussed some fundamental implications arising from the impact of COVID-19 on various HRM functions, how technology is playing an enabling and facilitating role for HRM, insightful predictions based on how organizations are responding and finally, also the way forward for HRM in the post COVID-19 world. We have specifically emphasized on the various digital innovations such as AI, machine learning, cloud computing, IoT etc. that are being harnessed to aid the HRM function. A brief discussion on how the market for HRM technology products is evolving, is also incorporated for wider contextual understanding. This paper will also present a summary of some of the best HRM practices adopted by the HR departments of leading global organizations. With a comprehensive analysis of these innovative technologies and creative HR ideas, we have presented a holistic picture of how HRM is evolving during COVID-19 pandemic.

Abstract The influence of reduction in emissions on the inherent temporal characteristics of PM2.5 and NO2 concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM2.5 and NO2 concentration in urban cities, causing low concentrations. It is observed that the original PM2.5 and NO2 concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM2.5 and NO2 concentrations. Hurst exponent of the PM2.5 and NO2 concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.

Abstract The aim of this paper is two-fold. First, we investigate the nexus between investor attention to COVID-19 and daily returns in 59 countries. We use Google Search Volume Index to account for investor attention. Our empirical findings suggest that the search volume of the pandemic is negatively associated with daily returns. The effect was strong in the week that the World Health Organization declared it as pandemic and among advanced countries. Second, we explore the relationship between search volume and market volatility. The findings suggest that COVID-19 sentiment generated excess volatility in the market. Our findings remain robust with alternative specifications.

AbstractPurposeTo advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics by providing large-scale annotations of the abnormalities in frontal CXRs in BIMCV-COVID19+ database, and to provide a robust evaluation mechanism to facilitate its usage.Materials and MethodsWe provide the abnormality annotations in frontal CXRs by creating bounding boxes. The frontal CXRs are a part of the existing BIMCV-COVID19+ database. We also define four different protocols for robust evaluation of semantic segmentation and classification algorithms. Finally, we benchmark the defined protocols and report the results using popular deep learning models as a part of this study.ResultsFor semantic segmentation, Mask-RCNN performs the best among all the models with a DICE score of 0.43 ± 0.01. For classification, we observe that MobileNetv2 yields the best results for 2-class and 3-class classification. We also observe that deep models report a lower performance for classifying other classes apart from the COVID class.ConclusionBy making the annotated data and protocols available to the scientific community, we aim to advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics. This large-scale data will be useful for ML algorithms and can be used for learning radiological patterns observed in COVID-19 patients. Further, the protocols will facilitate ML practitioners for unified large-scale evaluation of their algorithms.Data Availability StatementThe data associated with this work is available here : Radiologists’ Annotations on COVID-19+ X-rays https://osf.io/b35xu/ via @OSFramework andhttp://covbase4all.igib.res.in/.

Background Healthcare workers (HCWs) involved in administration and patient management during COVID-19 pandemic are at high risk of developing psychological problems related to fear and stress of contacting COVID infection. This is augmented by the stigma faced at home and society, owing to the nature of their job. Aim To assess the mental health issues and stigma amongst health care workers involved in COVID care. Methodology We conducted a hospital based cross sectional study where 150 health care workers involved in the care of COVID-19 patients, directly and indirectly, were selected using systematic random sampling. They were assessed using Depression, Anxiety and Stress Scale (DASS-21) Hindi Version, The Impact of Event Scale - Revised scale and a Modified Stigma scale. Results Significant psychological stress, anxiety, depression and high risk for developing post-traumatic stress disorder was found in more than half of the healthcare workers, albeit more in those having direct contact with COVID patients (p < 0.05). Stigma was significantly reported in most HCWs, especially with concerns regarding public attitude and disclosure of their work profile. Conclusion Healthcare Workers are at a higher risk for developing psychological disorders and post-traumatic stress disorder because of the immensely stressful work-related conditions and stigma related to working with COVID patients. This may lead to long lasting psychosocial consequences which may affect more severely than the infection itself. Early identification of psychological issues of HCWs and timely intervention is the key.