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Minimizing the effects of COVID-19 is one of the world's highest priorities, and India is taking essential measures to curb the outbreak. Many businesses promoted social distancing through the announcement of compulsory work-from-home. In this backdrop, Remote Work has become a significant model mainly to retain talent and attrition. The present study analyses whether work-life integration and work-life satisfaction are related to psychological well-being. Research hypotheses are formulated based on the investigation of the literature review. This data was collected in India during July and October 2020 while the Covid pandemic was coming down to normal. The survey was aimed at people who work in Indian-based companies. Workers received an electronic version of the questionnaire, and organizations received e-mail requests. The decision was made to use the comfort sampling method. During the pandemic, a sample of 400 employees from the Information Technology industry in the cities of Hyderabad and Bangalore, India. Work-Life integration leading to Psychological Well-being were analyzed considering the role of work-life satisfaction as a mediator. This research focuses on job and life satisfaction, showing the mechanisms that help workers in remote working conditions stay mentally healthy during a pandemic. This mediation study revealed that Work-life satisfaction has an optimistic and robust relationship with psychological health, with this relationship being influenced by work satisfaction and life satisfaction among the Indian IT companies surveyed. These studies' findings suggest that a successful workfrom-home community is built on a foundation of work-life integration based on contentment. The research findings are that positive work-life integration contributes to employees' positive levels of psychological well-being. We found that the higher the job and life satisfaction, the better the psychological well-being. Since the current study is a cross-sectional analysis, there are some restrictions on how long the investigation can last. Longitudinal research should be conducted to improve the results of the studies.
ABSTRACT
Introduction: Mucosa-associated lymphoid tissue (MALT) lymphomas are extra-nodal marginal zone B-cell lymphomas, most commonly found in the stomach, associated with Helicobacter pylori infections, and generally not linked with inflammatory bowel disease. Rectal MALT lymphoma is very uncommon and often associated with painful defecation, change in bowel habits, or rectal pressure/prolapse. Here, we present a rare case of an asymptomatic female with ulcerative colitis (UC) found to have benign-appearing rectal polyps during a routine screening colonoscopy. Case Description/Methods: The patient is a 56-year-old female with a history of left-sided UC, diagnosed in 1993, with one flare after receiving the 2nd dose of the Moderna COVID-19 vaccine, taking oral Olsalazine 500 mg twice daily, low-dose Prednisone, and mesalamine suppositories as needed presenting for screening colonoscopy. The patient was asymptomatic, citing regular non-bloody bowel movements and normal stool consistency. Colonoscopy revealed two 7 mm sessile, non-bleeding rectal polyps, surrounded by congested, erythematous, friable, and ulcerated mucosa in the rectosigmoid colon. Cold forceps biopsies were taken. Hematopathology evaluation of the routine colon biopsy samples revealed chronic nonspecific colitis while pathology of the rectal polyps showed marked lymphoplasmacytic infiltrate and extra-nodal marginal zone lymphoma of MALT. Ancillary studies, immunohistochemistry, and molecular studies for B-cell gene rearrangement confirmed extra-nodal marginal zone lymphoma of MALT with prominent plasmacytic differentiation. The patient was informed and close follow-up in Gastroenterology clinic was arranged. (Figure) Discussion: Rectal MALT lymphoma is rare with unclear management options. Treatments of UC include watchful waiting, surgical resection, endoscopic mucosal resection, radiation, and/or chemotherapy. Helicobacter pylori infections, though strongly linked with gastric MALT lymphoma, have not been shown to be strongly correlated with rectal MALT lymphoma. Given that patients with UC have chronic UCassociated colonic inflammation, lymphoma is often difficult to distinguish visually during colonoscopy, frequently masked by ulcerations and pseudo-polyps. In cases like these, more definitive treatments such as surgical resection could therefore be warranted. Long-term follow-up data is sparse and definitive management remains a clinical conundrum, thus these patients require reliable long-term multidisciplinary close follow-up. (Figure Presented).
ABSTRACT
The novel corona virus disease (COVID-19) is a pandemic, and quarantines are helping to stop it. However, defections of confined individuals around the world are causing considerable concern. This study examines the impact of artificial intelligence methods in the retail business utilizing a qualitative concept research approach. It's encouraging to see the retail sector steadily getting back on track following the impact of COVID-19. This hand band device will easily find the COVID-19 affected. Persons are identified by a 5-metre radius, and they are warned by vibrating. Finally, this chapter provides a conceptual framework for speeding up the struggle to prevent the spread of COVID-19 in India by combining IoT and AI technologies used to combat COVID-19 outbreaks around the world. Business decision-makers and managers should prepare their organizations and workers for the upcoming market shifts based on the findings of this research. © 2023, IGI Global. All rights reserved.
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Background and Aim: We aimed to study the healthcare disparity among elderly patients with cirrhosis and its impact on composite outcomes during the COVID pandemic. Methods: We conducted a prospective observational study between July 21- May 2022 via telephonic interview or physical consultations at 2 large tertiary care centers in India. The data was captured using pre-validated questionnaires to assess healthcare disparity amongst elderly patients with cirrhosis. Results: Of 475 elderly (≥65years) patients with cirrhosis (based on imaging) were included with mean age of 72.1years, male:female(3.8:1) with aetiology of cirrhosis being NAFL/NASH (58%), alcohol(32%) in majority. 323 (69%) had access to internet, however amongst them only 247 (52%) were conversant with use of WhatsApp, the preferred platform for tele-consultations in most hospitals in India. 147(31%) had walkability and accessibility to hospitals and 223(47%) had access to a family physician or community nurse, 271 (67%) had laboratory access. 295(62%) had taken both Covid vaccines, 349(74%) had first dose alone and only 38(8%) had booster dose. 213 (45%) felt that they needed admission/ expert opinion at least once during the period with 137 (29%) needed inpatient treatment. 266 (56%) missed at least 15 days doses of drugs with 85(32%) citing unavailability and 48(18%) unaffordability. Cancellation/rescheduling of medical services was widely prevalent with 74(15.5%) needing to reschedule endoscopy, 132 (28%) had cancellation of imaging of any form at least once, loco-regional therapy was rescheduled in 14(18%) patients of HCC. Advanced age, presence of >1 co-morbidity, tense ascites, hepatic encephalopathy, lack of caregivers were signi cantly associated with poor quality of life, restricted access and utilization of healthcare Conclusions: Elderly patients with cirrhosis had significant barriers in seeking healthcare during Covid19 pandemic highlighting the disparity and need for concerted e orts to improve their access to care.
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Coronaviruses are a large viral family that attacks key organs, particularly the lungs. The infection spread is growing by the day, affecting almost every industry. Various Artificial Intelligence studies have been proposed, to learn the measurable information of people who have been affected with COVID-19 and those who have recovered, as well as the death rate. Various data samples like chest images, lung images, swab results, blood samples, and CT scans are used to predict the COVID-19. The paper gives an in-depth look at how AI and machine learning techniques can be used to accurately predict COVID-19. The proposed review is centered around investigating the different AI methods, models, and logical registering procedures used in foreseeing the COVID-19 sickness. The study also summarizes the difficulties associated with current methods and future exploration works.
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Background:Healthcare workers are at a high risk of contracting SARS CoV 2 infection due to their close contact with COVID19 confirmed and suspected cases. Preventing infection amongst healthcare workers is crucial, not only for main- taining a healthy and functional workforce during the pandemic, but also to reduce secondary transmission to collegues and other patients. Prevalence of IgG antibodies against the infection provides essential information regarding unde- tected infection and transmission. The present study is being conducted to estimate the seroprevalence of SARS CoV 2 antibodies among healthcare workers working in COVID19 isolation wards. Methods:90 healthcare workers working in covid isolation wards were recruited into the study. A questionnaire was administered for risk assessment and history of previous RT-PCR confirmed COVID-19 infection,if any. Serum sample collected from the participants were tested for anti SARS CoV 2 IgG antibodies by Indirect ELISA (Covid Kawach IgG Mi- crolisa by J Mitra). Results:Out of 48 samples processed so far, 16(33.3%) samples were positive for SARS CoV 2 IgG antibody.of the 16 pos- itive samples, 14 samples were negative by RT-PCR previously. The remaining results will be produced at the time of the presentation. Conclusions:Presence of anti SARS CoV 2 IgG antibodies among healthcare workers at high risk, who tested negative by RT-PCR previously can indicate a previou asymptomatic infection, which calls for further evaluation
ABSTRACT
The proposed work is focused on COVID-19 classification of cough sounds based on machine learning which is used to differentiate COVID-19 coughs from non COVID-19 and healthy coughs. It follows a non-contact based screening test which is very easy to apply being non-invasive and simply carried out within the boundaries of home so that the medical testing centers are not over flooded with patients and there is an overwhelming pressure because of maintenance of those patients with shortage of adequate infrastructure facilities. The dataset used in this study has been derived from the Coswara database which comprises of around 160 infected and 480 healthy individuals. Therefore, Artificial Intelligence based machine learning classifiers were used as an alternative means of diagnosis. Logistic regression (LR), K- Nearest neighbor (KNN), support vector machines (SVM), decision tree algorithms were used as classifiers in the proposed work. The results of this study show that the SVM classifier turned out to be the best in comparing among the COVID-19 and non COVID- 19 coughs with area under receiver operating characteristic curve (ROC) of 0.98. The novelty in the proposed work includes the collection of dry cough samples which would aid in preliminary diagnosis of the infection. This form of classification can also be implemented in a smart phone after performance evaluation from medical authorities. © 2021 IEEE.