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Introduction: The clinical outcomes of COVID-19 infection in children with cancer have been variable worldwide. Therefore, we aimed to collect data from all regions in India through a national collaborative study and identify factors that cause mortality directly related to COVID-19 infection. Method(s): Data was collected prospectively on children across India on cancer therapy and diagnosed with COVID-19 infections from 47 centers from April 2020 to October 2021. Information was recorded on the demographics, the number of children that required intervention, and the outcome of the infection. In addition, we analyzed the impact of the delta variant in 2021. Result(s): A total of 659 children were studied, of whom 64% were male and 36% were female. The data from the eastern region was sparse, and this was a collection bias. COVID-19 infection was predominantly seen in children less than five years. The delta variant had a higher impact in the southern region, and this was statistically significant. Of the 659 children, 30 children died (4.5%), however only 7 of the deaths were directly attributed to COVID-19 infection (1%). Conclusion(s): The study reports the largest nationally representative cohort of children with cancer and COVID-19 to date in India. We identified demographic and clinical factors associated with increased all-cause mortality in patients with cancer. Complete characterization of the cohort has provided further insights into the effects of COVID-19 on cancer outcomes. The low mortality allows us to recommend that specific cancer treatments be continued without delays in therapy.Copyright © 2022
ABSTRACT
This paper emphasises the analysing sentiment of Indian citizens based on Twitter data using machine learning (ML) based approaches. The sentiment of about 1,51,798 tweets extracted from Twitter social networking and analysed based on tweets divided into six different segments, i.e., before lockdown, first lockdown, lockdown 2.0, lockdown 3.0, lockdown 4.0 and after lockdown (Unlock 1.0). Empirical results show that ML-based approach is efficient for sentiment analysis (SA) and producing better results, out of 10 ML-based models developed using N-Gram (N = 1,2,3,1-2,1-3) features for SA, linear regression model with term frequency - inverse term frequency (Tf-Idf) and 1-3 Gram features is outperforming with 81.35% of accuracy. Comparative study of the sentiment of the above six periods indicates that negative sentiment of Indians due to COVID-19 is increasing (About 4%) during first lockdown by 4.0% and then decreasing during lockdown 2.0 (34.10%) and 3.0 (34.12%) by 2% and suddenly increased again by 4% (36%) during 4.0 and finally reached to its highest value of 38.57% during unlock 1.0.
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Background: The Internet of Medical Things (IoMT) is now being connected to medical equipment to make patients more comfortable, offer better and more affordable health care options, and make it easier for people to get good care in the comfort of their own homes. Objective(s): The primary purpose of this study is to highlight the architecture and use of IoMT (Internet of Medical Things) technology in the healthcare system. Method(s): Several sources were used to acquire the material, including review articles published in various journals that had keywords such as, Internet of Medical Things, Wireless Fidelity, Remote Healthcare Monitoring (RHM), Point-of-care testing (POCT), and Sensors. Result(s): IoMT has succeeded in lowering both the cost of digital healthcare systems and the amount of energy they use. Sensors are used to measure a wide range of things, from physiological to emotional responses. They can be used to predict illness before it happens. Conclusion(s): The term "Internet of Medical Things" refers to the broad adoption of healthcare solutions that may be provided in the home. Making such systems intelligent and efficient for timely prediction of important illnesses has the potential to save millions of lives while decreasing the burden on conventional healthcare institutions, such as hospitals. patients and physicians may now access real-time data due to advancements in IoM. Copyright © 2022 Wal et al.
ABSTRACT
Background: Covid-19 infection time and again has been causing major morbidities and mortalities. Increased vulnerability of Covid-19 recovered patients was seen towards mucormycosis infection. Mucormycosisis is an aggressive, angioinvasive fungal disease caued by fungi of order Mucorales. This increase in cases may be attributed to a weakened immune system, pre-existing comorbidities such as diabetes, overzealous use of steroids. We conducted a study on 25 cases admitted in mucor ward in a tertiary care setting to highlight this association and focusing on possible causes so that we can be prepared to handle any such catastrophe in future in a better way. Methods and Results: We did a retrospective study on 25 cases admitted in a tertiary care center catering to large population of Covid -19 patients with varying severity.Covid-19 associated mucormycosis(CAM) was found to be more common in males(76%).Diabetes mellitus was the most common underlying condition(72%).68% patients had received steroids and antibiotics, 28% patients had history of receiving Oxygen. In CAM predominant presentation was rhino-orbital mucormycosis. Unilateral orbit involvement was seen in (88%) cases. Conclusion(s): As severe acute respiratory syndrome coronavirus-2 is highly susceptible to mutations and is causingseries of waves, its association with opportunistic fungal infection is a serious concern. Incidences of mucormycosis were increased in Covid-19 patients due to immune modulation and coexistence of immunosuppressive conditions such as diabetes. Concurrent glucocorticoid therapy further heightens the risk. Early diagnosis and prompt intervention can help improve outcome. Copyright © 2023 Ubiquity Press. All rights reserved.