Cutaneous Adverse Reactions after Administration of Covishield Vaccine in a Tertiary Care Hospital in Western Uttar Pradesh: A Series of Cases

COVID-19 is a recently discovered highly communicable disease caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and its variants. The sudden emergence of the COVID 19 pandemic and its impact on global health meant that the development of effective and safe vaccines was crucial for this new lethal disease as vaccination always plays an essential role in the advancement of global health. So far, there are three main types of COVID-19 vaccines in use around the world: mRNA-based vaccines, adenoviral vector vaccines, and inactivated whole-virus vaccines. Since the introduction of vaccines for the COVID-19 disease, various reports of a spectrum of mucocutaneous side effects have surfaced. With the aid of this case series we would like to highlight the different types of cutaneous adverse effects that were observed post vaccination with the COVISHIELDTM vaccine by the department of Dermatology at our institution. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Predicting the Disease Severity and Mortality in COVID-19 Patients based on Disease Characteristics: An Observational Study in Indian Setting

Introduction: The emergence of newer mutated variants of COVID-19 virus has posed a significant challenge. The present study is aimed at investigating the clinical characteristics of COVID-19 and the parameters that may serve as predictors of severity and mortality related to COVID-19 in an Indian setting. Method(s): The observation study was carried-out by using the data of COVID-19 patients admitted between July 2020 to June 2021 at JLN Medical College, Ajmer, Rajasthan, India. The demographic and clinical data of clinically significant parameters were collected. The statistical difference between recovery and death and between patients who required long-term oxygen and those who did not was evaluated for various demographic and clinical variables. Chi-square and Fisher exact test were performed for categorical variables and t-test for continuous variables. Regression analyses were also carried-out for different variables with respect to survival and death, and for oxygen dependency. Result(s): Variables namely age, duration of hospital stay, overweight, breathlessness, O2 mask therapy, BiPAP support, and ventilator usage were found to be significantly different between recovered and expired subjects (P 0.00). The study has noted hypertension (25.06%) and diabetes (23.73%) as the common comorbidities noted in COVID patients, followed by coronary artery disease (2.98%) and asthma. The study has validated the role of oxygen saturation and requirement of oxygen in predicting mortality among COVID-19 patients. The study identified age as a significant predictor of mortality, obesity as a risk factor in COVID-19 patients, gender as a factor influencing the requirement of oxygen, and fever as an independent factor related to oxygen therapy. Bilevel positive airway pressure was given to majority of expired patients (83%) compared to 10% in recovered patients. Conclusion(s): Variables namely age, BMI, duration of hospital stay, breathlessness, O2 mask therapy, BiPAP support, and ventilator usage could be predictive in COVID-19 severity and mortality. The variables to be considered for predicting oxygen dependency are age, urban/rural, gender, duration of hospital stay, weight, height, BMI, fever, cough, breathlessness, diabetes, hypertension, and CAD. Copyright © 2022, Indian Academy of Clinical Medicine. All rights reserved.

Offsetting Present Risks, Preempting Future Harms, and the Ethics of a ‘New Normal’

The ongoing pandemic has led some people to speak about a ‘new normal’, since we have temporarily had to radically change how we live our lives to protect ourselves and others from the spread of the SARS-CoV-2 virus. That expression – ‘a new normal’ – has been also be used in other contexts, such as in relation to societal disruptions brought about by things like new technologies or climate change. What this general idea of a ‘new normal’ means is unclear and hard to characterise, and there are diverging views about how to respond to a new normal, but one feature of a desirable new normal that most people would agree on is that it should be ‘safer’: safer technologies, safer institutions, and so on. But it is also important to consider what other ethical considerations and principles should be part of an ethics of a new normal. And it is also interesting to explore similarities and differences among different types of cases that can be classified as situations where we face a new normal. In this chapter, we will discuss the general idea of an ethics of a new normal, and consider what ethical distinctions, values, and principles are likely to be relevant in most instances where we face a new normal, including ethical considerations related to risk mitigation and ways of offsetting potential harms. © 2022, The Author(s).

Dynamic Analytics and Forecasting Model for Covid-19 Using Machine Learning Algorithms

In our day to day life, the availability of correctly labelled data as well as handling of categorical data are mostly acknowledged as two main challenges in dynamic analysis. Therefore, clustering techniques are applied on unlabelled data to group them in accordance with the homogeneity. There are many prediction methods that are being popularly used in handling forecasting problems in real time environment. The outbreak of coronavirus disease (COVID19)-2019 creates the need for a medical emergency of worldwide concern with a rapidly high danger of open out and strike the entire world. Recently, the ML prediction models were used in many real time applications which necessitate the identification and categorization for real time environment. In medical field Prediction models are vital role to obtain observations of spread and significances of infectious diseases. Machine learning related forecasting mechanisms have showed their importance to develop the decision making on the upcoming course of actions. The K-means algorithm and hierarchy were applied directly on the renewed dataset using R programming language to create the covid patient cluster. Confirmed Covid patients count are passed to Prophet package, then the prophet model has been created. This forecasts model predicts the future covid count, which is essential for the clinical and healthcare leaders to make the appropriate measures in advance. The results of the experiments indicate that the quality of Hierarchical clustering outperforms than the K-Means clustering algorithm in the structured structured dataset. Thus, the prediction model also used to support model predictions help for the officials to take timely actions and make decisions to contain the COVID-19 dilemma. This work concludes Hierarchical clustering algorithm is the best model for clustering the covid data set obtained from world health organization (WHO).