Embodying transnational queer Black and Brown utopia in alternative QTPOC nightlife spaces

This focus explores queer Black and Brown feminist and utopian politics as imagined in modern-day alternative nightlife spaces. This is done through case studies of the QTPOC (Queer and Trans People of Colour) nightlife spaces of Queertopia by the Other Village People in Johannesburg, Misery Party and Pxssy Palace in London, and Papi Juice and BK Boihood in New York. These cities are particularly lifted up as spaces of Black and Brown resistance to white dominance and racial capital, even within LGBTQIA+ spaces that implicitly or explicitly do not cater to Black and Brown queers. Through these examinations, it is argued that queer feminists of colour are embodying queer utopia through parties that centre healing, mental health, ancestral faith practices, queer Black and Brown music and dance traditions, and spaces for activists and cultural workers to gather beyond mainstream bars and nightlife. By linking these practices to transnational resistance to racial capitalism and cisheterophobia, and by particularly catering to queer people of colour involved in social movement, resistance, and cultural organising work, these parties exist as experiments in Black and Brown transnational feminist practice. This article examines the bonds that organisers and attendees of these parties build with each other across borders, both in physical nightlife spaces as well as in digital spaces conducted during COVID-19 lockdowns that explicitly brought queer people of colour together to dance and dream transnationally. It ultimately argues that these nightlife spaces are practices of imagining the possibility of utopias where queer people of colour thrive beyond borders.

Multifaceted COVID-19 Associated Coagulopathy: A Case Series

The pathophysiology behind Coronavirus Disease-2019 (COVID-19) has remained blur even after more than two years of onset of the pandemic. Apart from pulmonary parenchymal involvement, widespread vascular thrombosis affecting both pulmonary and extra-pulmonary systems has also been seen to contribute to COVID-19 associated morbidity. This vascular manifestation often remains undiagnosed due to non specific and varied symptoms that range from asymptomatic detection to life threatening presentations. A series of six COVID-19 positive (three male and three female) cases who presented with thrombosis of pulmonary, coronary and cerebral vessels despite being on thromboprophylaxis are reported herein. The age of patients ranged from 32 to 80 years. Out of six patients, three had comorbidities. The most common complication was Pulmonary Thromboembolism (PTE, n=3) followed by Brain infarct (n=2) and Myocardial Infarction (MI, n=1). Out of three patients with PTE, one patient had concurrent Deep Vein Thrombosis (DVT). All patients were managed as per guidelines issued by the Ministry of Health and Family Welfare for severe COVID-19 disease. Out of six patients, three patients died and three were discharged. The series highlights the need for high index of suspicion on the part of the treating physician that could aid in early detection and successful management of this potentially fatal condition.Copyright © 2023 Journal of Clinical and Diagnostic Research. All rights reserved.

Embodying transnational queer Black and Brown utopia in alternative QTPOC nightlife spaces

This focus explores queer Black and Brown feminist and utopian politics as imagined in modern-day alternative nightlife spaces. This is done through case studies of the QTPOC (Queer and Trans People of Colour) nightlife spaces of Queertopia by the Other Village People in Johannesburg, Misery Party and Pxssy Palace in London, and Papi Juice and BK Boihood in New York. These cities are particularly lifted up as spaces of Black and Brown resistance to white dominance and racial capital, even within LGBTQIA+ spaces that implicitly or explicitly do not cater to Black and Brown queers. Through these examinations, it is argued that queer feminists of colour are embodying queer utopia through parties that centre healing, mental health, ancestral faith practices, queer Black and Brown music and dance traditions, and spaces for activists and cultural workers to gather beyond mainstream bars and nightlife. By linking these practices to transnational resistance to racial capitalism and cisheterophobia, and by particularly catering to queer people of colour involved in social movement, resistance, and cultural organising work, these parties exist as experiments in Black and Brown transnational feminist practice. This article examines the bonds that organisers and attendees of these parties build with each other across borders, both in physical nightlife spaces as well as in digital spaces conducted during COVID-19 lockdowns that explicitly brought queer people of colour together to dance and dream transnationally. It ultimately argues that these nightlife spaces are practices of imagining the possibility of utopias where queer people of colour thrive beyond borders. © 2023 M. Bhardwaj.

Awareness, Perceived Risk, and Protective Behavior Towards Covid-19 Among Undergraduate Students of Delhi and NCR, India

Preventive measures are the best cure for any disease as they reduce the infection rate. Preventive measures are affected by knowledge, attitudes, and practices towards the disease. Therefore, this study aimed to assess the awareness, perceived risk, and protective behavior toward COVID-19 among undergraduate students of the Delhi and National Capital Region, India. An online questionnaire-based random survey was conducted amongst 605 undergraduate students to assess the demographic characteristics of participants, their level of awareness, perceived risk, and protective behavior regarding COVID-19. The overall awareness, perceived risk, and protective behavior for COVID-19 were found high in undergraduate students (0.000***,0.000***,0.000***). When variable (Gender, area of living, and subject studies) based analysis was performed among participants, a non-statistical significance difference was observed in total awareness among them (p>0.05) towards COVID-19 (p=0.996, 0.121, 0.937). Whereas Female, urban, and science participants were found to perceive the risk for COVID-19 more accurately in comparison to male, rural and non-science participants in total perceive risk analysis (p= 0.016**, 0.035**, 0.036**). However, urban participants showed more Total protective behavior as compared to the rural participants (p=0.048**) and there was no statistical significance difference in protective behavior in terms of Male/Female and Science/ non-science participants (p=0.189, 0.100). These findings will contribute to the continued regional/ global efforts to better understand preventive crisis response to the COVID-19 pandemic. This study emphasizes the need for conducting periodic webinars for educational intervention for all college students which could be useful to create more awareness.

Perceptions and Experience of Medical Students Regarding E-learning during COVID-19 Lockdown- A Cross-sectional Study

Introduction: Medical education has been adversely affected during COVID-19 pandemic. Imparting medical education through e-platforms exclusively was a novel experience both for students and teachers. Even though online classes have been ongoing since almost a year and half, not much data on perception and experience about e-learning among medical students is available from India. Aim: To evaluate perceptions, experiences and challenges faced by medical students regarding e-learning during lockdown period owing to COVID-19 along with their future preferences. Materials and Methods: The present study was a questionnaire based cross-sectional survey regarding use of e-learning during COVID-19 pandemic among 340 MBBS (Bachelor of Medicine and Bachelor of Surgery) and Bachelor of Science Nursing (BSc-NUR) students. It was conducted in the Department of Pulmonary Medicine at Shri Lal Bahadur Shastri Medical College and Hospital Mandi, Himachal Pradesh, India from May-July 2021. Students perceptions’ of e-learning were assessed using the validated Technology Acceptance Model (TAM) model and responses were measured on 5-point Likert scale. Quantitative data was expressed by mean and standard deviation and significant level of differences between means were tested by Student’s t-test (unpaired). Proportions were compared by Chi-square test or Fisher’s-exact test. Results: Out of 400 students, 340 responded of which 225 were females. Of the total 340 students, 97.9% (n=333) respondents, had an idea of e-learning and more than half (n=188;55.3%) had used any type of e-learning platform prior to onset of COVID-19. More number of MBBS students had used e-learning than BSc-NUR students (55% v/s 41%;p=0.033). Cell phone was the most common device (n=324;95.3%) used. The quick sharing of material (n=258;76%) and flexibility (n=233;68.5%) were top rated benefits of e-learning. The key disadvantages were suboptimal practical training (n=222;65.3%) and lack of face-to-face interactions (n=146;43%). Majority of students voted for traditional learning (n=156;45.9%) closely followed by blended learning (n=140;41.2%). Conclusion: The students had an overall positive attitude towards e-learning and wanted to continue e-learning alongside traditional teaching i.e., blended learning. Exploration of merits and barriers to e-learning during pandemic can act as a guide to implement blended learning in medical curriculum for enhanced teaching/learning experience.

Exploring Critical Events of Perishable Product Supply Chain Using Fault Tree Methodology to Deal with Impacts of COVID-19

This research investigates the impact of pandemic COVID-19, on the perishable product supply chain (PPSC). Thematic analysis for the cause of failure in PPSC has been identified through the NVivo application. It examines the events that cause disruption. Secondly, fault tree methodology has adopted qualitative evaluation using the minimum cut set analysis and importance measures. A case study of the apple supply chain in Shimla, India has been included, collecting data from respondents, research papers, government reports, and newspaper articles published from the period March 2020 to December 2020. The occurrence of failure in the apple supply chain included crop yield loss, unavailability, and inaccessibility of apple products. After analysis, 13 minimum cut sets are obtained. These include critical failure event as: assistance in failure from government and organization, high food prices, labour shortage, and cross border restriction. Potential strategies for resilient PPSC have been proposed for an efficient decision-making process. © 2021 IEEE.

Deep Reinforcement Learning Algorithms for Multi-Agent Systems-A Solution for Modeling Epidemics

Multi-agent reinforcement learning (MARL) consists of large number of artificial intelligence-based agents interacting with each other in the same environment, often collaborating towards a common end goal. In single-agent reinforcement learning system the change in the environment is only due to the actions of a particular agent. In contrast, a multi-agent environment is subject to the actions of all the agents involved. Multiagent systems can be deployed in various applications like stock trading to maximize profits in stock market, control and coordination of a swarm of robots, modeling of epidemics, autonomous vehicle and traffic control, smart grids and self-healing networks. It is not possible to solve these complex tasks with a pre-programmed single agent. Instead, the many agents should be trained to automatically search for a solution through reinforcement learning (RL) based algorithms. In general, arriving at a decision in a multi-agent system is almost close to impossible due to exponential increase of problem size with an increase in the number of agents. In this paper, multi-agent systems using Deep Reinforcement Learning (DRL) is explored with a possible application in modeling of epidemics. Different stochastic environments are considered, and various multi-agent policies are implemented using DRL. The performance of various MARL algorithms was evaluated against single agent RL algorithms under different environments. MARL agents were able to learn much faster compared to single RL agents with a more stable training phase. Mean Field Q-Learning was able to scale and perform much better even in the situation of hundreds of agents in the environment and is a sure candidate to model and predict the epidemics, in the existing frightening dangerous situation of corona pandemic. © 2021 IEEE.

No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection

The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English. In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with additional relevant features extracted from Twitter to identify fake tweets. To expand our approach to multiple Indic languages, we resort to mBERT based model which is fine tuned over created dataset in Hindi and Bengali. We also propose a zero shot learning approach to alleviate the data scarcity issue for such low resource languages. Through rigorous experiments, we show that our approach reaches around 89% F-Score in fake tweet detection which supercedes the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our zero shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali Tweets without any annotated data, which clearly indicates the efficacy of our approach. © 2021 IEEE.

Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts

Fake news, hostility, defamation are some of the biggest problems faced in social media. We present the findings of the shared tasks (https://constraint-shared-task-2021.github.io/ ) conducted at the CONSTRAINT Workshop at AAAI 2021. The shared tasks are ‘COVID19 Fake News Detection in English’ and ‘Hostile Post Detection in Hindi’. The tasks attracted 166 and 44 team submissions respectively. The most successful models were BERT or its variations. © 2021, Springer Nature Switzerland AG.

Pathophysiology of sars-cov-2 (Covid 19) viral infection

The city of Wuhan located in Hubei province of central China was burdened with a series of cases presenting with atypical acute respiratory infections in December 2019. Little did people know at that point in time, that a novel virus known as SARS-CoV-2 (COVID-19) or simply corona virus, was responsible for these peculiar presentations. COVID-19 had begun spreading at an alarm-ing rate worldwide, eventually gaining official status as a global pandemic, as affirmed by the World Health Organisation (WHO) on 11 March 2020. By 6 July 2020, globally, there were 1.5 million cases and around 536 893 deaths. As the pandemic took its toll globally, scientists struggled to classify and specify the manifestations of the virus. Medical practitioners, microbiologists and scientists worldwide gradually joined forces to define COVID-19 as an infection characterised by an immense inflammatory reaction or cytokine storm which may cause acute respiratory distress syndrome (ARDS) and multi-organ dys-function (MODS). During the latter half of 2020, multiple hospitals in India, France, America, Germany and Netherlands reported an increasing incidence of fatal invasive fungal infections in recovered SARS-CoV-2 patients. Increased severity of infections as well as mortality was observed in immunocompro-mised patients and those with co existing medical illnesses such as diabetes and hypertension. Furthermore, even though many patients recovered from SARS-CoV-2 infection, it was noted that their immunity post recovery was sig-nificantly diminished, and it was during this period they were more suscep-tible to fatal bacterial and fungal co-infections. This review article explores the pathophysiology of COVID 19 infection and difference in response to the infection in adult and paediatric populations.

Evaluation of Gamma glutamyl-transferase (GGT) levels in COVID-19: A retrospective analysis in tertiary care centre

Many recent studies have reported that patients infected with novel coronavirus 2019 or SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) might have a liver injury. However, few studies have focussed on the levels of Gamma glutamyl-transferase (GGT) alone and the variations associated with it. We retrospectively analysed the GGT levels of 476 admitted patients with confirmed COVID-19 in a tertiary care centre, PGIMER (Post Graduate Institute of Medical Education and Research), Chandigarh. Out of the total 476 COVID-19 patients studied, 35% had elevated GGT levels. ICU care was required for 51.19% (P <0.0001) of these patients and their hospital stay was of longer duration as compared to the patients with normal GGT levels. The incidence of GGT elevation was found to be more pronounced in males and elderly patients. The male population displayed higher GGT levels with 52% having raised levels compared to females where only 21.6% had elevated GGT levels. Although the number of COVID-19 cases was majorly from young age groups, the elevation in GGT levels has been reported more in elderly patients. GGT levels can therefore serve as a predictor for the extent of liver injury and severity in COVID-19 patients.