Ethical and Sustainable Perceptions on Cloud Kitchen Business- A Study of Consumers and Stakeholders during the Covid-19 Pandemic

Cloud kitchen has evolved as a popular means to meet the growing demand of home-delivered food during the pandemic. The purpose of this research is to gauge the perception of consumers as well as stakeholders about the ethical correctness of cloud kitchen models of business and whether the cloud kitchen model is sustainable or not. The manuscript is primary and quantitative in nature. The data was collected during the period of pandemic from the respondents living in Delhi and Kolkata. The research focuses on the Utility, Rights, Fairness and Care constructs of ethics to gauge the perception of respondents about cloud kitchen business including its sustainability and challenges. The analysis was done running ANOVA and binomial distribution. The consumers as well as stakeholders and employees appear to be positively responding on the question of the cloud kitchen model being ethically correct on the 4 constructs and they seem to agree that Cloud Kitchens are sustainable. Very little existing literature is available on the ethical and sustainability aspects of cloud kitchen model with Utility, Rights, Fairness and Care constructs. Thus, it is the first paper of its kind and is expected to add to the existing body of literature. ©Copyright IJHTS.

A Hand Gesture-Based Contact-less Interface for Electronic Health Records

Clinical data monitoring and storing are essential components of continuous and preventive healthcare systems. Data such as blood pressure, pulse rate, temperature, etc., can be recorded by the hospital staff daily for in-patient subjects. The usual way of noting them down is to check different parameters using various medical instruments and write it on paper with the corresponding patient's details (e.g., name, patient-id, or government identity card number). However, after the outbreak of COVID-19, there is a set of World Health Organization (WHO) guidelines to behave in public places. Ordinary people and professionals feel hesitant to touch any media even if they have some protection such as gloves and sanitizer. In this crisis, there is a natural demand for contact-less activities instead of touch-based traditional ways. Gesture-based activities might be one of the low-cost alternatives to some sensor-based systems. This paper uses a profound learning-based finger point gesture to capture writing in the air and realize it on the screen through a predictive model. Here, the proposed framework has been demonstrated as a proof of concept to record blood pressure data for multiple patients without touching any electronic screen or paper. The proposed architecture is developed based on the gesture recognition and metric learning, which have been used to recognize different digits trained from the MNIST digit dataset. The mean test accuracy is reached 99.47% on the same dataset. © 2022 IEEE.

Mathematical Morphology Adopted Automatic Detection of Ground Glass Opacities in Lung CT Images of COVID-19 Patients

The severity of the nCOVID infection relies on the presence of Ground Glass Opacities (GGO) present in the patient's chest CT scan images. Although, detecting and delineating the precise boundaries of GGO in the chest CT images is challenging. Here, we proposed a fast and novel technique to automatically segment the regions containing GGO in lung CT images using mathematical morphology. We have tested our algorithm on the chest CT images of 145 Covid-positive cases. This unique segmentation approach correctly segments the lung field from chest CT images and identifies GGO with average sensitivity, specificity, and accuracy of 96.89%, 95.23%, and 97.22%, respectively. We used expert radiologists' hand-curated segmentation of GGO as ground truth for quantificational performance analysis. Our research results indicate that this algorithm performs well found in the literature. © 2023 IEEE.

Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot

The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. © 2023 IEEE.

Unpacking systemic, cascading, and compound risks: A case based analysis of Asia Pacific

The Sendai Framework for Disaster Risk Reduction aims to reduce disaster risk and loss by prioritizing activities that promote a better understanding of disaster risk. It prioritizes activities such as understanding disaster risk and its dimensions, with a focus on preventing the creation of new risks, reducing existing ones, and preparing for residual risks. The concept of systemic, cascading, and compound risks is becoming increasingly important in disaster risk management. However, there is a lack of understanding about these terms and how they overlap and differ in real-world applications. The COVID-19 pandemic has highlighted the evolving and underlying risk patterns in our interconnected society, making it crucial to bridge this gap. The paper explores the existing literature on systemic, cascading, and compound risks, using a secondary literature review and content analysis. It provides a conceptual overview of the three risks and supports the review with an analysis of 40 case studies in the Asia Pacific region. The analysis focuses on the hazards, underlying vulnerabilities, impacted systems, and the complex interconnections between them. Based on the findings, the authors provide recommendations for the management of systemic, cascading, and compound risks in the future. © 2023

Fungal infection: An unrecognized threat

The fungi are eukaryotes and of great interest to microbiologist. Fungi are heterotrophic organism that require organic compounds for nutrition. According to Hawksworth, only around 100 fungi cause diseases in humans and animals out of around 1.5 million existing in the universe. Fungal pathogenic infection may cause allergies, superficial infection, as well as invasive mycosis in severe cases. Public health can be significantly affected by zoonotic fungi that transmit naturally between animals and humans. Prevention of fungal infection arising out of zoonotes has received insufficient attention as it lacks mass awareness. A number of different fungal infections, their signs and symptoms, preventive measures, and treatment protocol are demonstrated in this chapter. Regarding the treatment of various fungal infections, azoles, fluoropyrimidines, polyenes, and echinocandins are the only four molecular classes of drugs available as on date to target fungal metabolic pathways despite years of drug discovery research. Few other promising molecules like morpholines and allylamines are useful antifungal but with poor efficacy and severe side effects when administered systematically. Development of resistance against most common antifungal drugs further aggravates the situation. Fungal infection like mucormycosis is observed in some parts of the world after a patient gets infected with COVID-19 as there is impairment in the immunity system. There is an urgent need to control this fungal infection as it poses serious threat silently. We can limit the spread of fungal infection by protecting susceptible population from being exposed. More efforts are needed from a global health perspective to aware the people regarding neglected fungal infection and its problem so that socioeconomic consequences and mortality can be better explained. An integrated platform of prevention and control strategies for the spread of fungal infection is the need of the hour. © 2023 Elsevier Inc. All rights reserved.

Machine Learning Approaches to Assess Mood of the News Editorial

Social media and news channels have always been vital sources for spreading information and raising awareness about recent occurrences. As reported by a survey, 82 percent of respondents from India stated that they sourced their news online, which included social media, as of the year 2021, making it a popular form of accessing news1. For a long time, information about COVID - 19 has been one of the most popular topics. News channel networks and editorials were one of the first places where knowledge regarding COVID - 19 was widely disseminated. In this study, sentiment analysis models have been developed to categorize tweets by some of India's most well-known news stations into positive and negative during the COVID - 19 virus was new in India from June 2020 to July 2020. We attempted to do so by developing nine various models based on different datasets and classification algorithms to investigate the news channels' tweets more thoroughly. According to our findings, the model that provided us with the highest accuracy and performance has been trained using the NLTK Dataset and the Logistic Regression Classifier. © 2022 IEEE.

A Siamese Neural Network-Based Face Recognition from Masked Faces

In modern days, face recognition is a critical aspect of security and surveillance. Face recognition techniques are widely used for mobile devices and public surveillance. Occlusion is a challenge while designing face recognition applications. In the COVID19 pandemic, we are advised to wear a face mask in public places. It helps us prevent the droplets from entering our body from a potential COVID19 positive person’s nose or mouth. However, it brings difficulty for the security personnel to identify the human face by seeing the partially exposed face. Most of the existing models are built based on the entire human face. It could either fail or perform poorly in the scenario as mentioned above. In this paper, a solution has been proposed by leveraging Siamese neural network for human face recognition from the partial human face. The prototype has been developed on the celebrity faces and validated with the state-of-the-art VGGFace2 (Resnet50) model. Our proposed model has performed well and provides very competitive results of 93% and 84.80 ± 4.71 % best-of-five and mean accuracy for partial face-images, respectively. © 2022, Springer Nature Switzerland AG.

Social Innovation Hackathon for Driving Innovation in Disaster Risk Reduction (DRR)

Time and again, disasters bring forth various challenges concerning risk communication, disaster-resilient infrastructure, last-mile delivery, disaster reporting, etc. These challenges often highlight the existing gap between research and academicians, and the policymakers and practitioners. Secondly, it brings forth the lack of adequate collaboration among experts and practitioners of different fields. Most of these challenges require innovative and low-cost solutions catering to local and contextualized problems, and calls for a multi- and transdisciplinary approach and collaboration. With this vision, amidst the current pandemic of COVID-19, Resilience Innovation Knowledge Academy (RIKA) India, Indo-Japan Laboratory (Keio University, Japan) and four partnering universities have launched the Social Innovation Online Hackathon (SIOH) 2020. SIOH aims to provide a unique virtual platform to student innovators and mission-driven entrepreneurs from different fields like architecture, engineering, disaster management, etc., to collaborate and develop innovative solutions for tackling the pandemic and future disasters. The paper aims to introduce SIOH and its four-step process as a tool of multi-disciplinary collaboration to promote innovation for Disaster Risk Reduction (DRR). Besides the critical outcomes of the SIOH, the paper seeks to flag some indirect positive impacts of such an exercise. Among others, these include, firstly, the introduction of the field of DRR to academicians and practitioners of other sectors, thereby paving the way for its mainstreaming in other sectors. Secondly, such an exercise involving young students envisages to invoke a spirit of inquiry and innovation, which is crucial for bringing social change. Thirdly, it highlights the critical role of proper sectoral mentorship in handholding the young innovators in their journey of building resilient societies. © 2021 IDRiM Society.

Antibody response against sars-cov-2 following chadox1(astezeneca azd 1222) vaccine in health care workers in a tertiary care hospital in india

BACKGROUND: The ChAdOx1 nCoV-19 vaccine against COVID-19 is a two-dose vaccine spread 3 to 4 weeks apart. This study aims to ascertain the antibody response to each dose with respect to age, previous infection status etc. METHODS: Baseline total COVID-19 antibody level was ascertained using Siemens SARS-CoV-2 Total Antibody assay in consenting health care workers before the first dose of vaccination. Adverse effects were noted in each individual and were monitored weekly for the total antibody titre following both doses. Descriptive statistical tests were used to analyse the changes in antibodies levels weekly after both doses. Association of previous COVID infection and age with antibody levels was assessed. RESULTS: Median (range) of age of the 30 study participants was 31.5 years.23% of the participants had a history of previous COVID-19 infection. Mild adverse events following immunisation were reported by 87% participants after first dose whereas only in 7% after second dose. Median baseline antibody titres were significantly higher among those with previous COVID infection as compared to previously uninfected individuals. Antibody titres increased consistently after first dose and showed a declining trend following the second dose in all participants and showed no significant association with previous COVID-19 infection or age. CONCLUSIONS: Antibody titre response was similar amongst the various age groups. Higher response in the previously infected individuals following first dose may make them ideal candidates for a single dose vaccine regimen. Individuals showing lower levels of neutralising antibodies can be ideal candidates for a booster dose. © Medicina Moderna 2021.

EXTRACORPOREAL BLOOD PURIFICATION IN COVID-19 WITH SERAPH 100 MICROAFFINITY BLOOD FILTER: A SINGLE-CENTER CASE SERIES

TOPIC: Critical Care TYPE: Fellow Case Reports INTRODUCTION: The SARS-Cov-2 (COVID-19) pandemic has had a significant impact on healthcare and the economy worldwide. In a subset of patients, COVID-19 causes a cytokine-mediated systemic hyperinflammatory response, often resulting in acute respiratory distress syndrome (ARDS) as well as multi-organ dysfunctions. Due to the hyperinflammatory response, the Seraph 100 Microbind Affinity Blood Filter has obtained Emergency Use Authorization (EUA) from the Food and Drug Administration (FDA). The Seraph 100 filter is an extracoporeal hemoperfusion device designed to remove pathogens directly from the blood via heparin-coated polyethylene beads. It is conjectured and shown in vitro to assist in the treatment of COVID-19 organ dysfunction by reducing the burden of viremia as well as circulating cytokines within the bloodstream. CASE PRESENTATION: In this case series, we present four patients admitted with severe COVID-19 who were treated with the Seraph 100 filter under the EUA. Variables assessed included the mean arterial pressure, heart rate, oxygen saturation, temperature, lactate, and pH along with vasopressor requirements before, during, and after Seraph filter use. At the time of treatment initiation with the Seraph 100 filter, all four patients met criteria for septic shock and three of the four patients had bacteremia with Staphylococcus aureus. Two of our patients (survivors) had a significant reduction in vasopressor requirement in the first few hours of treatment despite a minimal change in patients' acid-base status. The survivors also demonstrated improvements in their oxygen saturation and oxygen requirements and were ultimately discharged from the hospital. The remaining two patients (non-survivors) did not have a change in vasopressor requirement or oxygenation after one treatment with the Seraph 100 filter. One of these patient's treatments was terminated prematurely due to clinical decline and reconsideration of the patient's goals of care with family. Both of these patients passed away. DISCUSSION: We present four cases with various outcomes of critical illness related to COVID-19 treated with the novel Seraph 100 filter. Two of the four patients treated with the Seraph 100 had significant and dramatic clinical improvement upon initiation of treatment and were weaned off vasopressor support within 48 hours. Unfortunately, the other two patients showed no clinical improvement and subsequently declined resulting in death during hospitalization. The two survivors had a shorter duration of vasopressor-dependent shock prior to treatment with the Seraph 100 than the non-survivors. CONCLUSIONS: In conclusion, the Seraph 100 may improve hemodynamics in patients with COVID-19 and secondary infections. Future studies with a larger cohort will help select appropriate patients as well as determine optimal timing for initiation of therapy. REFERENCE #1: Seffer, MT, Cottam, D, Forni LG, Kielstein, JT. Heparin 2.0: A New Approach to the Infection Crisis. Blood Purification. 2021;50(1):28-34. REFERENCE #2: "ExThera Medical: MedTech Company Developing Blood Filters That Can Capture a Wide Range of Pathogens." ExThera Medical V, www.extheramedical.com/. REFERENCE #3: Ronco, C, et al. Extracorporeal Blood Purification and Organ Support in the Critically Ill Patient during COVID-19 Pandemic: Expert Review and Recommendations. Blood Purification. 2021;50:17-27. DISCLOSURES: No relevant relationships by Rohini Chatterjee, source=Web Response No relevant relationships by Mateo Houle, source=Web Response No relevant relationships by John Hunninghake, source=Web Response No relevant relationships by Arjun Kalra, source=Web Response No relevant relationships by Ian McInnis, source=Web Response No relevant relationships by Mai Nguyen, source=Web Response No relevant relationships by Nicholas Niazi, source=Web Response No relevant relationships by Melissa Rosas, source=Web Response No relevant relationships by Lauren Sattler, source=Web Response No relevant relationships by Michal Sobieszczyk, source=Web Response No relevant relationships by Brandon Walker, source=Web Response No relevant relationships by Robert Walter, source=Web Response

Shelter management during pandemics: lessons from cascading risks of cyclones and COVID-19

Purpose: The concept of multi-purpose cyclone shelters has been found effective in saving various lives during past cyclones. The recent cyclone Amphan, which hit the Indian states of Odisha and West Bengal in the middle of pandemic COVID-19 has posed severe issues related to cyclone shelter management in the rural areas. The purpose of this paper is to investigate the case of Odisha in a pandemic and draw some key lessons of cyclone shelter management, which can be useful for future cascading risks in other parts of the country and the region. Design/methodology/approach: Cyclone shelters are critical infrastructures in the management of cyclones, associated hazards and saving crucial lives. The effective management of shelters during emergencies is dependent on the existing institutional mechanism, local stakeholders and their understanding of the key functions of the emergency shelters. This paper reviews the key challenges through literature, reports and direct interviews of field professionals and practitioners. Findings: In normal times, cyclone shelters are used as schools and their management lies with the local communities and/or elected bodies. Some of the key emerging issues include: the convincing population at risk for evacuation with proper care, existing emergency shelters being repurposed as COVID-19 facilities, need for hygiene and safety material, special arrangement and segregation of population at higher risk of COVID-19 and large destruction of social infrastructures. Originality/value: During cascading disasters, adaptive governance becomes important. With the study of cyclones during the pandemic period, the paper draws key decision-making and governance points of cyclone shelter management. This case analysis can be useful to other similar situations during the prolonged pandemic time. © 2020, Emerald Publishing Limited.