Evaluating the Performance of Low-cost PM Sensors over Multiple COALESCE Network Sites

Air quality is a global concern, with particulate matter receiving considerable attention due to its impact on human health and climate change. Recent advances in low-cost sensors allow their deployment in large number to measure spatio-temporal and real-time air quality data. Low-cost sensors need careful evaluation with both regulatory approved methods and other data sets to understand their efficacy. In this work, PM concentrations measured by deploying low-cost sensors at four regional sites are evaluated through comparison with satellite-based model MERRA-2 and the SASS reference instrument. Daily PM2.5 mass concentration variation was analyzed at four regional sites of India from January 2020 to July 2020, including pre-lockdown and six different lockdown periods. Higher PM2.5 concentration was observed at Rohtak (119 mu g m-3) compared to Mahabaleshwar (33 mu g m-3), Bhopal (45 mu g m-3) and Kashmir sites during the pre-lock down period. During the lockdown period, the PM2.5 mass concentration was reduced significantly compared to the pre-lockdown period at every location, although the PM2.5 concentration was different at each location. The air quality trend was quite similar in both the measurements, however, MERRA-2 reconstructed PM2.5 was significantly lower in the pre-lockdown period compared to the lockdown periods. Significant differences were observed between low-cost sensor measurements and MERRA-2 reanalysis data. These are attributed to the MERRA-2 modelling analysis that measures less PM2.5 concentration as compared to ground-based measurements, whereas low-cost sensor are and biases.

Aerosols: Science and engineering

Aerosol science and engineering is a vibrant field of particle technology and chemical reaction engineering. The book presents a timely account of this interdisciplinary topic and its various application areas. It will be of interest to scientists or engineers active in aerosol physics, aerosol or colloid chemistry, atmospheric processes, and chemical, mechanical, environmental and/or materials engineering. • Almost all micro-and nanoparticle processes in chemical processes involve aerosol dynamics or fine-particle dynamics. • Understanding the critical role of aerosols is vital in the dissemination of the Corona virus. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

A Spotlight on Novel Coronavirus (COVID - 19) with Various Types of Strains

Deadly COVID-19 viruses have raised a pandemic situation in the year 2019, causing serious and contagious respiratory infections in humans. SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the main causative agent for this disease outbreak. The pandemic created a critical impact on the global economy. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis that lasted about 11 months. Since, late 2020, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations. This resulted so far, in over 2.7 million deaths and near about 122 million infection cases. Most mutations in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome are either deleterious and swiftly purged or relatively neutral. As far as the concern is the variants it impacts the virus characteristics, including antigenicity and transmissibility in response to the modification of the human immune profile. In recent days, COVID-19 affected cases are rapidly increasing and it became difficult to inhibit this virus as they are continuously mutated in the host cell forming various new strains like B.1.1.7, B.1.351, P.1, P.2, B.1.1.529, etc. These monitoring, surveillance of variation, and sequencing efforts within the SARS-CoV-2 genome enabled the rapid identification of the first some of Variants of Concern (VOCs) in late 2020, where genome changes became the most observable impact on virus biology and disease transmission. In this review article, we tried to focus and spot the light on the genetic diversification of various strains, their nature, similarities and dissimilarities, mechanism of action, and the prophylactic interventions which could prevent this life-threatening disease in the long run.

Usage of ML and IoT in Healthcare Diagnose During Pandemic

The pandemic crisis has obliterated human existence as we know it, as well as regional, social, and commercial action, as well as compelled human civilization in living inside the defined perimeter. Uses of IoT with ML in health care applications is described in this article. The created ML with IoT dependent observation prototype assists for tracing COVID-19 positive detected persons using prior information and isolates them from non-infected individuals. By anticipating as well as analyzing information with AI, proposed ML-IoT system employs parallel computing to track pandemic sickness and also to avoid pandemic disease. The use of machine learning-dependent IoT for COVID in health conditions diagnose likely to be demonstrated the effectiveness for detection and prevention of CORONAVIRUS transmission. It still effects in better way on lowering preventive expenditures also leds to better treatment for infected individuals. In terms of monitoring and tracking, the recommended technique is 95% accurate. The findings will aid for stopping the pandemic's spread and providing assistance to the healthcare sector. © 2022 IEEE.

Knowledge on Marriage and Reproduction in Islam for Multicultural Healthcare and Social Work Needs: Results of the Survey at Five Public Universities in Slovakia

Objective: The aim of our research was to find out, if university students of humanities and social sciences at five Slovak public universities have theoretical prerequisites for intercultural competencies mainly needed in multicultural healthcare. These concrete theoretical prerequisites are dealing with knowledge regarding Islamic teachings on: family;female infanticide;reproduction;usage of assisted reproduction technologies;and induced abortion. design: Research study. Participants: Overall 1000 students at 5 Slovak public universities (at each n=200). methods: Empirical research was done using our own questionnaire. Verification of our three hypotheses has been done using the method of statistical testing for testing hypotheses on equality of parameters of two alternative divisions with large selection ranges. results: Responses to questions concerning definition of marriage in Islam (Questions #1 - 3) have shown, that both male and female students have proved better knowledge of this issue than in the case of the area concerning possibilities of use of reproductive medicine achievements in Islam. Responses to question (Question #4) regarding Muhammad's attitude to feminine infanticide have shown that men, in comparison to women, have manifested more radical (more numerous) inclination to the answer that Muhammad entrusted fathers with decision on its performance. Responses to questions dealing with possibilities to use reproductive medicine achievements in Islam (Questions #5-7) have shown that female, in comparison to male students, have manifested more radical (more numerous) rejective position. conclusion: Knowledge of marriage and reproductive issues in Islam among students stays at historical level, what causes a problem not to be able to understand and respect contemporary needs of Muslim patients in the frame of an holistic approach in multicultural healthcare and social work in Slovakia. Improvements in current curriculum concerning students' intercultural competencies mainly connected to an understanding of standpoints of Muslim believers concerning their social foundations, health and entire well-being are inevitable.

Association of comorbidities with the COVID-19 severity and hospitalization: A study among the recovered individuals in Bangladesh

Objectives: We aimed at the identification of the association of comorbidities with the COVID-19 severity and hospitalization. Methods: It is a retrospective cross-sectional study to investigate the variation in age, sex, dwelling, comorbidities, and medication with the COVID-19 severity and hospitalization by enrolling 1025 recovered individuals while comparing their time of recovery with or without comorbidities. Results: COVID-19 patients mostly suffered from fever. The predominant underlying medical conditions in them were hypertension (HTN) followed by diabetes mellitus (DM). Patients with cardiovascular disease (CVD) (54.3%) and hepatic disorders (HD) (43.6%) experienced higher severity. The risk of symptomatic cases was higher in aged (odds ratio, OR = 1.04, 95% CI = 1.02-1.06) and comorbid (OR = 1.87, 95% CI = 1.34-2.60) patients. T-test confirmed the differences between the comorbid and non-comorbid patients' recovery duration. The presence of multiple comorbidities increased the time of recovery (15-27 days) and hospitalization (20-40%). Increased symptomatic cases were found for patients having DM+HTN whereas CVD+Asthma patients were found with higher percentage of severity. Besides, DM+CKD (chronic kidney disease) was associated with higher hospitalization rate. Higher odds of severity were found for DM+CVD (OR = 4.42, 95% CI = 1.81-10.78) patients. Hospitalization risk was also increased for them (OR = 5.14, 95% CI = 2.02-13.07). Moreover, if they had HTN along with DM+CVD, they were found with even higher odds (OR = 6.82, 95% CI = 2.37-19.58) for hospitalization. Conclusion: Our study indicates that people who are aged, females, living in urban area and have comorbid conditions are at a higher risk for developing COVID-19 severity. Clinicians and health management authorities should prioritize these high-risk groups to reduce mortality attributed to the disease.

A Heuristic Approach using Block Chain to Fight Novel COVID-19 during an Election

India is a highly densely populated country. India is facing a very serious challenge due to the novel COVID-I9 outbreak. In this situation, the economic state of the people is very unstable. Blockchain technology proves helpful to subdue the situation. A voting system with ballot papers is quite tuff now. It is very costly for the government. The pandemic may also spread very quickly. The system of developing ledger serves the idea of developments of a new e-voting system that is economical, easy to use, and highly secure. This paper proposes ways and solutions to design an e-voting system using blockchain technology. The paper's primary goal is to develop a highly secure e-voting technique using which people can cast their valuable votes from their home, working place - anywhere from the world. This e-voting technique is least costly, highly secure, and able to prevent further spreading of any infectious disease in the near future. In this paper, an e-voting system using blockchain has been designed to overcome all these difficulties mentioned above during traditional voting using ballot. The significance of the proposed work is to design of a highly safe and least costly e- voting system using blockchain. Using this system the people can cast their vote easily, securely and without wasting any time. Casting vote will be now just from few clicks away and from any place with a stable internet connection. © 2021 IEEE.

Skewed urbanisation and the contagion

Alongside the dearth of healthcare infrastructure, unplanned and market-driven urbanisation further challenges the containment of the outbreak of infections like Covid-19, in India. In this context, the prospects of an inclusive urban land use plan are also focused on.

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Background: Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus. Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing. However, because of the present lockdown measures in several countries, the validation of computer vision systems using large-scale datasets is a challenge. Methods: In this paper, a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality (VR) technology. Using VR, we modeled a digital twin (DT) of an existing office space and used it to create a dataset of individuals in different postures, dresses, and locations. To test the proposed solution, we implemented a convolutional neural network (CNN) model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures. Results: We detected the number of persons in both the real and synthetic datasets with more than 90% accuracy, and the actual and measured distances were significantly correlated (r=0.99). Finally, we used intermittent-layer- and heatmap-based data visualization techniques to explain the failure modes of a CNN. Conclusions: A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals. The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed. © 2021 Beijing Zhongke Journal Publishing Co. Ltd

Validating social distancing through deep learning and VR-based digital twins

The Covid-19 pandemic resulted in a catastrophic loss to global economies, and social distancing was consistently found to be an effective means to curb the virus’s spread. However, it is only as effective when every individual partakes in it with equal alacrity. Past literature outlined scenarios where computer vision was used to detect people and to enforce social distancing automatically. We have created a Digital Twin (DT) of an existing laboratory space for remote monitoring of room occupancy and automatically detecting violation of social distancing. To evaluate the proposed solution, we have implemented a Convolutional Neural Network (CNN) model for detecting people, both in a limited-sized dataset of real humans, and a synthetic dataset of humanoid figures. Our proposed computer vision models are validated for both real and synthetic data in terms of accurately detecting persons, posture, and intermediate distances among people. © 2021 Copyright held by the owner/author(s).

Generating Synthetic Data for Deep Learning using VR Digital Twin

The ongoing Covid-19 pandemic has made it challenging for large scale data collection, in particular for Convolutional Neural Network (CNN)-based computer vision systems. Additionally, there are numerous circumstances where security, privacy, and limitations pertaining to the accessibility of the required equipment make it arduous to validate computer vision systems with real-world datasets. In this paper, we investigated the possibilities of using synthetic datasets, generated from Virtual Environments (VE) for training and validation of CNN models. We present two use cases where the above-mentioned circumstances play a vital role in preparing the datasets and validating the model with large-scale datasets. By developing and leveraging a three-dimensional Digital Twin (DT), we produce large scale datasets for validating social distancing in workspaces;and in the context of semi-autonomous vehicles, we evaluate how a CNN-based object detection model would perform in an Indian road scenario. © 2021 ACM.

Eye gaze controlled adaptive virtual keyboard for users with SSMI

BACKGROUND: Users with Severe Speech and Motor Impairment (SSMI) often use a communication chart through their eye gaze or limited hand movement and care takers interpret their communication intent. There is already significant research conducted to automate this communication through electronic means. Developing electronic user interface and interaction techniques for users with SSMI poses significant challenges as research on their ocular parameters found that such users suffer from Nystagmus and Strabismus limiting number of elements in a computer screen. This paper presents an optimized eye gaze controlled virtual keyboard for English language with an adaptive dwell time feature for users with SSMI. OBJECTIVE: Present an optimized eye gaze controlled English virtual keyboard that follows both static and dynamic adaptation process. The virtual keyboard can automatically adapt to reduce eye gaze movement distance and dwell time for selection and help users with SSMI type better without any intervention of an assistant. METHODS: Before designing the virtual keyboard, we undertook a pilot study to optimize screen region which would be most comfortable for SSMI users to operate. We then proposed an optimized two-level English virtual keyboard layout through Genetic algorithm using static adaptation process;followed by dynamic adaptation process which tracks users' interaction and reduces dwell time based on a Markov model-based algorithm. Further, we integrated the virtual keyboard for a web-based interactive dashboard that visualizes real-time Covid data. RESULTS: Using our proposed virtual keyboard layout for English language, the average task completion time for users with SSMI was 39.44 seconds in adaptive condition and 29.52 seconds in non-adaptive condition. Overall typing speed was 16.9 lpm (letters per minute) for able-bodied users and 6.6 lpm for users with SSMI without using any word completion or prediction features. A case study with an elderly participant with SSMI found a typing speed of 2.70 wpm (words per minute) and 14.88 lpm (letters per minute) after 6 months of practice. CONCLUSIONS: With the proposed layout for English virtual keyboard, the adaptive system increased typing speed statistically significantly for able bodied users than a non-adaptive version while for 6 users with SSMI, task completion time reduced by 8.8% in adaptive version than nonadaptive one. Additionally, the proposed layout was successfully integrated to a web-based interactive visualization dashboard thereby making it accessible for users with SSMI. © 2021-IOS Press. All rights reserved.

Love and emotions at the time of COVID-19

COVID-19 has already swept millions of lives and created the deep black cloud made up of negative emotions. Analysis of origin of negative emotions from previous experiences indicates diversity in thinking, reasoning, self-centeredness and lack of empathy give rise to human vices which are difficult but not impossible to overcome by resurrection of positive emotions like love, empathy, motivation for good deeds and philanthropic activities. Complex interplay of positive and negative emotions orchestrated by intricately associated neuronal circuits, neurotransmitters coupled with endocrinal influence holds responsible for human behavior, considered as the root of human civilization, is currently facing existential crisis during COVID-19 pandemic. Human civilization is experiencing unique psychosocial problems through emerging COVID-19 pandemic. Depression, panic buying, herd behavior, infodemic, immense sufferings of marginalized people, surge of addictive behavior, racism, domestic violence, rape, divorce, financial constraints, stigmatization, all stem from negative emotions and lack of positive vibes amidst the rally of death and sorrows. COVID-19 and surge of negative emotions are the two pandemics which conjointly causing major mental health threat. Cultivations and practice of positive emotions and triumph of optimistic octet over sinister septet are desirable to save Mother Nature and her habitats from the cruel claws of this pandemic.

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study.

OBJECTIVES: COVID-19 pandemic has led to unprecedented increase in rates of stress and burn out among healthcare workers (HCWs). Heart rate variability (HRV) has been shown to be reflective of stress and burnout. The present study evaluated the prevalence of burnout and attempted to develop a HRV based predictive machine learning (ML) model to detect burnout among HCWs during COVID-19 pandemic. METHODS: Mini-Z 1.0 survey was collected from 1615 HCWs, of whom 664, 512 and 439 were frontline, second-line and non-COVID HCWs respectively. Burnout was defined as score ≥3 on Mini-Z-burnout-item. A 12-lead digitized ECG recording was performed and ECG features of HRV were obtained using feature extraction. A ML model comprising demographic and HRV features was developed to detect burnout. RESULTS: Burnout rates were higher among second-line workers 20.5% than frontline 14.9% and non-COVID 13.2% workers. In multivariable analyses, features associated with higher likelihood of burnout were feeling stressed (OR = 6.02), feeling dissatisfied with current job (OR = 5.15), working in a chaotic, hectic environment (OR = 2.09) and feeling that COVID has significantly impacted the mental wellbeing (OR = 6.02). HCWs with burnout had a significantly lower HRV parameters like root mean square of successive RR intervals differences (RMSSD) [p < 0.0001] and standard deviation of the time interval between successive RR intervals (SDNN) [p < 0.001]) as compared to normal subjects. Extra tree classifier was the best performing ML model (sensitivity: 84%) CONCLUSION: In this study of HCWs from India, burnout prevalence was lower than reports from developed nations, and was higher among second-line versus frontline workers. Incorporation of HRV based ML model predicted burnout among HCWs with a good accuracy.

Does sildenafil improve endothelial dysfunction in rheumatoid arthritis -A pilot clinical trial

Background: Rheumatoid arthritis (RA) is independently associated with an increased risk of cardiovascular disease (CVD). One of the early stages of atherosclerosis is endothelial dysfunction, which is increased in RA. Using drugs to target endothelial dysfunction is a promising novel strategy for CVD prevention in RA. Sildenafil has been shown to improve endothelial function in diabetics, who have similar increased CVD risk. Our hypothesis was that sildenafil use may be a novel primary CVD prevention strategy in RA. Objectives: To determine if sildenafil use in RA patients improves endothelial dysfunction (as measured by brachial artery flow-mediated dilation [FMD] and peripheral arterial tone [PAT]), as well as serum inflammatory and atherosclerosis biomarkers. Methods: This NIH-funded study was a phase II, randomized double-blind placebo-controlled crossover efficacy trial of 25 RA patients, with no known history of CVD, but at least one traditional CVD risk factor. Patients were randomized 1:1 to receive either sildenafil or placebo for 3 months, then after a 2-week washout, crossed over to each respective group for an additional 3 months. Vascular studies (FMD and PAT) and serum atherosclerosis biomarkers (e-Selectin, ICAM-1, VCAM-1) were performed at baseline, 3 months pre-and post-washout, and 6 months. Adverse events were collected. Given the cross-over design, analyses included a random effects model for within-subject comparisons of sildenafil versus placebo periods, adjusting for the baseline (FMD or EndoPAT) within that period and a term for treatment order. All tests were 2-sided with α=0.05. Results: A total of 233 subjects were assessed for eligibility, with 25 subjects being randomized after written informed consent. A total of 13 subjects were randomized to placebo first, and 12 to sildenafil first. Baseline characteristics were similar between those randomized to Placebo vs. Sildenafil first. Mean age was 62.0+/-10.9 years;84% were female;and 92% were white. A total of 6 adverse events experienced in 3 subjects occurred. The primary endpoint (increase in %FMD in Sildenafil period vs. Placebo period) was not significant (p=0.19). However, note the study was powered at 80% to detect an effect size of 0.37 for change in %FMD or biomarker with a sample size of 60, not 25. However, sildenafil use was associated with a significant increase (improvement) by 0.200 units of PAT ratio (p=0.003) compared with placebo, adjusted by treatment order and baseline PAT ratio (within the given treatment period). Exploratory linear mixed models comparing e-Selectin, ICAM-1, and VCAM-1 between Sildenafil vs. Placebo periods, adjusted for treatment order and the baseline biomarker level, did not show any significant differences except for ICAM-1 (55.3 units higher in Sildenafil vs. Placebo periods, p=0.011). Conclusion: In this pilot trial of 25 RA subjects, sildenafil use was associated with a significant increase (improvement) in endothelial function as measured by PAT. However, there was no significant difference in FMD. The study is limited due to the small sample size, which was impacted by slow recruitment as well as the COVID-19 pandemic. Future larger studies are required to assess whether other PDE5 inhibitors may improve endothelial dysfunction in RA and other autoimmune disease patients at high risk of CVD.

Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

COVID-19 is an infectious disease that has been declared a global public health emergency by the World Health Organization. Besides claiming over 3 million lives worldwide, COVID-19 led to unprecedented disruption in industrial productivity, trading, and global food supply, resulting in loss of livelihood. Despite initial success in curbing the spread of diseases through a lockdown and rapid vaccine development, human lives are threatened by sudden outbreaks from new strains of the virus. This motivates the conceptualization of effective interdiction rules to inform human mobility in a manner that the damage to lives as well as the economy could be minimized. In this work, we present three interdiction rules that employ machine learning-based network inference on daily infected cases to infer the influence of contagion between neighboring zones. The proposed rules leverage network science concepts such as coloring and clustering to attain time-varying partial or complete travel restrictions. Through extensive simulation experiments, we show that these strategies yield lower infection spread than greedy and random migration-based tie elimination approaches as well as a balance between contagion mitigation and economic gain.

Challenges in predicting the filtration performance of a novel sewn mask: Scale-up from filter holder to mannequin measurements

Novel designs and materials for filtering face-piece respirators (FFRs) have been disseminated in response to shortages during the COVID-19 pandemic. Since filtration efficiency depends on particle diameter and air face velocity, the relevance of material filtration or prototype fit data depends on test conditions. We investigate whether characterizing a material in a filter holder at a range of face velocities enabled precise prediction of the filtration performance of a novel sewn mask design. While larger particles (>500 nm) are more relevant for inhalation exposure to respiratory emissions, we compare this mask and a N95 FFR (as a control) with smaller particles more similar to those in the N95 test method. Sewn from sterilization wrap, our mask (sealed to a mannequin head with silicone) filters 85 ± 1% of 136 nm particles (NaCl, 85 L min–1) and passes quantitative fit tests for 4 of 6 volunteers, representing intermediate protection between a surgical mask and N95 FFR. Filter holder material measurements overpredict the sewn mask’s filtration efficiency by 8.2% (95% CI 7.4–9.1%) (136 or 200 nm). While testing flat material in a filter holder enables comparison between materials, filtration performance does not precisely scale-up from filter holder to mannequin tests. Testing full prototypes at relevant conditions is crucial if an improvised design is intended as a substitute for a commercial surgical mask or FFR. © The Author(s).

The efficacy and safety of hydroxychloroquine in treatment of COVID-19-a systemic review and meta-analysis

Introduction: The treatment of SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) also known as COVID-19 (coronavirus disease 2019) continues to remain an enigma even after six months of the pandemic. Hydroxychloroquine (HCQ) has been one of the most widely tested drugs for SARS-CoV-2 on account of its antiviral properties. However, the results so far have been far from categorical. The meta-analyzes conducted to date are also lacking in precision and appropriateness. This systematic review and meta-analysis addresses the efficacy and safety of HCQ in SARS-CoV-2 by overcoming the limitations of earlier meta-analysis. Materials and methods: A total of five prominent medical databases were searched and fourteen studies (n = 12,455) were included in the systematic review and metaanalyzes. The data on survival, alleviation of symptoms, conversion of RT-PCR positivity to negativity, use and efficacy in presence of comorbidities (hypertension, diabetes, and heart disease), and cardiac and gastrointestinal side effects were extracted. Metaanalysis was applied to calculate the pooled estimates. Fixed-effects model results were chosen since I2 was <25%. Meta-analysis was conducted using STATA version 13 (StataCorp LP, College Station, TX, USA). Results: The pooled estimates showed that HCQ treatment did not significantly affect survival at 14 and 28 days in COVID-19 patients with respect to the control population (RR: 1.003, 95% CI: 0.98-1.02), alleviation of symptoms at day 10 (RR: 1.04, 95% CI: 0.91-1.19), success in presence of co-morbidities (RR: 1.06, 95% CI: 1.04-1.08) and conversion from RT PCR positive to RT PCR negative on day 6 (RR:1.12, 95% CI: 1.04-1.21). There was higher risk for cardiac side effects (RR: 2.01, 95% CI: 1.43-2.83) and gastrointestinal side effects (RR: 1.32, 95% CI: 0.73-2.38) in HCQ recipients. Discussions: Our study is the most recent update on the safety and efficacy of HCQ in SARS-CoV-2 infection and an in-depth analysis of its survival benefits and alleviation of symptoms. This large systematic review and meta-analysis of 12,455 patients encompassing 14 studies has clearly demonstrated the lack of benefit of HCQ treatment for SARS-CoV-2 infection. It has additionally found higher cardiovascular side effects in the recipients of HCQ. It has assessed the benefits of using HCQ in the presence of heart disease, hypertension, and diabetes which no other meta-analysis has investigated so far. It has found that use of this drug is used more commonly in patients with these diseases but did not improve the outcome as compared to control. Conclusion: There is no evidence on the safety and efficacy of HCQ either alone or in combination with other drug treatments in SARS-CoV-2 infection.

Hatey Bazarey – A novel approach to ensure food security in lockdown period through an Android App

The adverse effects of the Novel Corona Virus or COVID – 19 on countries like United States of America, Italy, and Spain depicts how fast this virus can spread due to our irresponsible living habits. Till date, social distancing is the only solution to prevent the communal outspread of this pandemic. Other than fighting Corona, Government’s primary concern is to ensure proper food security for every citizen through Public Distribution System. But crowd management at such Fair Price Shops is a hectic job for the administration. Hence in this paper, we look forward to discussing a unique method of Public Distribution with the help of an Android App, named Hatey Bazarey, specially designed for Ration Dealers. Through this method, we aim to provide home delivery service of the lower-priced items as well as to keep a detailed record of the distributed items in a digital platform. © 2021 Institute of Physics Publishing. All rights reserved.