How the global airline industry behaved to restrictions on air travel to India? An event study analysis

We examine the impact of the recent restrictions/bans imposed by several nations on air travel to India in the light of the increasing number of infections amid the second wave of covid-19. We employ the standard event study method on a sample of 34 airline stocks across seven nations to find that the recent restrictions/bans on air travel significantly impact the global airline industry, although the country-specific impacts are not similar. We find that the post-event reaction in all nations has been different from those evidenced during the global pandemic declaration. We are the first to examine these impacts during the current wave of the pandemic. It contributes to the literature on the effects of the pandemic on the global airline industry. Further, it also provides practical explanations to the investors on how the airline stocks react to the persistence of the pandemic. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Mucormycosis, Covid -19 & Diabetes "THE TROUBLESOME THREESOME"

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.

A Cross-sectional Survey to Understand the Public Attitude towards Attributes of COVID-19 Vaccine in the Chhattisgarh State of India

BackgroundandObjectives:Thissurveyisconductedtounderstandtheattitudeofthe population towards vaccination for COVID-19. Perception regarding COVID-19 vaccination such as efficacy, duration of protection, etc can affect the affinity of the population for readiness, enthusiasm, and willingness. Materials and Methods: A qualitative cross-sectional questionnaire-based survey was conducted during December 2020 and January 2021 at Chhattisgarh province of India. A bilingual questionnaire consisted of questions on belief, willingness, and attitude to receive future COVID-19 vaccination was developed. The non-probability purposive sample of 1717 respondent (1026 responded online while 691 responses offline) were chosen in this study. Results: 60% and 40% of respondents were male and female respectively. 51.4% of respondents belonged to 31-40yrs of age. 46.1 % of respondents believe that COVID-19 vaccine can prevent COVID-19 illness. In 82% of respondents, willingness was observed for COVID-19 vaccination, and willingness was highly dependent on literacy and qualification. Data support a good belief and willingness of the people from Chhattisgarh province towards the COVID-19 vaccination. Conclusion:The current study annuls the illusion and future hesitancy towards vaccination drive. The government must consider vaccine attributes like cost, the nation of vaccine origin, vaccine booth distances and attitude of the population like education status, occupation, socio-economic status, previous vaccination experience should also be undertaken for the largest single vaccine drive.

Emergency cash transfers for informal workers: Impact evidence from Mexico

We study the effects of a sizeable ($526 PPP) one-time-only emergency cash transfer targeted at self-employed, sub-employed, and informal sector workers during the COVID-19 pandemic. The transfers were processed on a first-come, first-served basis until program funds were depleted, creating a plausible source of exogenous variation in program participation. Combining this discontinuity with a purpose-built phone survey, we find substantial positive effects on measures of food security and psychological well-being three to four months after reception. The point estimates for summary measures of business health outcomes and support for lockdowns are positive but imprecisely estimated. © 2023 Elsevier B.V.

Ground-level gaseous pollutants (NO2, SO2, and CO) in China: Daily seamless mapping and spatiotemporal variations

Gaseous pollutants at the ground level seriously threaten the urban air quality environment and public health. There are few estimates of gaseous pollutants that are spatially and temporally resolved and continuous across China. This study takes advantage of big data and artificial-intelligence technologies to generate seamless daily maps of three major ambient pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10km. Cross-validation between our estimates and ground observations illustrated a high data quality on a daily basis for surface NO2, SO2, and CO concentrations, with mean coefficients of determination (root-mean-square errors) of 0.84 (7.99μgm-3), 0.84 (10.7μgm-3), and 0.80 (0.29mgm-3), respectively. We found that the COVID-19 lockdown had sustained impacts on gaseous pollutants, where surface CO recovered to its normal level in China on around the 34th day after the Lunar New Year, while surface SO2 and NO2 rebounded more than 2 times slower due to more CO emissions from residents' increased indoor cooking and atmospheric oxidation capacity. Surface NO2, SO2, and CO reached their peak annual concentrations of 21.3±8.8μgm-3, 23.1±13.3μgm-3, and 1.01±0.29mgm-3 in 2013, then continuously declined over time by 12%, 55%, and 17%, respectively, until 2020. The declining rates were more prominent from 2013 to 2017 due to the sharper reductions in anthropogenic emissions but have slowed down in recent years. Nevertheless, people still suffer from high-frequency risk exposure to surface NO2 in eastern China, while surface SO2 and CO have almost reached the World Health Organization (WHO) recommended short-term air quality guidelines (AQG) level since 2018, benefiting from the implemented stricter "ultra-low"emission standards. This reconstructed dataset of surface gaseous pollutants will benefit future (especially short-term) air pollution and environmental health-related studies. © 2023 Jing Wei et al.

An emerging natural antioxidant therapy for COVID-19 infection patients: Current and future directions

Objective: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) affects millions of people worldwide. The article aims to review the therapeutic perspective on natural antioxidants, their mechanism of action, pharmacokinetics in management and cure of COVID-19/ SARS-CoV-2 infection. Methods: We conducted a literature search including World Health Organization and National Institute of Health guidelines and clinical trials registered with ClinicalTrials.gov limited to antioxidants in COVID-19 management. Results: Elderly, immunocompromised patients, and others with underlying health conditions or multiple comorbidities have a high mortality rate. Disrupted redox homeostasis and oxidative stress seem to be biological pathways that may increase personal vulnerability to infection. Antioxidants like vitamins C, D, E, epigallocatechin-3 gallate, and morin have been reported to protect against COVID-19 disease. Reactive oxygen species are immunological regulatory elements of viral replication. Natural antioxidants exhibit potential action in preventing inflammation and organ dysfunction during viral infection. They also increase glutathione level, oxygenation rate, and immunological responses in the treatment of sepsis and acute respiratory distress syndrome. Conclusion: No wonder the selection of prevention, treatment, and cure of COVID-19 and SARS-CoV-2 mainly depends upon the antiviral and immunoregulatory activity which they possess. Yet, their efficacy against COVID-19 is of great concern and demands extensive study. © 2023 The Authors. Food Frontiers published by John Wiley & Sons Australia, Ltd and Nanchang University, Northwest University, Jiangsu University, Zhejiang University, Fujian Agriculture and Forestry University.

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

Evaluation of hospital environment for presence of Mucorales during COVID-19-associated mucormycosis outbreak in India - a multi-centre study.

BACKGROUND: An unprecedented rise in the number of COVID-19-associated mucormycosis (CAM) cases has been reported in India. Myriad hypotheses are proposed for the outbreak. We recently reported uncontrolled diabetes and inappropriate steroid therapy as significant risk factors for the outbreak. However, Mucorales contamination of hospital environment was not studied. AIM: To perform a multi-centre study across India to determine possible Mucorales contamination of hospital environment during the outbreak. METHODS: Eleven hospitals from four zones of India representing high to low incidence for mucormycosis cases were included in the study. Samples from a variety of equipment used by the patients and ambient air were collected during May 19th, 2021 through August 25th, 2021. FINDINGS: None of the hospital equipment sampled was contaminated with Mucorales. However, Mucorales were isolated from 11.1% air-conditioning vents and 1.7% of patients' used masks. Other fungi were isolated from 18% of hospital equipment and surfaces, and 8.1% of used masks. Mucorales grew from 21.7% indoor and 53.8% outdoor air samples. Spore counts of Mucorales in air were significantly higher in the hospitals of North and South zones compared to West and East zones (P < 0.0001). Among Mucorales isolated from the environment, Rhizopus spp. were the most frequent genus. CONCLUSION: Contamination of air-conditioning vents and hospital air by Mucorales was found. Presence of Mucorales in these areas demands regular surveillance and improvement of hospital environment, as contamination may contribute to healthcare-associated mucormycosis outbreaks, especially among immunocompromised patients.

Comparative analysis of the diagnostic performance of five commercial COVID-19 qRT PCR kits used in India.

To meet the unprecedented requirement of diagnostic testing for SARS-CoV-2, a large number of diagnostic kits were authorized by concerned authorities for diagnostic use within a short period of time during the initial phases of the ongoing pandemic. We undertook this study to evaluate the inter-test agreement and other key operational features of 5 such commercial kits that have been extensively used in India for routine diagnostic testing for COVID-19. The five commercial kits were evaluated, using a panel of positive and negative respiratory samples, considering the kit provided by National Institute of Virology, Indian Council of Medical Research (2019-nCoV Kit) as the reference. The positive panel comprised of individuals who fulfilled the 3 criteria of being clinically symptomatic, having history of contact with diagnosed cases and testing positive in the reference kit. The negative panel included both healthy and disease controls, the latter being drawn from individuals diagnosed with other respiratory viral infections. The same protocol of sample collection, same RNA extraction kit and same RT-PCR instrument were used for all the kits. Clinical samples were collected from a panel of 92 cases and 60 control patients, who fulfilled our inclusion criteria. The control group included equal number of healthy individuals and patients infected with other respiratory viruses (n = 30, in each group). We observed varying sensitivity and specificity among the evaluated kits, with LabGun COVID-19 RT-PCR kit showing the highest sensitivity and specificity (94% and 100% respectively), followed by TaqPath COVID-19 Combo and Allplex 2019-nCoV assays. The extent of inter-test agreement was not associated with viral loads of the samples. Poor correlation was observed between Ct values of the same genes amplified using different kits. Our findings reveal the presence of wide heterogeneity and sub-optimal inter-test agreement in the diagnostic performance of the evaluated kits and hint at the need of adopting stringent standards for fulfilling the quality assurance requirements of the COVID-19 diagnostic process.

Application of Deep Learning on Student Engagement in e-learning environments.

The drastic impact of COVID-19 pandemic is visible in all aspects of our lives including education. With a distinctive rise in e-learning, teaching methods are being undertaken remotely on digital platforms due to COVID-19. To reduce the effect of this pandemic on the education sector, most of the educational institutions are already conducting online classes. However, to make these digital learning sessions interactive and comparable to the traditional offline classrooms, it is essential to ensure that students are properly engaged during online classes. In this paper, we have presented novel deep learning based algorithms that monitor the student's emotions in real-time such as anger, disgust, fear, happiness, sadness, and surprise. This is done by the proposed novel state-of-the-art algorithms which compute the Mean Engagement Score (MES) by analyzing the obtained results from facial landmark detection, emotional recognition and the weights from a survey conducted on students over an hour-long class. The proposed automated approach will certainly help educational institutions in achieving an improved and innovative digital learning method.

Novel COVID-19 global outbreak and a need for proactive dental care

The global pandemic Novel Coronavirus Disease (COVID-19), which originated in Wuhan, has affected the countries worldwide and has been declared as a public health emergency by World Health Organisation. Because of the exclusive features of dental healthcare set-ups, risk of cross-contamination is greater between patients and dental personnel due to high chances of getting in contact with suspected or asymptomatic COVID-19 patients. Preventive measures are essential to be taken for prevention of furthermore spread of nosocomial infection. The present article provides a brief overview on COVID-19 in dental settings and recommended protocols for screening/assessment, patient management and precautions for dental health care professionals.

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images.

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

An update on novel COVID-19 pandemic: a battle between humans and virus.

In the 21st century, human civilization has witnessed three major epidemics caused by Coronaviruses namely severe acute respiratory syndrome coronavirus (SARS CoV) in 2003, Middle East respiratory syndrome coronavirus (MERS CoV) in 2012 and 2019 novel coronavirus (2019 nCoV) or coronavirus disease (COVID 19) in 2019. Among these, COVID-19 has greater transmission and mortality rate. 2019 nCoV belongs to a large family of positive sense single-stranded RNA viruses (+ssRNA) that can be isolated in different animal species. The most communal symptoms of COVID-19 include fever, cough, and shortness of breath during the incubation period (2-14 days) of infection. COVID-19 transmission is occurring from infected humans to close contact with one another through respiratory droplets, coughs, and sneezes of infected person. Moreover, the virus containing surfaces may also transmit the infection. Diagnosis is being carried out by collecting a nasopharyngeal swab or sputum specimen for detection of SARS-CoV-2 RNA by reverse-transcription polymerase chain reaction (RT-PCR). Rapid diagnosing methods are also under development which can diagnose COVID 19 in few minutes to hours. Currently, there is no specific cure or preventive therapeutics available. Hence, based upon limited in-vitro and anecdotal data, Chloroquine, or Hydroxychloroquine, Remdesivir, Lopinavir and Ritonavir are being employed in the management. Search for new specific anti-viral drugs from natural/synthetic origins is under full swing and many of them are currently used as chemotherapeutic drugs under clinical investigation. Yet, there is a strong need for development of vaccine, which may take several months to few years for the development.

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.