ABSTRACT
Foot-and-mouth disease (FMD) and Coronavirus Disease 2019 (COVID-19) are transboundary diseases caused by single-stranded positive-sense RNA viruses with similarities in genome replication and viral protein synthesis. In FMD, asymptomatic infection leads to carrier status and persistently infected animals that threaten the animals vaccinated with a trivalent inactivated whole virus vaccine. Similar information on COVID-19 is not yet available. As COVID-19 vaccination is introduced in January 2021 (since 16 January 2021 in India), its outcome can be assessed by the year-end;and while doing so, the experiences gained in the control of FMD in livestock worldwide can be applied, including monitoring of vaccination response, duration of immunity, level of herd immunity developed, and antigenic matching of the vaccine virus. Antigenic divergence of the virus is a major issue in FMD, and different geographical regions in the world use different virus strains in vaccine preparations to antigenically match circulating virus strains in respective regions for control of the disease. Non-synonymous mutations in the critical antigenic determinants of SARS-CoV-2 have been observed, and there is likely the existence/development of antigenic variants. Therefore, during the post-COVID-19 vaccination regime, it will be essential to monitor the suitability of the in-use vaccine strain region-wise from time to time, as there could be an eruption of isolated outbreaks in a country arising due to antigenic variation and variants. In the context of the present scenario of COVID-19 around the Globe and multiple ongoing efforts to develop suitable vaccine(s) to control the disease, it is a must to develop NSP-antibody (that differentiate infected from vaccinated) assays to differentiate infected from vaccinated individuals(DIVI;DIVA in veterinary epidemiology). The techniques used and experiences gained in ongoing FMD control programs in the endemic countries can be applied to COVID-19 control in a country;and finally, the Globe. After achieving the control of COVID-19, the aim would be to eradicate the virus, which will be tough even with vaccination, as the disease/infection may become endemic during the time to come. To achieve this, applying the principles of Progressive Control Pathway for Foot-and-Mouth Disease (PCP-FMD;FAO/OIE) to COVID-19 control will be beneficial in its control. The present review discusses the issue of control of COVID-19. © 2021 by the authors.
ABSTRACT
The river water quality index (RWQI) of the upper Ganga canal has been computed to assess the effect of lockdown and unlock phases lead down by COVID-19 in India. Geospatial distribution of RWQI from January 2019 to December 2021 in the study area revealed significant impacts of lockdown on water quality. However, unlock phases (post lockdown) have deteriorated water quality since August 2020 and reached to actual conditions of the river by August 2021. To evaluate the lockdown as a management strategy to clean the river, other factors were reviewed including rainfall data, migration, and other activities. The results revealed that all the monitoring stations have improved water quality index ranging from 3 to 45 between March to June 2020. The River Ganga at Haridwar showed a two-fold improvement in the water quality index making it the highest positive impact of the lockdown, and at Rishikesh, the water quality index showed the least changes. The average decrease in RWQI has been observed to be 13 units in the year 2020 as compared to 2019 between March to June. In October 2020, RWQI has been observed to be higher as compared to the years 2019 and 2021. This is possibly due to a shift in rainfall patterns and other factors such as evapotranspiration, precipitation, and atmospheric temperature involved in river water quality control. Up to 60% reduction in average total coliforms and fecal coliforms has been observed due to the nationwide lockdown and a shift in human behavior towards cleaner and sustainable approaches. © 2023, Chulalongkorn University - Environmental Research Institute. All rights reserved.
ABSTRACT
In the present work, the time series of exchange rates of 5 dominant global currencies in terms of the Indian currency rupee is analysed. The time series of the exchange rate of EURO, GBP, JPY, SGD and USD in terms of Rupee from 2015 to 2021 are considered in present study. The Indian currency is taken as the base currency to analyse impact of various critical events on it and to identify safe heaven currencies. The technique of random matrix theory (RMT) is applied in static periods. For static period, 8 time windows of one year length starting from 2015 were considered. The static analysis identifies highly correlated and weakly correlated currencies during local critical events such as Covid-19 and Demonetisation in India. The study shows impact of such events on correlations among global currencies. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
As a result of the outbreak, an unusual virus spread event has occurred, threatening human safety worldwide. To prevent infections from spreading quickly, large numbers of people must be screened. Rapid Test and RT-PCR are common testing tool for regular testing that is used to test all covid affected users. However, the increasing number of false positives has paved the way for the investigation of alternative test methods for corona virus effected patients' chest X-rays have shown to be an effective alternate predictor for testing if an individual is affected with COVID-19 virus. However, consistency is, once again, dependent on radiological experience. A diagnostic decision support device that assists the physician in evaluating the victims' lung scans can alleviate the doctor's medical workload. Machine Learning Techniques, specifically Convolutional Neural Networks (CNN) VGG16 model is used to train dataset and use trained model to predict, have been developed in this project. Four distinct deep CNN architectures are tested on photographs of chest X-rays for treatment of COVID-19. The collection of data sets of covid 19 X-ray imageries and non-covid 19 X-ray imageries are used to train the model and test its accuracy. CNN-based architectures were discovered to be capable of diagnosing COVID-19 disease. © 2022 IEEE.
ABSTRACT
The present review describes about the disease and the fungal agent in brief, and also stresses for establishment of mechanically ventilated hospital wards across the country. Second wave of COVID-19 pandemic in India has been complicated by high infection rate (R-0), 600% jump in medical oxygen demand by COVID hospitals, and after all emergence of Mucormycosis since April-May 2021 leading to fatal complications. Mucormycosis is a very rare infection in humans. It is caused by exposure to mucor mould/ fungi which is commonly found in soil, plants, manure, and decaying fruits and vegetables. These Fungi belonging to the class Zygomycetes and order Mucorales often cause devastating angio-invasive infections, primarily in co-morbid and immunocompromised patients. The emergence of mucormycosis is being reported globally. In India, people with diabetes mellitus are highly susceptible to the disease. But during the second wave of Covid-19 in India, several cases of mucormycosis, also called the "black fungus" disease, have been reported among convalescing and convalescent COVID-19 patients across India with a mortality rate of 50%. More than 5000 cases of mucormycosis during the second wave of Covid-19, have been reported in about 21 states of the country, with Maharashtra and Gujarat states reporting highest number of cases (as on 21 May 2021). The infection has claimed over 120 lives so far, and has complicated recovery from Covid-19 in many more. The Indian Union health ministry has declared mucormycosis as a notified disease under the Epidemic Diseases Act, 1897.
ABSTRACT
Coronaviruses (CoVs), classified into four genera, viz., alpha-, beta-, gamma-, and Delta-CoV, represent an important group of diverse transboundary pathogens that can infect a variety of mammalian and avian species including humans, animals, poultry, and non-poultry birds. CoVs primarily infect lung and gut epithelial cells, besides monocytes and macrophages. CoVs have high mutation rates causing changes in host specificity, tissue tropism, and mode of virus excretion and transmissions. The recent CoV zoonoses are SARS, MERS, and COVID-19 that are caused by the transmission of beta-CoVs of bats to humans. Recently, reverse zoonoses of the COVID-19 virus have been detected in dogs, tigers, and minks. Beta-CoV strains also infect bovine (BCoV) and canine species (CRCoV);both these beta-CoVs might have originated from a common ancestor. Despite the high genetic similarity between BCoV, CRCoV, and HCoV-OC43, these differ in species specificity. Alpha-CoV strains infect canine (CCoV), feline (FIPV), swine (TGEV and PEDV), and humans (HCoV229E and NL63). Six coronavirus species are known to infect and cause disease in pigs, seven in human beings, and two in dogs. The high mutation rate in CoVs is attributed to error-prone 3′-5′ exoribonuclease (NSP 14), and genetic recombination to template shift by the polymerase. The present compilation describes the important features of the CoVs and diseases caused in humans, animals, and birds that are essential in surveillance of diverse pool of CoVs circulating in nature, and monitoring interspecies transmission, zoonoses, and reverse zoonoses. © 2021, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.