Targeting Angiotensin-converting Enzyme 2 (ACE2) for the Discovery of Anticoronaviral Drugs

Coronaviruses are a leading cause of emerging life-threatening diseases, as evidenced by the ongoing coronavirus disease pandemic (COVID-19). According to complete genome sequence analysis reports, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, has a sequence identity highly similar to the earlier severe acute respiratory syndrome coronavirus (SARS-CoV). The SARS-CoV-2 has the same mode of transmission, replication, and pathogenicity as SARS-CoV. The SARS-CoV-2 spike protein's receptor-binding domain (RBD) binds to host angiotensin-converting enzyme-2 (ACE2). The ACE2 is overexpressed in various cells, most prominently epithelial cells of the lung (surface of type 1 and 2 pneumocytes), intestine, liver, kidney, and nervous system. As a result, these organs are more vulnerable to SARS-CoV-2 infection. Furthermore, renin-angiotensin system (RAS) blockers, which are used to treat cardiovascular diseases, intensify ACE2 expression, leading to an increase in the risk of COVID-19. ACE2 hydrolyzes angioten-sin-II (carboxypeptidase) to heptapeptide angiotensin (1-7) and releases a C-terminal amino acid. By blocking the interaction of spike protein with ACE2, the SARS-CoV-2 entry into the host cell and inter-nalization can be avoided. The pathogenicity of SARS-CoV-2 could be reduced by preventing the RBD from attaching to ACE2-expressing cells. Therefore, inhibition or down-regulation of ACE2 in host cells represents a therapeutic strategy to fight against COVID-19. However, ACE2 plays an essential role in the physiological pathway, protecting against hypertension, heart failure, myocardial infarction, acute respiratory lung disease, and diabetes. Given the importance of ACE's homeostatic role, targeting of ACE2 should be realized with caution. Above all, focusing on the SARS-CoV-2 spike protein and the ACE2 gene in the host cell is an excellent way to avoid viral mutation and resistance. The current review summarises the sequence analysis, structure of coronavirus, ACE2, spike protein-ACE2 complex, essential structural characteristics of the spike protein RBD, and ACE2 targeted approaches for anti-coronaviral drug design and development.Copyright © 2022 Bentham Science Publishers.

Impact of COVID-19 on marketing behaviour of mango growers in Prakasam district of Andhra Pradesh

Marketing involves assembling, storing, processing, packaging, grading, transportation and distribution of agricultural commodities from farmers to consumers. Marketing plays an important role in accelerating the pace of economic development of farmers. The present study mainly deals with the impact of COVID-19 on marketing behaviour of mango growers in Prakasam district of Andhra Pradesh. The paper deals with changes that occurred in marketing behavior before and during pandemic, problems faced by the mango growers during pandemic. The study area was selected by purposive sampling and 50 respondents were selected randomly from three villages. The impact of pandemic on marketing behaviour was analyzed with the help of frequency, percentage and paired t-test using SPSS software. Before pandemic 78.40 per cent of mango growers sold their produce when prices are attractive,80.00 per cent sold to the export organization,86.00 per cent to nearby town,78.00 per cent by means of tractor and 82.00 per cent received their market information through fellow farmers. During pandemic, 80.00 per cent of the respondents sold their produce immediately after the harvest, 84.00 per cent of mango growers sold directly to the consumers, 64.00 per cent sold to nearby villages, 64.00 per cent sold by means of other transport sources, 78.00 percent received their market information through social media. Indicators of marketing behaviour such as time of sale, mode of sale, place of sale, mode of transport, source of market information had significant mean difference and showed decrease in mean values during pandemic when compared to before pandemic. The findings of the study revealed that there is a significant impact of pandemic on marketing behaviour of mango growers. Major problem faced by the mango growers during pandemic is inadequate transport facilities (Garrett score 69.32) followed by export organizations remain closed(67.68) and fluctuations in price(60.54).

Effective COVID Monitoring Process to Observe Patients Status using Blockchain

Collecting important information helps physicians, specialists, and health care providers to simplify the care of their patients. However, because patients do not explicitly retain their data, autonomous possession and the right to protect personal data develop. This paper discovers the block chain's potential to improve health care by placing the patient at the center of the system and improving health data protection and collaboration. The main goal of this research work is to discover the vast power of BitCoin technology that can be applied to patient records and health record protection management. As a dispensed era, Blockchain can be very beneficial, giving sufferers control over their statistics and impartial identification. With the COVID-19 pandemic, existing health information exchange systems are being put through the ringer. There is an increase in patient information sharing, as well as the need to respond to medical data requests more efficiently. There are some limitations in the current health information technologies, including the inability to remotely share medical data beyond their protected, local data stores. As a result, a secure and user-centric approach to accessing and controlling sensitive medical data is provided that is based on Blockchain immutability and decentralization. An innovative peer-to-peer system underpins the framework. Information is distributed through smart contracts that are connected to a blockchain-based protocol to ensure data integrity and traceability. Implementing the framework over a pilot study demonstrates its effectiveness. © 2022 IEEE.

A Comparative Study on Gender -wise Social Media Utilization Behavior of Agricultural Students during Covid – 19

Social media utilization behavior of youth has tremendously changed. There were major differences observed in boys’ and girls' social media utilization behavior Keeping this in mind, study was undertaken with the 1st year M.Sc. (Agriculture) students of Tamil Nadu Agricultural University. 50 students were taken as respondents for this study. The study focuses on analyzing genderwise differences in social media utilization behavior. The variables considered for this study were experience in social media;most frequently used social media, duration, frequency, status-posting behavior, a suitable time for social media use, and money spent on social media. Nearly 88.8 percent of boys had more than five years of experience in social media. Among girls, 56.3 percent of girls had 3 – 4 years of experience in social media. Most of the boys (55.6%) used social media to make new relationships in their life. On the other hand, 93.8 percent of the girl respondents used social media to sustain their already established relationships in their life. Mostly 44.4 percent of boys considered that usage of social media affects their academic performance and then most of the girls (37.5%) respondents considered that usage of social media improved their academic performance. This study revealed some observable changes in the social media utilization behavior of boys and girls in the context of time spent on social media, experience in social media use, money spent on the internet, and types of content shared on social media. it was also observed that there exists some difference in the social media utilization behavior among boys and girls. [ FROM AUTHOR] Copyright of Madras Agricultural Journal is the property of Madras Agricultural Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Study on marketing behavior of 'Uzhavar Sandhai ' vegetable farmers during COVID 19 in Coimbatore district of Tamil Nadu

During the Covid 19 disaster and lockdown imposed by the Government, farmers found it difficult to harvest, transport, and market their farm produce and sell at competitive prices. Vegetables being highly perishable required immediate marketing. 'Uzhavar 'Sandhai' (Farmer's market) was one of the main channels for selling fresh vegetables directly to consumers. The present study was conducted to understand the marketing behaviour of vegetable farmers and identify the major constraints faced in marketing their farm produce during Covid 19 in two Uzhavar Sandhai in Coimbatore district of Tamil Nadu. A sample of 50 sellers was selected based on simple random sampling method. The results revealed medium level of overall marketing behavior among vegetable farmers in 'Uzhavar 'Sandhai' during Covid 19 pandemic situation. The lack of transportation facilities and non-availability of buyers visiting the Sandhai were the significant constraints faced by vegetable farmers. The study also found the need for grading-based fixed prices for the vegetables sold in 'Uzhavar Sandhai'.

Prediction of Epidimic Outbreak using Deep Learning Methods

In late December 2019, a lot of unexplained pneumonia cases have been accounted for in Wuhan, China. Later it is declared a pandemic. The spread of the virus from people is increasing at a high rate and that led to the loss of human life. People with Hereditary diseases are likely to be the most affected by this virus. In this, an analysis is made to know in how many days a patient will take to recover from virus and how the infection spread across people. This analysis is performed using Deep Learning (DL) method. K-Means and the SIR Model are the two models used for implementation to analyze the disease. © 2021 IEEE.

Detection of COVID-19 using Hybrid ResNet and SVM

The whole world facing a huge crisis because of Corona virus also known as COVID-2019, identified first in December 2019 in the city of Wuhan located in China. The detection of persons infected with the virus is most important as it can be spread easily from him to others and also the person infected with the virus may not know that he is infected until a number of symptoms fallout from him. In this paper the virus detection is done using deep learning and machine learning algorithms using the X-ray images. A dataset is created with three classes consisting of normal, corona virus, and pneumonia images. The proposed method uses ResNet50 and SVM, deep learning features are extracted using ResNet50 and classification is done using SVM classifier. The classification accuracy obtained from the model is 100% when testing on the Corona virus and normal images, whereas the results obtained from the model is 94% when it is tested on the dataset consisting of normal, Corona virus and pneumonia images and performed well compared to VGG16. © 2020 IOP Conference Series: Materials Science and Engineering.