Scientific rationale of Indian AYUSH Ministry advisory for COVID-19 prevention, prophylaxis, and immunomodulation

The current outbreak of COVID-19 is caused by the SARS-CoV-2 virus that has affected > 210 countries. Various steps are taken by different countries to tackle the current war-like health situation. In India, the Ministry of AYUSH released a self-care advisory for immunomodulation measures during the COVID-19 and this review article discusses the detailed scientific rationale associated with this advisory. Authors have spotted and presented in-depth insight of advisory in terms of immunomodulatory, antiviral, antibacterial, co-morbidity associated actions, and their probable mechanism of action. Immunomodulatory actions of advised herbs with no significant adverse drug reaction/toxicity strongly support the extension of advisory for COVID-19 prevention, prophylaxis, mitigations, and rehabilitation capacities. This advisory also emphasized Dhyana (meditation) and Yogasanas as a holistic approach in enhancing immunity, mental health, and quality of life. The present review may open-up new meadows for research and can provide better conceptual leads for future researches in immunomodulation, antiviral-development, psychoneuroimmunology, especially for COVID-19.Copyright © 2021, Institute of Korean Medicine, Kyung Hee University.

Knowledge Practice Gap Among Nurses Towards COVID-19 Patients' Dead Body Care in a Tertiary Care Hospital

COVID-19 due to SARS-CoV-2 is a global pandemic that presents a serious challenge from many angles for healthcare professionals. The virus causes a potentially fatal disease that is easily transmitted among patients and caregivers, hence specific dead body care is required for such patients. Our study was conducted to identify knowledge, attitude, and practice regarding COVID-19 dead body care among hospital nursing personnel. A cross sectional survey-based study was performed involving 282 nurses who worked in COVID-19 units during data collection from July 2020 to September 2020. The online structured questionnaire was based on world health organization guidelines, institutional infection control protocols, and course material regarding emerging respiratory diseases including COVID-19. We found that work experience in the COVID-19 unit had a significant impact on knowledge and practice regarding COVID-19 dead body care. Similarly, we observed that training improved the knowledge and practice of nursing personnel regarding dead body care. Good knowledge, attitude, and practice were observed in experienced and trained nurses (p-value <0.005). No significant changes were observed with age, gender, and education qualification. Overall knowledge, attitude, and practice regarding COVID-19 dead body care were moderate to good. Adequate training among nurses should prevent the transmission of disease due to occupational exposure.Copyright © 2023, Science Publications. All rights reserved.

Health informatics system using machine learning techniques

A global pandemic is the cause of concern for humanity. The data collection and their analytics are a critical part of research and clinical studies for decision-making activities in the healthcare sector. Healthcare informatics systems and analytics (HCI&A) is a rapidly emerging technology in the medical domain that could be explored for analyzing pandemics like coronavirus disease 2019 (COVID-19). The ethical, legal, and privacy issues to be considered during data collection for research activities. Data governance and data stewardship are required to be addressed during interoperability and interpretation while sharing and reusing the data in collaborative research. The sharing of comprehensive records of clinical data collected by EHRs, also known as electronic health records, to be stored and analyzed on a time-to-time basis. The emerging area of information technology, represented by big data and artificial intelligence (AI) technology, has been widely studied in recent circumstances like COVID-19 for pandemic management. The possibility of using machine learning is explored for better predictive diagnostics and treatment. This chapter discusses the application of artificial intelligence in pandemic management including prevention, diagnosis, treatment, and also critical policy decisions in the COVID-19 pandemic. The methods to collect the digital data of health records are categorized along with few constraints as most of the electronic records related to clinical and epidemiological data are obtained through a shared database such as national and international collaborative informatics infrastructure. The necessity of digital technologies for pandemic emergencies including medical infrastructure reorganization and data workflow model is highlighted. A comparative study of different machine learning models is discussed in the subsequent sections. The digital healthcare informatics envisage a decentralized network architecture and better privacy and security such as blockchain and heterogeneous data collection with machine learning capability are also emphasized. © 2022 River Publishers.

Ayurvedic Herbs Advised for COVID-19 Management: Therapeutic Potential and Clinical Relevance

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. There is no effective medication for COVID-19 as of now, so it would be good to take preventive measures that not only boost our immunity but also fight against infections. The use of traditional Chinese medicine in China to treat COVID-19 patients sets the prototype demonstrating that traditional medicines can contribute to prevention and treatment successfully. In India, the Ministry of AYUSH (Ayurveda, Yoga, Unani, Siddha, Homeop-athy) released a self-care advisory during the COVID-19 crisis as a preventive aspect. This review article discusses the therapeutic potential and clinical relevance of some herbs [(Tulsi (Ocimum sanctum), Haridra (Curcuma longa), Tvaka (Cinnamon), Maricha (Piper longum), Shunthi (Zingi-ber officinale), Munakka (Dried grapes), Lavang (Syzigiumaromaticum), Pudina (Mentha arvensis), and Ajwain (Trachyspermum ammi)] advised by AUYSH to take during COVID-19 infection. They are effective in COVID-19 management, therefore, authors have discussed their detailed traditional uses as therapeutics and spotted scientific insight and clinical significance of the herbs mentioned above along with their mechanistic viewpoint, adequately, on a single platform. Provided information could be a treasure to open up a new research arena on natural products to manage human health crises effectively, caused not only by COVID-19 but also by other infectious diseases.Copyright © 2023 Bentham Science Publishers.

Factors Influencing Suicidal Behaviour among University Students: A Cross-Sectional Study from North India

In the majority of the affected nations, suicidal behavior against COVID-19 leads to various concerns. This study aimed to analyze determinants affecting suicidal behaviour among university students in Uttarakhand. An online cross-sectional survey of 18-year-old university students in Uttarakhand was conducted between April 2 and May 13, 2022. The questionnaire comprised socio-demographic information, the Suicidal Behaviors' Questionnaire-Revised (SBQ-R) scale, and elements related to the physical and psychological health of COVID-19 (CRPPF). The statistical study included demographic information, basic statistics in terms of frequency and percentage, and logistic regression. In comparison to students with fewer than seven family members, students with more than seven family members were less likely to participate in suicide behaviour (AOR = 2.21;95% CI: 1.79 to 2.67) and vice versa (AOR = 0.81;95% CI: 0.56 to 0.97). According to the study, a substantial majority of students (76.35%) claimed that the lockdown implemented to stop the spread of COVID-19 was extremely upsetting for them and that the pandemic had caused them to miss their graduation (73.90%). Adjusted multivariate logistic regression shows that feelings of a burden on family, (AOR= 1.98, 95% CI: 1.09 to 2.82), distancing from family or friends, (AOR =1.66;95% CI: 1.26 to 2.01), having relationship dilemmas, (AOR= 2.31;95% CI: 1.84 to 2.97), and being anxious during the lockdown, (AOR= 1.84;95% CI: 1.08 to 2.27), are significant factors among participants that are linked to higher risk of engaging in suicidal behaviour. The possibility of university students engaging in suicide behaviour was significantly affected by numerous factors. In addition to defending the students' mental health, the concerned authorities should devise and implement strategies to safeguard the students' physical health. © 2023, Editorial board of Journal of Experimental Biology and Agricultural Sciences. 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.

Impact of COVID-19 on Societal Behavior via Twitter Analytics

The outbreak of the COVID-19 pandemic has caused a notable challenge to the well-being of people all around the globe. In such times, it is of foremost importance to analyze the information posted by people on social media. In this study, a Twitter-based dataset related to COVID-19 has been analyzed, and the effect of the pandemic on societal behavior has been revealed. Tweets have been hydrated and pre-processed using the NLTK toolkit to find the most frequently posted COVID-related words. This research can help identify the social response of people to the Pandemic, realizing what people are majorly concerned about and extracting knowledge about the daily trend of sentiments around the world. It has been concluded from our analysis that rather than the expected negative trend in the use of COVID-19 terms on a daily basis, more positive figurative language has been used in the posted tweets.

MoS2 Quantum Dots Based MSM Deep UV Photodetector

This letter reports facile hydrothermally synthesized colloidal MoS₂ quantum dots (QDs) based inexpensive solution processed metal-semiconductor-metal (MSM) photodetector (PD) with device structure Au/MoS₂ QDs/Au on Si/SiO₂ substrate to observe the ~275 nm deep UV radiation used in SARS-CoV-2 virus inactivation. The ligand exchange technique has been used thereby, reducing inter QDs distance for better film quality. Under deep UV exposure (275 nm), the fabricated MSM PD displays good responsivity (343.53 mA/W), external quantum efficiency (EQE) (154.9 %), and detectivity (2.51 ×1011Jones) values. The time response analysis under deep UV irradiation ~275 nm demonstrates adequate rise time (81.17 ms) and fall time (79.58 ms). IEEE

Impact of COVID-19 on volatility of BSE Sensex stock index

The present study investigates the impact of COVID-19 on the volatility of BSE Sensex stock index. The weekly data on COVID-19 fatality cases, an independent variable, in India from 1 March 2020 to 27 December 2020 has been taken from the official website of the World Health Organization. The weekly data on a dependent variable (Sensex) and control variables (crude oil, Bitcoin, Ethereum, Litecoin) have also been considered for the period under study. The GARCH(1, 1) model has been used to extract the volatility series of the variables that are considered in the investigation, and vector error correction model (VECM) is also applied. Further, robust tests like ADF, variance decomposition test, impulse response test have been performed to check the validity of the results. The findings suggest the significant negative effect of COVID-19 fatality cases on BSE Sensex stock index during the specified study period. This negative coefficient of COVID-19 fatality cases in India reflects the increasing volatility of the BSE Sensex stock index. © 2022 Inderscience Enterprises Ltd.

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.

Effects of Covid-19 on Patients with Cardiovascular Diseases: A Systematic Review

Aim of Object: During the COVID-19 pandemic, the entire world is experiencing a mortality situation;most people are battling against the corona virus, but some individuals have already suffered from cardiovascular problems. For improved patient care, adequate information and comprehension of the relationship between cardiovascular disorders and COVID-19 is required. The dominant clinical manifestations of the corona virus infection are on the respiratory system. In this instance, the acute cardiac injury is the most often reported cardiac abnormality, in which the degree of cardiac output is increased, troponin levels rise, and mostly it is found in about 8% to 12% of patients. The involvement of viral cardiomyocytes and systemic inflammation is the most prevalent mechanism for cardiac damage. The corona virus attaches itself and enters through angiotensin converting enzyme-II. Discussion and Conclusion: Recent articles on COVID-19 have revealed nothing regarding these individuals' cardiac vascular manifestations. This is a critical component of all that has a big influence on COVID-19 patients' cardiovascular systems. To fully comprehend the method and effects, more study is required.

Analysing time-frequency relationship between COVID-19 and FII trading activities using wavelet coherence analysis

Recently, the emerging markets like India have exhibited huge volatility due to trading activities of FIIs in the pandemic situation due to COVID-19. The fear of a lockdown situation during the pandemic raised concerns among the market participants and the investors that resulted in a steep fall of 27% in NIFTY50 during 10-23 March 2020. The infusion and withdrawal of portfolio investments have added research dimension as to whether the trading behaviour is due to pandemic and subsequent government actions or has resulted from the other markets like the oil crisis. This paper uses wavelet coherence analysis to examine the co-movement of COVID-19 cases and net investments from FIIs during the different phases of the lockdown period. © 2021 Inderscience Enterprises Ltd.

Amine Functionalized Noble Metal: Metal Oxide Nanohybrid for Efficient Electrochemical Determination of 25-Hydroxy Vitamin-D-3 in Human Serum

Quantification of 25-hydroxy vitamin-D-3 (25-OHVD3) is important because its deficiency is related to numerous diseases, including osteoporosis, depression, diabetes, heart disease, certain autoimmune conditions, and even Covid-19. In the present study, noble metal-metal oxide nanohybrid based on L-cysteine functionalized gold decorated zirconia nanoparticles (Cys-Au@ZrO2 NPs) were synthesized with the objective of enhanced electrochemical behavior, stability, availability of functional group to covalently bind with biomolecules, and developing an efficient immunosensor for 25-OHVD3 detection. The formation of Au@ZrO2 NPs and further Cys functionalization was validated by various characterizations. Cys-Au@ZrO2 NPs were electrodeposited onto ITO substrate and further modified with antibodies specific to 25-OHVD3 (ab-25VD(3)) and bovine serum albumin (BSA) to build an immunosensing platform for 25-OHVD3 detection. The fabricated BSA/ab-25VD(3)/Cys-Au@ZrO2/ITO immunosensor demonstrated an improved sensitivity of 2.01 mu A ng(-1) ml cm(-2), LOD of 3.54 ng ml(-1) for the linear detection range of 1-50 ng ml(-1) with regression constant of 0.98. Moreover, the analytical performance of the BSA/ab-25VD(3)/Cys-Au@ZrO2/ITO immunosensor in determining 25-OHVD3 in human serum samples collected from four healthy people yielded satisfactory results that were highly correlated with the conventional enzyme-linked immunosorbent assay (ELISA).

Correlation between temperature and COVID-19 (suspected, confirmed and death) cases based on machine learning analysis. (Special Issue: Coronaviruses and COVID-19- Past, Present, and Future.)

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of te.

Indian economy amid COVID-19 lockdown: a perspective. (Special Issue: Coronaviruses and COVID-19- Past, Present, and Future.)

COVID-19 virus has resulted in the lockdown of schools, offices, factories, temples, railway stations, and even the airspace. It is estimated that due to the lockdown, the Indian economy may face prolonged adverse impact. The paper is an attempt to ascertain the impact of lockdown on the Indian economy and explore future perspective. The study has addressed important issues like consumption expenditure, demand & supply, unemployment rate, purchasing power, financial market, etc. Under the given circumstances, the lockdown will cost India around USD 120 bn. The manufacturing and service sector has come to an abrupt stop and interrupted domestic supply chains. If this crisis continuous it will indirectly affect all economic sectors. The study has given suggestions as a learning curve which can be used by different stakeholder to improve the economic situation of the country and minimize negative effect of lockdown.

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.

COVID-19 Sample Collection Kiosk in AIIMS Rishikesh: A safe and efficient model

Introduction: Following the pandemic, screening suspected individuals on a large scale is imperative to curtail the spread of the disease to a large extent. The walk-in kiosk is an ideal example of an innovation that is time and labour efficient and safe to use. Methodology and review of literature: Embase, Google Scholar, and PubMed were used to extract scholarly articles about the subject published worldwide. The Walk-in kiosk concept was an idea taken from the biosafety chamber used in advanced microbiology laboratories. Results: This ergonomic design enabled the HCW to perform better without bending forward or reaching out for the oropharyngeal or nasopharyngeal swabs. It avoids a great deal of inconvenience for both HCW and the patient.

QSAR studies of some pharmacologically important compoundsfor SARS CoV

Currently, the outbreak of the novel human respiratory coronavirus, also popularly known as COVID-19, has sought the attention of the scientific community across the world and stresses on the need for new therapeutic alternatives in order to cease the global health crisis and fight the pandemic. The situation, therefore, calls out for new researchcentred on targeting the pathogen. A number of studies reveal the potential of different chemical moieties that could possibly act against the virus. In our work, we report the semi-empirical based 3D-QSAR 3D-quantitaive structure and activity relationship/QSAR studies of 3 series of compounds viz. Ethacrynic Acid Derivatives (E1-E3), Isatin (2,3-oxindole) Inhibitors (I1-I7) and Flavonoid and Biflavonoid Derivatives (F1-F7) on the basis of their reported activities against SARS Co-V. The studies are carried out on Hyperchem 8.0 version software using AM1 and PM3 methods. Selected QSAR/3D-QSAR equations including different physical parameters of these series are reported.

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.