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Context: Amid the COVID-19 Pandemic, the education system swiftly shifted to online mode. Aims: To understand the attitude, comfortability, challenges, and health issues faced by dental students during online classes. Settings and Design: A cross-sectional survey was conducted among dental undergraduate students in Haryana. Materials and Methods: A self-designed questionnaire survey form having five sections and 28 questions was used. Statistical Analysis Used: The content validity index was 0.88. Cronbach's alpha coefficient for attitude was 0.844, and 0.905 for health issues. Descriptive statistics and frequency distribution were studied. Results: The response rate was 94.25%. Most students reported that they find it difficult to concentrate during online classes and strongly agreed that learning is better during offline classes. Practical classes were a bigger challenge in online mode. Conclusions: The majority of students preferred offline classes to online classes. © 2023 Journal of Indian Academy of Oral Medicine & Radiology ;Published by Wolters Kluwer - Medknow.
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Introduction There was and still is much speculation about the COVID-19 pandemic impact on suicide rates. We aimed to assess the effect of the COVID-19 pandemic on suicide rates around the world. Methods We sourced real-time suicide data from countries or countries areas through a systematic internet search (official websites of Ministries of health, police agencies, and government-run statistics agencies or equivalents), recourse to our networks (e.g. ICSPRC) and the published literature (a living systematic review). We used an interrupted time-series analysis to model the trend in monthly suicides before COVID-19 in each country or country area, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic (from April 1 to July 31, 2020, in the primary analysis). We have now updated this work to cover the first 15 months of the pandemic and stratified analyses by age and sex and method. We will present findings from the new updated data (35 countries) at the conference. Results Initially we sourced data from 21 countries (16 high-income and five upper-middle-income countries). Rate ratios (RRs) and 95% CIs based on the observed versus expected numbers of suicides showed no evidence of a significant increase in risk of suicide since the pandemic began in any country or area. There was statistical evidence of a decrease in suicide compared with the expected number in 12 countries or areas. Conclusions This was the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. Early on high-income and upper-middle-income countries, suicide numbers remained largely unchanged or declined compared with the expected levels based on the pre-pandemic period. We need to remain vigilant and be poised to respond as the longer-term mental health and economic effects of the pandemic unfold. We will present updated findings with more recent data.
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Ayurveda is an ancient traditional medicinal system that has gained widespread acceptance, however, its ancient wisdom still needs in-depth investigation. Acharya Balkrishna is a prominent scholar and expert in Ayurveda, Sanskrit, yoga, and the Vedas. His efforts led to the acceptance of Ayurveda and yoga practices by millions of people worldwide. His team is presently working on the World Herbal Encyclopedia, a massive compendium that includes descriptions of 50,000 medicinal plants from around the globe and is intended to boost the traditional medicine system. His continued attempts to save traditional knowledge from extinction have resulted in the digitization of 60 lakh pages of ancient manuscripts. Together with his intellectual partner Swami Ramdev, a well-known yoga guru, he successfully treated millions of patients with a variety of severe ailments over the last two decades to achieve a better universal health. In the service of humanity, both laid the foundation for the herbal and yoga empire. Acharya Balkrishna’s research team developed various evidence-based ayurvedic medications, including Acidogrit, Bronchom Cardiogrit Gold, Madhugrit, and the most popular Coronil, an anti- COVID-19 herbal remedy. He is the recipient of several prestigious accolades, including the Bharat Gaurav and the UNSDG 10 most influential people in healthcare award. He has done extensive research in several areas such as herbal research, the revival of traditional literature, agriculture, organic farming, information technology, and tribal welfare. He has authored more than 150 books, and 200 research articles, and also filed 41 patents. He is an outstanding visionary with a dynamic personality, multifaceted skills, with a commitment to serve the humanity. This article commemorates about Acharya Balkrishna’s revolutionary life and discusses his brief journey from an innocent child to a renowned Ayurvedacharya.
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Despite the development and implementation of vaccinations, the SARS-CoV-2 pandemic has persisted for more than two years due to emerging novel variants. The new Omicron lineages are being intensively monitored by WHO. Scientists from all over the world have reported the recombinant variants, but their existence remains a point of contention. Currently, XE and BA.2 are the most common variants. Deltracron (a recombinant of Delta and Omicron) has been discovered in several investigations, although some experts believe the sequences that confirm its emergence are the product of contamination. This study looked at emerging variants including XD, XE, and XF, as well as various omicron lineages (BA.1, BA.2, BA.3, BA.4, and BA.5). Bioactive lipids including arachidonic acid, docosahexaenoic acid, and eicosapentaenoic acid have been highlighted as possible COVID-19 therapeutics. On the other hand, herbal remedies are supposed to be the key player in minimizing the fatality rates in India. Coronil, a herbal formulation was found to be the evidence-based medicine against SARS-CoV-2. The activity of the Coronil is attributed to its diverse bioactive composition which includes cordifolioside A, rosmarinic acid, magnoflorine, withaferin A, ursolic acid, withanone, palmatine, betulinic acid, withanoside IV, and withanoside V. The interaction of bioactive lipids, Coronil and SARS-CoV-2 emerging variants might be beneficial in the management of pandemic until the herd immunity is achieved.
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Yoga is a multimodal practice that strengthens muscles, improves balance, and promotes flexibility. It has long been used against hypertension, obesity, anxiety, insomnia, aging, and other ailments. Several yog gurus have promoted the yoga legacy, but it has gained worldwide social popularity after the initiation of Swami Ramdev. Swami Ramdev is a spiritual leader best recognized for popularizing yoga and Ayurveda throughout India and the world. He had vowed to become a “Sanyasi” since childhood and achieved his goal with dedication, self-discipline and meditation. Initially, he began teaching yoga at Gurukuls but later laid the foundations for yoga and the herbal empire with his intellectual partner Acharya Balkrishna, a renowned Ayurvedacharya. He established The Divya Yog Mandir Trust in 1995, followed by Patanjali Yogpeeth in Haridwar, India to promote yoga and Ayurveda. These foundations have significantly expanded yoga, spiritualism, and Ayurveda research and treatment centers, including the world’s largest residential naturopathy facility at Haridwar. Swami Ramdev is known for his significant contributions to yoga and Ayurveda by saving millions of people’s life suffering from fatal diseases. Many individuals usually benefit daily from his live programming of yoga and herbal medication on television channels. As a research visionary, during the COVID-19 pandemic along with infrastructure and monetary assistance, his team developed an anti-SARS-CoV-2 remedy, Coronil, which was quite effective and popular among society. Yoga has the potential to improve public health, it is safe, inexpensive, and promotes self-sufficiency. Subsequently, Ayurveda provides comprehensive insights into the treatment and management of various illnesses.
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The novel coronavirus (COVID-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the COVID-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the COVID-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.
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Background: COVID-19 is due to severe acute respiratory syndrome coronavirus 2 (SARSCoV2) and was declared a global pandemic by World Health Organization. Various biochemical parameters and serial High Resolution Computed Tomography (HRCT) chest were used to evaluate the severity of the disease. Aim & Objectives: The study was done to assess the association between biochemical parameters and serial HRCT chest. Material and Methods: It was a retrospective observational study at a tertiary hospital in Central India. Patients >/= 18 years old admitted from 01 July 2020 to 31 December 2020 with laboratory-confirmed COVID-19 cases were included in the study. The laboratory parameters included lymphopenia, neutrophil-lymphocyte ratio (NLR), C- Reactive protein (CRP), Lactate Dehydrogenase (LDH), Ferritin, D- Dimer. Only Serial HRCT chest patients were included in the study. Results: In the study total of 44 patients (29 Males/15 Females) were taken. Serial HRCT chest severity and biochemical markers severity were assessed, and the results were obtained. It was found that there is a definite correlation between biochemical markers severity and HRCT chest severity. Conclusion: Since there was a definitive relationship between the severity of biochemical markers and the severity of HRCT chest, biochemical profile can be utilized to determine severity in the absence of HRCT chest.
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Aim/Purpose This paper aimed to explore the impact of compassion fatigue, compassion satisfaction, and burn-out among the general population during the pan-demic. Background The paper has attempted to explore compassion fatigue, compassion satisfac-tion, and burn-out among the population at large, especially during the pan-demic. This area has not been explored as yet. Methodology A simple random sample of 98 males and 88 females was collected anony-mously through a Google form survey. Part A collected demographic data and Part B comprised of 15 statements with 5 each for compassion fatigue, compassion satisfaction, and burn-out, adapted from a Compassion Fa-tigue/Satisfaction Self-Test. ANOVA single factor was employed for the three variables of compassion fatigue, compassion satisfaction, and burn-out using a 0.05 significance level. Correlations among the variables were also an-alyzed. Contribution The present paper contributes to covering the research gap of investigating the presence of compassion fatigue, compassion satisfaction, and burn-out among the population at large comprising the age group of 18 to 60+ and from different professions. Findings The findings revealed significant differences in the levels of compassion fatigue, compassion satisfaction, and burn-out in the population at large during the pandemic. Impact on Society The paper addresses issues in society at large. Future Research The findings can be further strengthened by extending it to a larger sample size across different nations and, specifically, studying gender differences during such adverse pandemic situations. © This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes.
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The public health threat posed by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has persisted since December 2019. Many countries were able to manage SARS-CoV-2 due to various strategies, like masking, social distancing, and vaccinations. Virus mutations are one of the most significant barriers to the disease's eradication;as a result, various variants have emerged (Alpha, Beta, Gamma, Delta, etc.) from time to time with higher mortality. Omicron, a variant identified from South Africa, has put the world on high alert. In this context, we have reviewed some reports on Omicron along with other variants. This new variant has been designated as a variant of concern by the World Health Organization. The preliminary reports revealed that Omicron is heavily mutated with more than 30 mutations (A67V, del69-70, T95I, del142-144, Y145D, etc.) in its spike protein. Despite vaccination with the most efficacious Pfizer candidate, Omicron infection was reported in South Africa. Bioactive lipids, such as arachidonic acid, alpha-linolenic acid, docosahexaenoic acid, eicosapentaenoic acid, and others have been reported to be the key components in inactivating the virus, so their future role is imperative. Ongoing research for the development of new or modification of existing vaccines must continue. © 2022. Acharya Balkrishna et al. All Rights Reserved.
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The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised global health concerns. RNA-dependent RNA polymerase (RdRp) is the prime component of viral replication/proliferation machinery and is considered to be a potential drug target against SARS-CoV-2. The present study investigated the anti-RdRp activity of phytochemicals against SARS-CoV-2 infection. Virtual ligand screening was carried out to determine the potent compounds against RdRp. Molecular docking and an MD Simulation study were employed to evaluate the spatial affinity of selected phytochemicals for the active sites of RdRp. Structural stability of target compounds was determined using root mean square deviation computational analysis and drug-like abilities were investigated using ADMET. Bond distances between ligand and receptor were marked to predict the strength of interaction. Aloe, azadirachtin, columbin, cirsilineol, nimbiol, nimbocinol and sage exhibited the highest binding affinities and interacted with active sites of RdRp, surpassing the ability of chloroquine, lamivudine, favipiravir and remdesivir to target the same. All the natural metabolites exhibited stable conformation during MD Simulation of 101 ns at 310 K. Kinetic, potential and electrostatic energy were observed to be least in the case of natural metabolites in comparison with synthetic analogues. Deviations and fluctuations were observed to be structurally least in target phytochemicals. Physiochemical and biological properties of these compounds further validated their drug-like properties. Non-bonded distance was found to be short enough to form hydrogen bonding or hydrophobic interactions, which revealed that these target compounds can strongly bind with RdRp. The study found potential phytochemicals to disrupt the replication domain of SARS-CoV-2 by hindering RdRp. We therefore anticipate that the current findings could be considered as valuable for the development of an efficient preventive/therapeutic expedient against COVID-19.
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Aims & Objectives: Atypical Chronic myeloid leukemia (aCML) is a rare subtype of MDS/MPN. The diagnosis of aCML has evolved over years with more evidence from cytogenetic and molecular studies. We report here a case series of three patients with aCML that was picked up based on morphology and molecular workup. Patients/Materials & Methods: Case 1: A 68-year male with incidental detection of leukocytosis on routine hemogram without organomegaly with Hb11.6 g/dL,TLC 35X103/μl,platelets 41X103/ ll,DLC My30MM5N53L8E3Baso1%. Bone marrow was hypercellular with M:E ratio7.7:1, severe dysgranulopoiesis, 07% blasts,and dysmegakaryopoiesis (>50%). His extended MPN reflex panel (BCR-ABL1, JAK2, CALR, MPL) was negative. Provisional diagnosis of aCML was made. NGS panel revealed multiple mutations in ZRSR2, ASXL1, RUNX1, SF3B1, EZH2, TET2 genes. He has completed 6 cycles of Azacytidine and remains stable for 8 months since the diagnosis. Case 2: A 76-year old male with suspected CML presented with mild hepatosplenomegaly with Hb 8.9 g/dL,TLC 46X103/μl, platelets 75X103/μl, DLC Blast3My12MM8 N66Ly8 Mono2Baso1% and 2nRBC/100 WBCs. Bone marrow was hypercellular with M:E 6.3:1, significant dysgranulopoiesis 03% blasts, and dysmegakaryopoiesis. His extended MPN reflex panel was negative. NGS revealed mutations in ASXL1 and KRAS genes. After diagnosing aCML he was started on hydroxyurea and remains stable after 6 months of diagnosis. Case 3: A 71-year old female with suspected CML based on moderate splenomegaly and hemogram showed Hb 8.9 g/dL,TLC 65.94X103/μl, Platelets 45X103/μl with DLC Blast1My29MM22N38Ly5Eo4Baso2% Bone marrow was hypercellular with M:E 5.8:1, prominent left shift in granulocytic series, 01% blasts, no dysgranulopoiesis. There was significant dysmegakaryopoiesis. A provisional diagnosis of CML vs MDS/MPN was kept and was advised molecular workup. BCR-ABL1 and JAK2 mutation were negative. NGS panel revealed ASXL1 and SF3B1 gene mutations. Finally diagnosed as aCML and treated with Hydroxyurea. She developed undiagnosed fever during COVID19 lockdown and succumbed to her illness. Discussion & Conclusion: Atypical CML is a very rare entity with close differential diagnoses of accelerated phase of CML or MDS/ MPN-U. However as more awareness of this entity has emerged, more patients are subjected to NGS evaluation, thus contributing to the knowledge about this under-reported diagnosis from the third world.
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COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.
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COVID-19 , Disasters , Censuses , Humans , Machine Learning , SARS-CoV-2 , United StatesABSTRACT
The global crisis of COVID-19 pandemic has ravaged the world economy and healthcare, igniting much fear, panic, and uncertainty among billions of people. As lockdowns being implemented in many places, panic buying has emerged as a reliable feature of the Coronavirus outbreak. Therefore, it is of urgent needs to examine consumers' panic buying behaviors during COVID-19 to gain a better understanding of the phenomenon and to provide managerial insights for policy-makers and marketers alike. In this study, under the theoretical guidelines of the Stimuli-Organism-Response model and the Competitive Arousal model, we investigate how in the panic situation created by the pandemic, external stimuli such as Limited Quantity Scarcity (LQS) and Limited Time Scarcity (LTS) affect the emotional arousal among people, which in turn influences consumers’ impulsive and obsessive buying behaviors. This study is conducted in a multi-country setting including the U.S., China, India, and Pakistan. Online surveys were conducted during the peak time of pandemic. Our findings show that LQS and LTS significantly increase perceived arousal in consumers, which further leads to more impulsive and obsessive buying. In addition, our results also reveal that excessive social media use intensifies the relationship between scarcity messages and perceived arousal whereas the urge to buy impulsively moderates the relationship between perceived arousal and behavioral outcomes in all selected countries except for India. Theoretical and managerial implications are discussed in details. © 2020 Elsevier Ltd
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COVID19 is now one of the most leading causes of death in the United States. Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of both vulnerable communities and the COVID19 pandemic. This study reports a COVID19 Vulnerability Index (C19VI) for identification and mapping of vulnerable counties in the United States. We proposed a Random Forest machine learning based COVID19 vulnerability model using CDC sociodemographic and COVID19-specific themes. An innovative COVID19 Impact Assessment algorithm was also developed using homogeneity and trend assessment technique for evaluating severity of the pandemic in all counties and train RF model. Developed C19VI was statistically validated and compared with the CDC COVID19 Community Vulnerability Index (CCVI). Finally, using C19VI along with census data, we explored racial inequalities and economic disparities in COVID19 health outcomes amongst different regions in the United States. Our C19VI index indicates that 18.30% of the counties falls into very high vulnerability class, 24.34% in high, 23.32% in moderate, 22.34% in low, and 11.68% in very low. Furthermore, C19VI reveals that 75.57% of racial minorities and 82.84% of economically poor communities are very high or high COVID19 vulnerable regions. The proposed approach of vulnerability modeling takes advantage of both the well-established field of statistical analysis and the fast-evolving domain of machine learning. C19VI provides an accurate and more reliable way to measure county level vulnerability in the United States. This index aims at helping emergency planners to develop more effective mitigation strategies especially for the disproportionately impacted communities.