Conserved serine and proline residues in SARS-CoV-2 spike cleavage sites modulate fusogenicity and infectivity

Background and Objectives: * The spike (S) protein of SARS-CoV-2 virus binds to the host cell receptor which facilitates the virus entry. This interaction is primed by host cell proteases like furin and TMPRSS2 acting at S1/S2 and S2' cleavage sites, respectively. * Both the cleavage sites have Serine and Proline residues conversed in all the coronaviruses. It has been speculated that mutations at these conserved residues may provide a gain-offunction, easing the SARS-CoV-2 entry into the host cell and cellto- cell spread, thus modulating the virulence and pathogenicity. * Unravelling the effects of these conserved residues in the S protein cleavage site in virus entry and transmission might facilitate development of novel therapeutics. Material(s) and Method(s): * This study employed a lentivirus based pseudovirus (PSV) system, where P and S residues at S1/S2 site of Spike gene, present in an expression vector, were mutated to Alanine (Fig A). * We then assessed the expression of the SARS-CoV-2-S variants in HEK293T cells and tested the infectivity and fusogenicity of mutant PSV and spike, respectively in the presence or absence of S1/S2 and S2' protease inhibitors. Results and Conclusion(s): * Conserved Serine residue mutation (S2SA) at S2' cleavage site resulted in complete loss of spike cleavage by furin and cathepsins (Fig B). * TMPRSS2 protease treatment was not able to rescue loss of spike cleavage and fusogenicity (Fig C & D). * S2SA mutant showed no significant response against E-64d and TMPRSS2 inhibitor. * Serine at S2' site in spike protein provides an ideal site to be further evaluated for the therapeutic purpose against SARS-CoV- 2.

Impact of COVID-19 vaccination on menstruation pattern among female nursing and paramedical students in a peripheral medical college in Eastern India

Background: Many females worldwide have reported abnormalities in their menstrual patterns post-COVID-19 vaccination. The present study was conducted to determine the impact of the COVID-19 vaccination on menstrual patterns among female nursing and paramedical students at a peripheral medical college in eastern India. Material(s) and Method(s): The cross-sectional, online self-administered survey was conducted using Microsoft Forms after taking institutional ethical clearance and informed consent from the participants. Two hundred BSc nursing and paramedical students who had received two doses of COVID vaccination (Covaxin or Covishield) and were not suffering from any endocrinopathies, bleeding disorders, structural gynecological abnormalities, or taking any medication known to affect the hypothalamic-pituitary-gonadal axis were included in the study. The questionnaire included menstrual length cycle length and amount of bleeding and there were no direct identifiers. Result(s): The participants reported a significant increase in the amount of bleeding on the heaviest day (mean +/- standard deviation [SD] of the number of pads used was 3.52 +/- 1.15 during prevaccination months vs. 4.64 +/- 1.36 during postvaccination months;P < 0.001) following vaccination. A similar result of increased bleeding on the heaviest day of the period was obtained in both the Covaxin group (mean +/- SD: 3.08 +/- 1.16 vs. 4.88 +/- 1.53;P: 0.001) and the Covishield group (mean +/- SD: 3.59 +/- 1.13 vs. 4.6 +/- 1.34;P < 0.001). No difference in change in the menstrual pattern was observed between the two groups who had received two different types of vaccine (P: 0.527). Conclusion(s): The study showed a possible connection between the COVID-19 vaccination and the change in menstrual patterns.Copyright © 2023 Authors. All rights reserved.

Probiotics and immunity: An overview

Recent outbreak of novel coronavirus COVID 19 as pandemic has threatened mankind and revealed the drawbacks in medical science. Finding innovative ways to treat a disease without generating resistance in pathogens is the need of the hour. Probiotics are commensal microorganisms that reside in the body and confer health benefits to the host. Till date, they have been used merely as food supplement that would enhance digestion. But research shows that they also have effects on the immune system of their host and can be used effectively as an immunomodulator in several diseases like bacterial, viral, cancerous, and autoimmune disorders. This chapter gives a brief insight into the role played by probiotics in enhancing immune response against the cited diseases. © 2023 Elsevier Inc. All rights reserved.

Analyzing changes in travel patterns due to Covid-19 using Twitter data in India

Advancements in technology have enabled researchers to gather large-scale mobility information cost-effectively. In fact, with millions of active users, location-based social media (LBSM) platforms such as Facebook, Twitter, Instagram, Flickr, etc., have become potential big data sources to measure individual behaviour. Despite such passive data collection techniques primarily not providing individual-level information, the sheer volume of such data facilitates a better understanding of aggregate patterns. Besides, conducting the conventional transportation survey during the initial waves of the COVID-19 pandemic was nearly impossible due to social- distancing and lockdown rules. In such context, the present research showcases a method for extracting the mobility traces and identifying the travel patterns of visitors and residents in Delhi from geo-labelled posts on Twitter. Initially, a heuristic classification strategy has been developed based on a few spatiotemporal assumptions to identify and differentiate visitors and residents based on user coordinates. Also, three supervised machine learning techniques, i.e., support vector machine (SVM), k-nearest neighbours (kNN) and decision tree, were used to classify users based on their historical coordinates. Afterwards, the spatial variation of their destination preferences was studied using K-Means, DBSCAN, and Means-Shift clustering techniques, out of which the K- Means clustering method performed best. Lastly, the travel patterns from tweets during pre and during pandemic (COVID-19) were compared using respective clusters. We observed that the performance of the proposed heuristic classifier is comparable with the supervised machine learning (ML) technique used for classification. Furthermore, the results indicate that the proposed model can successfully identify the cluster coordinates for visitors' spots as well as the locations of residents. During the pandemic situation, the mean distance travelled by users is significantly reduced. The study also shows that the number of long-distance trips has also decreased. Also, during COVID, tweets were done from very few unique tourist spots. This suggests lower tendencies of people to travel for tourism purposes. The proposed methods for classification and clustering in the present study will be crucial to obtain individual travel patterns from LBSM data. © 2023 World Conference on Transport Research Society

Analysis and Performance of Solar Photovoltaic Energy System in India: Case Study

There are seven types of renewable energy, of which only five will generate electricity. The most extensively utilized renewable energy source for residential usage is solar photovoltaic energy. With over 486GW of generation capacity, solar PV remains as the third largest renewable power source, with advanced photovoltaic (PV) innovation. The United States and China have the most solar plant installations. India holds the third place as the country's solar area is expected to recover completely after the COVID-19 epidemic, wherein the energy from solar PV is expected to overtake coal before 2040. To examine and observe the various processes carried out by solar PV, several experimental studies have been carried out. This research article presents the different case studies of solar PV systems and observes the characteristics of voltage and current or voltage and power for different solar radiations and temperature, respectively. Secondly, the proposed study has observed and analyzed the performance of PV module in series or parallel connections concerning I-V and V-P characteristics. In third section, the shading effects on solar PV module output power is observed. In the fourth and fifth sections, methods to solve the shading module's output power constraint has been studied and finally the MPP is observed by varying the duty cycle of the converter. In the sixth and seventh section, the performance of SEPIC converter is evaluated based on the open and closed-loop systems, and the challenges in buck-boost converter are solved. © 2022 IEEE.

Understanding the Use and Abuse of Social Media: Generalized Fake News Detection With a Multichannel Deep Neural Network

Fake news has spread across social media platforms and with the ease of access, negative consequences have come with it on individuals and society. This issue has become a focus of interest among various research communities, including artificial intelligence (AI) researchers. Existing AI-based fake news detection techniques primarily make use of a 1-D convolutional neural network (1D-CNN) with unidirectional word embedding. We propose a multichannel deep convolutional neural network (CNN) with different kernel sizes and filters as an AI technique. Multiple embedding of the same dimension with different kernel sizes technically allows the news article to be processed at different resolutions of different n-grams at the same time. Different kernel sizes increase the learning ability of the proposed classification model. The proposed model determines how to integrate these interpretations (different n-grams) most suitably. Three real-world fake news datasets were used in experiments to validate the classification performance. The classification results showed that the proposed model has high accuracy in detecting fake news. Regardless of the dataset, the proposed model can be used for fake news detection in binary classification problems. IEEE

SARS-CoV-2 virus: mechanism of pathogenesis, replication and significant impact on children, cancerous, diabetic patients and pregnant women - a review

'Corona', this alarming word comes from the 'Latin' word 'Crown' that protects the virus. On Dec, 19, firstly, this virus was isolated from three patients having pneumonia connected to a cluster of acute illnesses. WHO declared it a 'pandemic' in Jan, 20 but later in Feb, WHO's general director Tedros Adhanom Ghebreyesus named the virus nCOVID-19. It was first identified in Wuhan, China, as a respiratory illness causing novel diseases (SARS and MERS). CDC informed corona primarily causes mild to moderate upper RTI and, in a few cases, lower RTI (pneumonia, bronchitis). Transmission occurs through direct contact or air droplets of sneezing, coughing, etc. The origin is not clear, but recent studies reported that ACE 2, a membrane exopeptidase receptor, was used to enter the human cell. The primary symptoms are fever above 104 degrees F, shortness of breath, pneumonia, throat soreness, diarrhea, etc. Available approved therapeutics include hydroxychloroquine. This current review updates about the viral transmission and main effect of this virus on children, pregnant women, diabetic, and cancer patients.

Self-care Measures and Hygiene Practices in Shopping—Mediating Effect of Social Distancing

The outbreak of Covid-19 has brought a thrift in the lives of people, business, and the shoppers. As this disease was declared pandemic, the Indian government declared lockdown to break the chain of coronavirus. Due to this, people were locked in their homes and the world was come to stake. This resulted in the anxiety and fear among the people. This lockdown and social distancing guidelines have unsettled the buying habits of the consumers, and it has drastically transformed the shopping habits also. Shoppers are learning to devise and understand new practices. The new normal has become the part of lives of every individual. The study basically aims in identifying the relationship between self-care measure and hygiene practices adopted by the shoppers while they go out for shopping. A total of 236 respondents were administered in Smart PLS version 3.3.2. The results imply that there is a significant relationship between self-care measures and hygiene practices. In addition, the findings prove a mediating role for social distancing, self-care measures, and hygiene practice. The findings of the study can be useful to shoppers and retailers in the devising guidelines to be followed while shopping keeping in lieu of the protection of spreading coronavirus during pandemic. These findings can provide insight to consumer behaviour comprehensively, help companies agreement with similar status quo as well as suggestions for the government to aid businesses effectively in the future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Examining user attitudes towards ride-hailing services - A SEM-MIMIC Ordered Probit approach

Ride-hailing services (RHS) are rapidly transforming the urban transportation landscape, and subsequently, users' perception of mobility services. Hence, it is of utmost importance to understand the perceptual and latent attitudinal factors that drive such service usage. This gains more relevance in the context of a developing nation both because of its characteristically different transport environment-lifestyle interaction, and relatively fewer studies investigating RHS utilization. The present research uses revealed preference data from household surveys (N = 418) to estimate the usage propensity of RHS services in Kolkata, as it has the highest share of commuters among the four large metropolitan areas in India. A SEM-MIMIC Ordered Probit modelling framework has been developed, as it extracts the advantages of exploring latent constructs through a structural equation model (SEM) and examines their interaction with demographics and trip-specific factors with the Multiple Indicator Multiple Cause Model (MIMIC). This study relies on the confirmatory approach to establish the latent attitudinal factors which stem from accepted theories in travel behaviour, i.e., Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM). Subsequently, the Ordered Probit model estimates RHS use-frequency. The results highlight that the latent variables (LVs), viz., ride-hailing attitude and perceived usefulness, are the most significant while estimating RHS utilization. These findings encourage RHS providers to focus on service aspects (for example, security during rides, clean vehicles, reliability of rides, less wait time between booking and ride arrival), instead of only emphasizing on (cosmetic) changes to the booking interface. Besides, in contrast to the developed countries, subjective norms were found to have an inverse relationship with RHS usage, suggesting inhibition among the public, which is probably arising from the dearth of customer-friendly service, especially after being comparatively expensive. The model also suggests the supplementary role of RHS to public transit, which could be pivotal in its integration into mobility-as-a-service (MaaS) and also calls for regulatory actions. The demographics (e.g., age, gender, household income) and trip-specific (e.g., trip purpose, trip length, time-of -day) covariates add further meaning to the relationships among latent constructs. The results suggest a higher preference for RHS among non-car-owners, whereas frequent use of ride-hailing is observed to have a likely positive association with longer trip lengths. Overall, this research brings valuable and first-of-its-kind insights into attitudinal factors and their interaction with demographics and trip-specific covariates facilitating RHS utilization in the context of a developing nation.

Teaching electrocardiogram to first MBBS students in the department of physiology during COVID-19 pandemic: A challenge

Background: Continuing Medical education during the COVID-19 Pandemic has been a great challenge to Medical educators, especially teaching clinical skills online. Aims: The study aims too study the efficacy of teaching ECG in online mode to first MBBS students in the Department of Physiology. Materials and Methods: There are 200 students in the first MBBS batch at Burdwan Medical College. Our method of teaching was flipped classroom-assisted self-directed learning. For teaching ECG to our students we had first arranged for presession MCQ to assess the initial level of knowledge. We had then provided the students with PowerPoint presentations with voice narrations for their self-study, following which the students were divided into batches of twenty and each batch had a team leader and a facilitator. These small batches were shown videos of instruments, methods of recording ECG, normal and abnormal ECG along with explanations in multiple sessions (10). Doubt clearing sessions were arranged for each batch and these sessions were brainstorming. The students were assessed with MCQs (10 marks each Session), oral questions, short answer type questions, spots, and problem-based questions. We also took a feedback survey from the students and provided the students with feedback regarding their performance. Results: MCQ assessments of students in Pre and Posttest session on ECG teaching classes were 50.39 ± 19.41 vs. 65.25 ± 9.14;P ≤ 0.001FNx08. Students performed significantly better in MCQ assessments of students on Normal parameters of ECG assessment as compared to Abnormal ECG parameters: 67.25 ± 10.98 vs. 63.157 ± 7.399;P = 0.000424FNx08. Results of Written Examination and Viva Examination of students in ECG classes were 64.844 ± 9.923 vs. 71.89 ± 10.49;P ≤ 0.001FNx08. Conclusions: The online method of teaching ECG was a success in the institution as observed in this study as observed in the assessment. Students performed better in viva in online exam for ECG and students were satisfied with online delivery. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.

COVID-19 COMPLICATIONS AND SUGGESTED MEASURES: MODERN TOOLS FOR INTERVENING PANDEMIC

Viral diseases are the most devastating health concern worldwide. Outbreaks of coronavirus (CoVs)-related acute respiratory diseases are responsible for the massive health/socio-economic breakdown in the last two decades including the Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), the third reported spillover SARS-CoV-2 from an animal coronavirus to humans. After the H1N1 pandemic influenza (2009), SARS-CoV-2 (novel-beta coronavirus) causing COVID-19 has stretched across 215 countries in 5 major continents with 200,523,190 confirmed cases (4 August 2021;https://www.worldometers.info/coronavirus/). COVID-19 patients had cough, fever, dyspnea, headache, and respiratory failure, as well as shock, acute respiratory distress syndrome, and sepsis in severe instances. Independent of two preceding epidemics, SARS (2002) and MERS (2012), a knowledge gap about the emerging medical manifestations as well as complications of SARS-CoV-2 (2019-2020) infections in humans must be filled, with a focus on immunological complications and computational genomics for forecasting/preparedness for a similar outbreak in the future. This paper aims to address aspects of this gap.

Deep Learning Based Facial Mask Detection Using Mobilenetv2

The Covid-19 pandemic has had a profound effect on our daily lives. One of the most effective ways to protect ourselves from this virus is to wear face masks. This research paper introduces face mask detection that authorities can use to reduce and prevent COVID-19. The face mask recognition process in this research paper is done with a deep learning algorithm and image processing done using MobileNetV2. Steps to build the model are data collection, pre-processing, data classification, model training and model testing. The authors came up with this approach due to the recent Covid-19 situations for following specific guidelines and the uprising trend of Artificial Intelligence and Machine Learning and its real-world practices. This system has been made to detect more than one person whether they are wearing masks or not. This system also gives us the Covid cases-related worldwide updates as per our chosen country and type of cases like total cases, total deaths etc. Such systems are already available, but the efficiency of the available mask detection systems was not achieved thoroughly. This newly developed system proposes to take a step further, which recognizes more than one person at a time and increases the accuracy level to a much greater extent. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A study of performance of first MBBS students in internal assessment conducted online during the COVID-19 pandemic

Background: The COVID-19 pandemic has greatly impacted the progress of medical education. As the crisis continues, it is important to develop valid and reliable methods of assessment. Aims: To assess the impact of the competency-based medical education (CBME) implemented online in the department of physiology during this pandemic on the results of internal assessments conducted online. Materials and Methods: This pilot study was conducted on two hundred First-year MBBS students at Burdwan Medical College after taking Institutional clearance in 6 months. Two internal assessments had to be conducted online in 6 months. We divided our internal assessment sessions which were conducted at three monthly intervals into 4 compartments: multiple-choice questions, short answer type questions, orals, and spots. We conducted two surveys to assess the stress levels of the students and two feedback surveys to assess our teaching program. Statistical analysis: T-test and Chi-square test was used to analyze the data. Results: Assessments scores of students in the first session were 59.68 ± 10.91;assessment scores of students in the second session were 73.21 ± 8.66;P < 0.001 ∗∗ (highly significant). Perceived stress score (PSS) in the first survey was 21.36 ± 3.84 and PSS in the second survey was 20.77 ± 4.13;P = 0.144. Thirty students failed in the first session while 1 in the second session, χ 2 = 32.1;P < 0.0001∗∗. At the end of 3 months, 11.4% had difficulty in studying physiology, while at the end of 6 months, 8.5% had difficulty in studying physiology with online support. Conclusions: The first MBBS students were able to cope up better with the online mode of teaching with the passage of time and regular feedback provided to them. The results of the present study demonstrate that the department of physiology could implement CBME online and conduct internal assessments also. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow

Perceptions of charismatic leadership and safety behaviors during the Covid-19 pandemic – a follower-centric perspective in India and Germany

Purpose: The purpose of the article is to examine the interplay between charismatic leadership and two follower characteristics in predicting safety behaviors during the Covid-19 pandemic in two distinct countries. Design/methodology/approach: The quantitative investigation was conducted during the first wave of the Covid-19 crisis in India and Germany. Given the importance of safety behaviors during the pandemic, the authors proposed high charismatic public leadership, the perception of crisis and belief in science of the constituent influence safety behaviors. Findings: Consistent with the hypothesis, the authors found that there was a positive relationship between charismatic leadership and safety behaviors. Contrary to the expectations, belief in science did not moderate the relationship between charisma and safety behaviors. Opposite to the hypotheses, the relationship between charisma and crisis was stronger under followers' low in perception of crisis. Originality/value: The findings contribute to the understanding of charisma during a crisis and the role of followers' perceptions. Implications include raising awareness about the importance of charismatic leadership in encouraging critical safety behaviors during a crisis, but these effects depend in part on the followers' attributions of the public leader. © 2022, Emerald Publishing Limited.

A Hybrid Model for Effective Fake News Detection with a Novel COVID-19 Dataset

Due to the increasing number of users in social media, news articles can be quickly published or share among users without knowing its credibility and authenticity. Fast spreading of fake news articles using different social media platforms can create inestimable harm to society. These actions could seriously jeopardize the reliability of news media platforms. So it is imperative to prevent such fraudulent activities to foster the credibility of such social media platforms. An efficient automated tool is a primary necessity to detect such misleading articles. Considering the issues mentioned earlier, in this paper, we propose a hybrid model using multiple branches of the convolutional neural network (CNN) with Long Short Term Memory (LSTM) layers with different kernel sizes and filters. To make our model deep, which consists of three dense layers to extract more powerful features automatically. In this research, we have created a dataset (FN-COV) collecting 69976 fake and real news articles during the pandemic of COVID-19 with tags like social-distancing, covid19, and quarantine. We have validated the performance of our proposed model with one more real-time fake news dataset: PHEME. The capability of combined kernels and layers of our C-LSTM network is lucrative towards both the datasets. With our proposed model, we achieved an accuracy of 91.88% with PHEME, which is higher as compared to existing models and 98.62% with FN-COV dataset.

Online teaching-learning experience among medical students in a developing country during the coronavirus disease-19 pandemic: A pilot study

Background: Coronavirus disease (COVID)-19 pandemic has brought a sudden change in education across the globe. To ensure social distancing, Medical Colleges in India also started online medical teaching since Nation Wide lockdown from 24 March 2020. Aim and Objective: To assess the impact and effectiveness of online teaching program provided by the Department of Physiology in Burdwan Medical College, West Bengal, among the first Prof. M.B.B.S students. Materials and Methods: This pilot study was conducted in a time span of 3 months after obtaining Institutional ethical clearance. Two hundred students enrolled in 1st year participated in this study. Mode of teaching was Flipped Class Room Assisted Self-directed Learning. Multiple assessments were conducted. Two surveys to assess stress level with the perceived stress scale of Sheldon Cohen and three feedback surveys to assess and modify the online teaching program were conducted. Results: Academic activities carried by the majority of students were 5 h or more. About 77.1% were satisfied with the online support, 86.1% felt that regular assessment and feedback provided to them were beneficial, and 11.4% had difficulty in studying physiology in online mode. About 87% students wanted the online support to continue along with offline mode in the near future. Students performed significantly better in post-test sessions (65.155 ± 4.74 vs. 53.378 ± 5.4;P = 0.0045**) as compared to pre-test sessions. There was no significant difference in performance between traditional lecture (even with revision) classes and online sessions. No significant difference in stress scores was observed between two surveys conducted in consecutive 2 months. Conclusions: It is evident that online teaching is an effective tool in teaching physiology to undergraduate medical students and may be taken into consideration in future teaching-learning and assessment program.

MCNNet: Generalizing Fake News Detection with a Multichannel Convolutional Neural Network using a Novel COVID-19 Dataset

During the pandemic of COVID-19, the propagation of fake news is spreading like wildfire on social media. Such fake news articles have created confusion among people and serious social disruptions as well. To detect such news articles effectively, we propose a generalized classification model (MCNNet) having the power of learning across different kernel-sized convolutional layers in different parallel channel network. The capability of MCNNet is lucrative towards any real-world fake news dataset. Experimental results have demonstrated the performance of our model with different real-world fake news datasets. © 2021 Owner/Author.

Health information: Applications and challenges in the COVID-19 pandemic

Global health systems are under immense pressure with the exponential growth and spread of COVID-19. Public health and health system responses to the pandemic have relied on health information reporting, visualisation, and projections of incidence, morbidity, and mortality. This commentary aims to explore how health information has been used to inform the public, manage risk, understand capacity, prepare the health system and to plan public health strategy. We also aim to share the health information challenges and our insights to inform future debate and strategic investment. This paper will be relevant to health service and health information managers wanting to understand vulnerabilities and focus for future health information initiatives. © Asia Pacific Journal of Health Management 2020. All rights reserved.