The impact of video-based instruction on CPR knowledge in elementary school students (10-11 years)

Introduction: The European Resuscitation Council recommends cardiopulmonary resuscitation (CPR) training at age 12 or earlier to improve CPR utilization. [1] The COVID-19 pandemic has caused limitations on resources and in-person instruction (IPI). Remote video instruction (RVI) may increase participation in CPR education in areas with otherwise limited capacity. Purpose(s): The study compares the efficacy of RVI versus IPI on elementary school students' theoretical knowledge, confidence, and attitudes about CPR. Method(s): Data for in-person and remote instruction was collected in fifth grade classrooms (age 10-11 years) from 2019 to 2021. In the experimental RVI group, a total of 62 students (50.0% female) viewed a thirty-minute video-based lesson, practiced on household objects, and were invited to create their own instructional video. A total of 385 students (53.3% female) in the control IPI group participated in a one-hour traditional lesson with video and hands-on instruction. After their respective lesson, students completed a questionnaire assessing demographics, knowledge, and attitudes about CPR within 24 hours. Result(s): Demographics are listed for the participating students in Table 1. There was a significantly higher rate of previous training for the in-person group. A logistic regression showed that this did not correlate with performance on knowledge items. In-person instruction did not demonstrate superiority to remote video instruction in any of the knowledge items tested, as listed in Table 2. In fact, the students in the RVI group achieved significantly better outcomes in knowledge of compression rate (85.5% vs 55.7%, p<0.001). The RVI group also showed a trend towards superiority in knowledge of the first step of CPR (79.0% vs 68.6%, p=0.0964) Compared to students in the control group, students receiving RVI achieved confidence at similar levels (p=0.546). There was an increase in confidence from a median of 3 to 4 in both the RVI and IPI groups. Students from both groups similarly agreed at high levels that CPR should be required, and that training should start at 5th grade. Of note, the students receiving RVI demonstrated a willingness to disseminate their knowledge, with 37.1% willing to share with 6 or more people. Altogether, these students created 62 videos to share with members of their community. Discussion(s): The results show that remote instruction is a viable method that can achieve similar if not better results versus in-person instruction. A growing body of evidence supports that children as young as 11 years can acquire CPR knowledge and skills. [2] Students in both arms strongly supported training starting in 5th grade. Previous research has shown that younger learners have a higher likelihood of sharing CPR training with others. [3] In our study, a significant portion of students created and planned to share videos with community members, suggesting a powerful multiplier effect.

Thromboelastography Determined Fibrinolysis Shutdown and Its Correlation with Complications and Outcomes in Patients with Coronavirus Disease 2019

Introduction: Coronavirus disease 2019 (COVID-19) causes abnormalities in the hemostatic system, which are referred to as COVID-associated coagulopathy. Whole blood viscoelastic studies, such as thromboelastography (TEG), are the best way to determine the dynamics of clot formation. The hypercoagulable state seen in COVID-19 patients is aggravated by a severe state of fibrinolysis shutdown, which is caused by overexpression of plasminogen activator inhibitor 1 and thrombin activated fibrinolysis inhibitor. Method(s): An automated thromboelastogram was used to perform TEG on 28 COVID-19 patients. Based on TEG parameters such as R-time, K-time, alpha angle, maximum amplitude (MA), and clotting index (CI), the hemostatic state was classified as hypercoagulable in 17 (63 percent), hypocoagulable in 2 (7 percent), or normal in 8 (30 percent) patients. Result(s): Twenty-seven patients were included, with a median age of 50 years (IQR 40-60 years) and a male to female ratio of 0.9:1. COVID-19 was mild in 6 (22.2%), moderate in 2 (7.4%), and severe in 19 (70.4%) patients. Fibrinolytic shutdown was detected in 4 (15%) patients. TEG analysis revealed that they were hypercoagulable. All four patients showed high D-dimer levels and a LY-30 of 0%, as well as decreased K-time and increased alpha angle or MA. Conclusion(s): Despite thromboprophylaxis, hypercoagulable states and severe inflammatory states are prevalent in COVID-19 patients. Patients with hypocoagulable and hypercoagulable conditions had greater 28-day mortality than those with normal coagulation. (Log-rank test, P=0.002). Only two of the four patients who had their fibrinolytic systems turned down survived. These patients had no clinically evident thrombosis.

Thromboelastography Determined Fibrinolysis Shutdown And Its Correlation With Complications And Outcomes In Patients With Coronavirus Disease 2019

Introduction: Coronavirus disease 2019 (COVID-19) causes abnormalities in the hemostatic system, which are referred to as COVIDassociated coagulopathy. Whole blood viscoelastic studies, such as thromboelastography (TEG), are the best way to determine the dynamics of clot formation. The hypercoagulable state seen in COVID-19 patients is aggravated by a severe state of ''fibrinolysis shutdown,'' which is caused by overexpression of plasminogen activator inhibitor 1 and thrombin activated fibrinolysis inhibitor. Aims & Objectives: We aimed to study the coagulation pattern in COVID-19 patients on TEG and explored the predictors of outcomes in our patients. Material(s) and Method(s): An automated thromboelastogram was used to perform TEG on 28 COVID-19 patients. Based on TEG parameters such as R-time, K-time, alpha angle, maximum amplitude (MA), and clotting index (CI), the hemostatic state was classified as hypercoagulable in 17 (63 percent), hypocoagulable in 2 (7 percent), or normal in 8 (30 percent) patients. Result(s): Twenty-seven patients were included, with a median age of 50 years (IQR 40-60 years) and a male to female ratio of 0.9:1. COVID-19 was mild in 6 (22.2%), moderate in 2 (7.4%), and severe in 19 (70.4%) patients. Fibrinolytic shutdown was detected in 4 (15%) patients. TEG analysis revealed that they were hypercoagulable. All four patients showed high D-dimer levels and a LY-30 of 0%, as well as decreased K-time and increased alpha angle or MA. Conclusion(s): Despite thromboprophylaxis, hypercoagulable states and severe inflammatory states are prevalent in COVID-19 patients. Patients with hypocoagulable and hypercoagulable conditions had greater 28-day mortality than those with normal coagulation. (Logrank test, P = 0.002). Only two of the four patients who had their fibrinolytic systems turned down survived. These patients had no clinically evident thrombosis.

Data adequacy bias impact in a data-blinded semi-supervised GAN for privacy-Aware COVID-19 chest X-ray classification

Supervised machine learning models are, by definition, data-sighted, requiring to view all or most parts of the training dataset which are labeled. This paradigm presents two bottlenecks which are intertwined: risk of exposing sensitive data samples to the third-party site with machine learning engineers, and time-consuming, laborious, bias-prone nature of data annotations by the personnel at the data source site. In this paper we studied learning impact of data adequacy as bias source in a data-blinded semi-supervised learning model for covid chest X-ray classification. Data-blindedness was put in action on a semi-supervised generative adversarial network to generate synthetic data based only on a few labeled data samples and concurrently learn to classify targets. We designed and developed a data-blind COVID-19 patient classifier that classifies whether an individual is suffering from COVID-19 or other type of illness with the ultimate goal of producing a system to assist in labeling large datasets. However, the availability of the labels in the training data had an impact in the model performance, and when a new disease spreads, as it was COVID9-19 in 2019, access to labeled data may be limited. Here, we studied how bias in the labeled sample distribution per class impacted in classification performance for three models: A Convolution Neural Network based classifier (CNN), a semi-supervised GAN using the source data (SGAN), and finally our proposed data-blinded semi-supervised GAN (BSGAN). Data-blind prevents machine learning engineers from directly accessing the source data during training, thereby ensuring data confidentiality. This was achieved by using synthetic data samples, generated by a separate generative model which were then used to train the proposed model. Our model achieved comparable performance, with the trade-off between a privacy-Aware model and a traditionally-learnt model of 0.05 AUC-score, and it maintained stable, following the same learning performance as the data distribution was changed. © 2022 Owner/Author.

Dead Bodies - A Potential Source of Infection for COVID -19

The recent Covid-19 pandemic has raised a lot of questions regarding the mode of transmission of the virus. The rapid spread across the globe has compelled researchers to focus on this issue. Theories claiming droplet transmission, fbmites as well as airborne transmission have cropped up. The primary concern for the autopsy surgeons is whether the dead bodies harbor the virus and if so for how long. The present study was undertaken to find out the possibility of the virus being isolated from the human cadavers by testing at specified intervals after death. Out of the 74 cases examined, 59.5% of cases tested positive 1 day after death and 20.5% were still positive 5 days after death. The diflerence between males and females was not significant. The age of the subjects in our study ranged from 20 days to 90 years. The results of the study clearly indicate that the virus persists in the human cadavers for a sufficient period of time to act as a potential source of infection. Adequate precautionary measures while packing the body and autopsy examination are of utmost essential to prevent the spread of the disease among the dead body handlers and the family members while performing the last rites © 2022. Journal of Indian Academy of Forensic Medicine.All Rights Reserved.

Elucidating the role of digital technologies in bridling the ramifications of COVID-19 in restaurant services: moderation of pandemic susceptibility and severity

Purpose The purpose of this study is to augment the perceived service quality (PSQ) dimensions as well as evaluate the effects of pandemic susceptibility and severity by appending crucial enablers of customer satisfaction (CS) in the restaurant industry (RI). Design/methodology/approach The top 10 restaurants from Mumbai and Kolkata were selected based on the Conde Nast Traveller Magazine List, 2020. The study used a cross-sectional design to collect responses from 840 respondents across the two major metropolitans of India after the second wave of COVID-19 by employing a structured questionnaire. The proffered hypotheses in this study were validated using factor analysis and structural equation modelling (SEM) techniques. Findings This research espies pivotal facilitators of CS and customers' perceived value (CPV). The results divulge that food quality (FQ) and tangibility dimensions markedly enhance CS while the FQ and digital technologies (DT) dimensions augment CPV in Indian restaurants. The study asserts that CPV acts as a partial mediator between FQ and DT on the one hand and CS on the other. In addition, perceived pandemic susceptibility (PPSU) and perceived pandemic severity (PPSE) moderate the association between CPV and CS in restaurants. Research limitations/implications This study exemplifies the critical enablers of CS and CPV that may invigorate restaurant owners, managers and policymakers to prioritize the identified dimensions to aggrandize CS and CPV quotients. Originality/value The study enriches the literature by assimilating DT and CPV dimensions in a comprehensive theoretical framework. The research is unique in attempting to unfurl the moderating effects of PPSU and PPSE in the RI.

Public perception of media’s role during Covid-19 pandemic in Bangladesh

This article represents an example of a non-Western study into the public perception of the mass media’s role during the coronavirus pandemic in Bangladesh, which is of particular importance given the global environment of a high level of informational uncertainty and health risk that is equally applicable to countries around the world. Quantitative research methodology was used to gather perceptions of citizens across the country on the role and performance of the mass media’s coverage of the coronavirus pandemic. The responses gathered demonstrated that the pandemic generated an increased demand for news and information on the virus, which was used as a means of attempting to reduce personal risk and harm. In this time of an increased demand for information, respondents tended to perceive the information that they received from mainstream media news sources as being credible and rated media performance positively. This final observation is seemingly bucking the general global trend of decreased public trust in news media sources. © 2022, Lomonosov Moscow State University, Faculty of Journalism. All rights reserved.

The Impact of COVID-19 on the Volatility of Bangladeshi Stock Market: Evidence from GJR-GARCH Model

The enormous sway of COVID-19 on the international financial market has been felt across the globe. The financial markets of Bangladesh have also been similarly affected by the global epidemic and experienced a significant increase in volatility. To scrutinise the connection between COVID-19 and the Dhaka Stock Exchange (DSE) indices' return and instability, this study uses data of the DSE from February 2014 to September 2021. A comparative examination of the return and instability of the stock indices of the DSE has also been done considering the outbreak of the current COVID-19 situation. After using the GJR-GARCH (1,1) model, this review uncovers that the outbreak of COVID-19 has a statistically positive noteworthy association with the DSE stock indices' instability, which increases the market's volatility. Traders' fear and the rising frequency of COVID-19 reported patients could cause this. Besides, according to this study, COVID-19 shows a substantial positive linkage with stock market returns that increases the market's return. An appealing valuation, lower interest rates in the banking channel, economic rebound following the closure to prevent coronavirus transmission, improved remittance inflows, and a return of export revenues could all have contributed to this outcome. In addition, the findings also reveal that all market indices are in a mean-reverting phase.

Supply Chain Management with Demand Forecasting of Covid-19 Vaccine using Blockchain and Machine Learning

Vaccination of the global population against COVID-19 is one of the challenging tasks in supply chain management that humanity has ever faced. The rapid roll-out of the COVID-19 vaccine is a must for making the worldwide immunization campaign successful, but its effectiveness depends on the availability of an operational and transparent distribution chain that can be audited by all related stakeholders. In this paper, the necessity of Blockchain and Machine Learning in supply-chain management with demand forecasting of the COVID-19 vaccine has been presented. The aim is to understand how the convergence of Blockchain technology and ML monitor the prerequisite of vaccine distribution with demand forecasting. Here, we have proposed an approach consists of Blockchain and Machine Learning which will be used to ensure the seamless COVID-19 vaccine distribution with transparency, data integrity, and end-to-end traceability for reducing risk, assuring the safety, and also immutability. Besides this, we have performed demand forecasting for appropriate COVID-19 vaccines according to the geographical area and the storage facilities. Lastly, we have discussed research challenges and also mentioning the limitations with future directions. © 2021 IEEE.

Students' Adaptability Level Prediction in Online Education using Machine Learning Approaches

Online Education has become a buzzword since the COVID-19 hit the World. Most of the educational institutions went online to continue educational activities while developing countries like Bangladesh took a significant period of time to ensure online education at every education level. Students of several levels also faced many difficulties when they got introduced to online education. It is important for the decision-makers of educational institutions to be informed about the effectiveness of online education so that they can take further steps to make it more beneficial for the students. Our main motivation is to contribute to this matter by analyzing the relevant factors associated with online education. In this work, we have collected students' information of all three different levels(School, College, and University) by conducting both online and physical surveys. The surveys form consists of an individual's socio-demographic factors. To get an idea about the effectiveness of online education we have applied several machine learning algorithms named Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and also Artificial Neural Network(ANN) on our dataset to predict the adaptability level of the students to online education. Among used algorithms, the Random Forest classifier achieved the best accuracy of 89.63% and outperformed other algorithms. © 2021 IEEE.

Supervised Machine Learning Approach for the Prediction of COVID-19 Cases

COVID-19 was first discovered in the city of Wuhan. From then onward the virus has spread rapidly infecting thousands of people. The virus is still spreading and attempts are being made to predict and control the growth and spread of this virus. The trend of spread of this virus is highly unpredictable and normal statistical methods of predictions have not provided promising results, thus another approach of predicting the growth of this virus is required. This approach must be able to predict the nonlinear growth of the virus. Thus, an attempt is made to predict the growth of this virus and to show that the normal statistical methods are not able to predict the growth of the virus with high accuracy. The linear predicting algorithms used are Linear Regression, Support Vector Machine, Polynomial Regression and Auto Regressive Integrated Moving Average. The nonlinear predicting algorithm used is Prophet Algorithm for the prediction of exponential growth of spread of the virus. A comprehensive study is done to show how the spread of the virus takes place in different countries. A comparative study is also done to show the differences in performance parameters based on Absolute Mean Error, Mean Squared Error and R-squared (R2) score among different types of predictors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

To Study the Effect of Covishield Vaccinationon Platelet Counts of Plateletpheresis Donors-A Pilot Study

Introduction: About 20% of the Indian population has been vaccinated till September, since its first roll out on 16 January 2021.Majority of Indian population was vaccinated through ChAdOx1nCoV-19 Corona Virus Vaccine (Recombinant), commonly known asCovishield. Although the vaccine is proven safe but occasional reports of Vaccine-induced thrombocytopenia and thrombosis raisedfew eyebrows regarding the safety. Apheresis-derived platelet concentrates are frequently required in all sorts of clinical illnesses andpost-vaccination decrement of platelet counts might lead to increaseddeferral of the plateletpheresis donors.Aims &Objectives: The study aims to find out the effect of Covishield vaccination on deferral rates of plateletpheresis donors. Theprimary objective is to compare deferral rates of vaccinated plateletdonors (cases) from non-vaccinated ones (control) and the secondaryobjective is to correlate vaccination with pre-donation platelet countsof the donors.Materials &Methods: A blood sample was collected from thepotential platelet donors after the standard questionnaire for a complete blood count. Data collected were tabulated in an MS Excelspreadsheet and was analyzed with SPSS v23, p-value ≥ 0.05 wastaken as significant. We compared this data with an equal number ofage and sex-matched platelet donors (controls) from the year 2019.Result: The mean age of cases and controls was 29.69 ± 8.57 and30.15 ± 7.11 respectively. There is a marked difference betweenplatelet counts of cases (188,496.35 ± 72,065.66/mm3) and controls(269,524.50 ± 53,981.60/mm3) with p-value of <0 > 3 than thosewho received 2 doses 179,970.83 ± 66,773.73/mm3. The differencein deferral rates was remarkable between the two groups 34.7% vs0.9% with p-value <0.001. Weak positive correlation is establishedbetween days after 2nd dose and platelet count (rho = 0.2, p = 0.002).Conclusions: Vaccination undoubtedly increased the deferral rates ofplateletpheresis donors due to low platelet counts. Average plateletcounts were low in fully vaccinated individuals, however, plateletreturned to normal counts as the days progress post-vaccination.

Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection

In this time of COVID-19 crisis, the threat posed by the propagation of misinformation leading to mistrust needs to be kept in check. Misinformation related to the vaccines, remedies, false symptoms, etc. are spiraling out of control. We might not be able to directly put a stop to the flow or spread of fake news to a large extent at the moment, but it may be able to identify it as such with the help of Natural Language Processing (NLP) tools and Deep Learning (DL) algorithms. Steps involved in achieving this goal can be narrowed down into collection and analysis of data from various sources, sorting out the articles as covid-relevant and categorizing them as real or fake using DL models. However, DL models use different optimizers in the learning process, which plays an important role in identifying the fake news. This chapter also compares the efficiency of different optimizers in the context of COVID-19 fake news detection using DL models. The newly developed Continuous Coin Betting (CoCoB) Optimizer for DL studied extensively for fake news detection and performed compared with four other widely used optimizers. The comparative analysis shows the CoCoB as well as popularly used Adam optimizers are quite effective in finding optimal classification results for detection of fake news related to COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Reconnoitering enablers of travelers' online hotel booking intention: moderation of service affordability and perceived pandemic risk

Purpose The meddling of foreign players into the Indian hotel industry has triggered fervent competitiveness, and therefore, consumers' attitude, intention and behavior have been the epicenter of all activities. This study endeavors to explicate enablers of online hotel booking intention (OHBI) in the Indian hospitality industry. Design/methodology/approach The study examined OHBI of 560 travelers during the first wave of COVID-19 pandemic in India using structural equation modeling and an extended technology acceptance model. Direct and indirect associations were explored using mediation and moderation. Findings The results manifest that hotel website credibility, perceived website interactivity and perceived ease of use (PEU) aggrandize perceived usefulness (PU), which, in turn, considerably magnifies travelers' OHBI. PEU and PU partially mediate the relationship in the model. Into the bargain, service affordability reinforces the relationship, while perceived pandemic risk enfeebles the relationship between PU and OHBI. Research limitations/implications The study unfurls pressing determinants of PEU, PU and OHBI that may facilitate hoteliers to lure travelers and enhance profitability. Originality/value There is a paucity of literature on "hotel website credibility" and "perceived pandemic risk" in the hospitality industry. Hence, the study enriches literature by assimilating underlying constructs through an epigrammatic conceptual model. The study is distinctive because it unearths the possibilities of mediation and moderation amongst the aforementioned constructs and posits the calamitous effects of the COVID-19 pandemic on the tourism and hospitality sector.

Sensitivity Analysis of Physically Doped, Charge Plasma and Electrically Doped TFET Biosensors

TFET based label-free biosensors are fast, sensitive and more power efficient as compared to CMOS biosensors, which are prone to short channel effects (SCEs). However, literature is flooded with various TFET biosensors that have become the reason of dilemma for researchers during pandemic situations like COVID-19. Therefore, in this work, a physically doped (PD), charge plasma (CP) and electrically doped (ED) dielectric modulated (DM) TFET based label-free biosensors are compared, which cover almost the entire range of doping and junctionless devices. Also, we found that the ED based TFET biosensors provide better current sensitivities of 5.10 x 10(7), 4.77 x 10(8) and 7.11 x 10(8) for biomolecules with K=12, positive charge= 1 x 10(13) C/cm(2) and negative charge= -1 x 10(13) C/cm(2) respectively. Hence, ED-DM-TFET based biosensors can act as promising candidates to provide better detection and identification quality.

Suicidal deaths amid covid-19 pandemic: A cross-sectional autopsy-based study

Background: Suicide is a global issue, with an estimated 75.5% of the cases occurring in developing countries, and India alone accounting for 26.6% of all global suicidal deaths. With an advent of COVID-19 in the early months of 2020, India observed a rapid rise in suicidal deaths. Though, various media reports predicted loneliness, mental illness and economical instabilities as the major triggering factors, there is a lack of analytical or descriptive studies confirming this hypothesis. In this context, the present cross-sectional study was planned to determine the socio-demographic profiles of the victims and the triggering factors of the suicidal deaths during the COVID-19 phase, in context to the victims of suicide from 2017 to the Pre-COVID phase. Methods: The present cross-sectional study was conducted by analyzing the suicidal deaths from 2017 to 30th June, 2020, interviewing the deceased family members during the COVID-19 phase and studying the Inquest reports, with the documents from the Institutional Medical Record Section. Conclusion: The authors feel that suicide is an act of moment in mind, so any decision made under excitement or incitement is the real culprit. To curb the menace of suicide, state and society should ensure education, employment and socioeconomic well-being, along with strict law enforcement.

Smart Face Recognition with Mask/No Mask Detection

Ever since Covid-19 has affected our lives, the use of masks in public places has taken extreme importance. Be it restaurants, shopping malls, workplaces, airports or any other public place, wearing a mask is essential for the safety of oneself as well as the others. However, there are always some defaulters who will not wear a mask putting all at risk. In this paper, a mask/no mask detector has been implemented to identify such defaulters. Also, a face recognition system has been incorporated to identify those who are not wearing a mask. This setup could be used in offices or any other place of public gathering to ensure the safety of one and all. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.