Use of metolazone as an adjunct therapy to loop diuretics in diuretic resistant acute decompensation of heart failure: A systematic review and meta-analysis: Metolazone in diuretic resistant AHF: A Meta-Analysis

Introduction: Loop diuretics are the first-line treatment for volume overload in acute decompensation of congestive heart failure (AHF). Loop diuretic resistance is common due to pharmacologic tachyphylaxis. Therefore, thiazide and thiazide-like diuretics are often used as add-on therapy to combine two different pharmacologic mechanisms. This systemic review and meta-analysis aimed to synthesize the current evidence on the efficacy and safety of metolazone and other thiazide-like diuretics in AHF. Method(s): PRISMA guidelines were followed in conducting this systematic review. PubMed, Scopus, PubMed Central, and Embase databases were searched using relevant keywords for studies published before 5 Jan 2022. and title screening was performed, followed by full-text screening using the Covidence software. Data were extracted, and analysis was done using Cochrane Review Manager (RevMan v5.1). The results were reported in odds ratio and mean difference with 95% confidence intervals. Result(s): Out of 2999 studies identified by database search, eight studies met the inclusion criteria (2 RCTs and 6 cohort studies). Pooled analysis using a random-effects model showed no difference in mean difference among the metolazone group and control group for 24 hours total urine output (MD 69.32, 95% CI -638.29 to 776.94;n = 551;I2 = 84%), change in urine output in 24 hours (MD -284.09, 95% CI -583.99 to 15.81;n = 345;I2 = 0%), 48 hours total urine output (MD -465.62, 95% CI -1302.22 to 370.99;n = 242;I2 = 0%) and urine output at 72 hours (MD -13.24, 95% CI -90.88 to 64.40;n = 205;I2 = 0%). However, studies with furosemide only in the comparator arm, 24 hours of total urine outcome favored metolazone (MD 692.70, 95% CI 386.59 to 998.82;n = 334;I2 = 0%). There was no difference between the two groups in the rate of adverse events, loss of weight, mortality, or readmission rates. Conclusion(s): Metolazone therapy in diuretic resistant AHF may improves urine output and facilitates achieving a net negative balance. Thus, metolazone and thiazide-like diuretics can be used as add-on therapy in acute decompensation of heart failure, especially in diuretic resistance.Copyright © 2023 The Author(s)

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes' Response against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.

Priority Degrees and Distance Measures of Complex Hesitant Fuzzy Sets With Application to Multi-Criteria Decision Making

The notion of a complex hesitant fuzzy set (CHFS) is one of the better tools in order to deal with complex information. Since distance plays a crucial role in order to differentiate between two things or sets, in this paper, we first develop a priority degree for the comparison between complex hesitant fuzzy elements (HFEs). Then a variety of distance measures are developed, namely, Complex hesitant normalized Hamming-Hausdorff distance (CHNHHD), Complex hesitant normalized Euclidean-Hausdorff distance (CHNEHD), Generalized complex hesitant normalized Hausdorff distance (GCHNHD), Complex hesitant hybrid normalized Hamming distance (CHHNHD), Complex hesitant hybrid normalized Euclidean distance (CHHNED), Generalized complex hesitant hybrid normalized distance (GCHHND) and their weighted forms. Moreover, the continuous form of the proposed distances is also developed. Further, the proposed distances are applied to medical diagnosis problems for their effectiveness and application. Furthermore, a multi-criteria decision making (MCDM) approach is developed based on the TOPSIS method and proposed distances. Finally, a practical example related to the effectiveness of COVID-19 tests is presented for the application and validity of the proposed method. A comparison study was also done with the method that was already in place to see how well the new method worked.

The state of health in Pakistan and its provinces and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding health trends and estimating the burden of disease at the national and subnational levels helps policy makers track progress and identify disparities in overall health performance. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides comprehensive estimates for Pakistan. Comparison of health indicators since 1990 provides valuable insights about Pakistan's ability to strengthen its health-care system, reduce inequalities, improve female and child health outcomes, achieve universal health coverage, and meet the UN Sustainable Development Goals. We present estimates of the burden of disease, injuries, and risk factors for Pakistan provinces and territories from 1990 to 2019 based on GBD 2019 to improve health and health outcomes in the country. Methods We used methods and data inputs from GBD 2019 to estimate socio-demographic index, total fertility rate, cause-specific deaths, years of life lost, years lived with disability, disability-adjusted life-years, healthy life expectancy, and risk factors for 286 causes of death and 369 causes of non-fatal health loss in Pakistan and its four provinces and three territories from 1990 to 2019. To generate estimates for Pakistan at the national and subnational levels, we used 68 location-years of data to estimate Pakistan-specific demographic indicators, 316 location-years of data for Pakistan-specific causes of death, 579 location-years of data for Pakistan-specific non-fatal outcomes, 296 location-years of data for Pakistan-specific risk factors, and 3089 location-years of data for Pakistan-specific covariates. Findings Life expectancy for both sexes in Pakistan increased nationally from 61 center dot 1 (95% uncertainty interval [UI] 60 center dot 0-62 center dot 1) years in 1990 to 65 center dot 9 (63 center dot 8-67 center dot 8) years in 2019;however, these gains were not uniform across the provinces and federal territories. Pakistan saw a narrowing of the difference in healthy life expectancy between the sexes from 1990 to 2019, as health gains for women occurred at faster rates than for men. For women, life expectancy increased by 8 center dot 2% (95% UI 6middot3-13middot8) between 1990 and 2019, whereas the male life expectancy increased by 7 center dot 6% (3 center dot 5-11 center dot 8). Neonatal disorders, followed by ischaemic heart disease, stroke, diarrhoeal diseases, and lower respiratory infections were the leading causes of all-age premature mortality in 2019. Child and maternal malnutrition, air pollution, high systolic blood pressure, dietary risks, and tobacco consumption were the leading all-age risk factors for death and disability-adjusted life-years at the national level in 2019. Five non-communicable diseases-ischaemic heart disease, stroke, congenital defects, cirrhosis, and chronic kidney disease-were among the ten leading causes of years of life lost in Pakistan. Burden varied by socio-demographic index. Notably, Balochistan and Khyber Pakhtunkhwa had the lowest observed gains in life expectancy. Dietary iron deficiency was the leading cause of years lived with disability for both men and women in 1990 and 2019. Low birthweight and short gestation and particulate matter pollution were the leading contributors to overall disease burden in both 1990 and 2019 despite moderate improvements, with a 23 center dot 5% (95% UI 3 center dot 8-39 center dot 2) and 27 center dot 6% (14 center dot 3-38 center dot 6) reduction in age-standardised attributable DALY rates during the study period. Interpretation Our study shows that progress has been made on reducing Pakistan's disease burden since 1990, but geographical, age, and sex disparities persist. Equitable investment in the health system, as well as the prioritisation of high-impact policy interventions and programmes, are needed to save lives and improve health outcomes. Pakistan is facing several domestic and foreign challenges-the Taliban's return to power in Afghanistan, political turmoil, catastrophic flooding, the COVID-19 pandemic-that will shape the trajectory of the country's health and development. Pakistan must address the burden of infectious disease and curb rising rates of non-communicable diseases. Prioritising these three areas will enhance Pakistan's ability to achieve universal health coverage, meet its Sustainable Development Goals, and improve the overall health outcomes.

Pandemic Fatigue in Japan: Factors Affecting the Declining COVID-19 Preventive Measures

Pandemic fatigue has threatened the efforts to contain the coronavirus disease 2019 (COVID-19) worldwide;thus, government-mandated preventive measures have declined. The Japanese government has implemented several methods to address COVID-19′s spread, including hand hygiene, mask requirements, and social distancing. This study is the first to examine the socioeconomic factors affecting Japan's decline in COVID-19 prevention measures. It utilized the Preference Parameters Study of the Osaka University Institute of Social and Economic Research data of the 2021 and 2022 waves. With approximately 1580 observations, we detected a 10%, 4%, and 13% decline in hand hygiene practice, mask-wearing, and social distancing, respectively, between January 2021 and January 2022. Men were more likely to dislike the hand hygiene practice and mask-wearing and were also more reluctant to maintain social distancing. Moreover, financially satisfied individuals were positively associated with a decrease in the hand hygiene practice, while those with greater assets were more likely to dislike maintaining social distancing. People who exercised regularly were less likely to abandon the hand hygiene practices. Our results highlighted the significance of selective prevention programs targeting specific groups to promote compliance and lead to more effective pandemic management and less fatigue or discontentment. © 2023 by the authors.

Who Became Victims of Financial Frauds during the COVID-19 Pandemic in Japan?

The COVID-19 pandemic has provided a unique opportunity for fraudsters to innovatively swindle money through the trade of necessary goods and services. Although several incidents of financial fraud were reported during the pandemic, there is a lack of studies comparing financial frauds before and during the pandemic and the risk factors associated with frauds. This study uses two waves of a panel survey conducted before and during the pandemic and applies mean comparison tests and logit regressions to investigate financial frauds at the aggregate and specific levels. The comparative analysis shows no significant change in financial frauds at the aggregate level between before and during the pandemic. However, refund frauds for men have increased, while loan guarantee frauds for women have decreased significantly during the pandemic. The regression results show that being male, younger in age, living with family, having employment status, having a household income, household assets, having financial literacy, having a myopic view of the future, and having careful buying habits are associated with the probability of being victims of financial frauds during the pandemic. The study reveals differences in risk factors associated with victims of financial frauds at the aggregate and specific levels. The results further imply that risk factors differ across the types of fraud, which authorities should consider while combating financial frauds. © 2023 by the authors.

In silico analysis for such natural compounds and COVID-19

Coronavirus infection has become a common cause of sickness and death worldwide. Many drugs have been studied for the treatment of SARS-CoV-2 infections, and vaccines are injected to boost the immune system and safeguard people around the world. Many drug-like compounds are under clinical trials and have the potential to cure respiratory and viral diseases. Natural extracts and herbal products have been extensively used in traditional Chinese medicine and Indian Ayurveda. Natural medicines are more acceptable and are considered cheap and safe for COVID-19 treatment. This comprehensive chapter highlights in silico techniques for drug design and discovery using natural products against coronavirus infection. Especially computational studies of SARS-CoV-2 drugs have been explained. The effects of the mentioned natural metabolites repurposed for coronavirus diseases, especially for SARS-CoV-2, should be evaluated more by clinical investigation so that we may be able to develop potential drugs for most challenging respiratory diseases, especially SARS-CoV-2. © 2023 Elsevier Inc. All rights reserved.

Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks. © 2023 by the authors.

Priority Degrees and Distance Measures of Complex Hesitant Fuzzy Sets with Application to Multi-Criteria Decision Making

The notion of a complex hesitant fuzzy set (CHFS) is one of the better tools in order to deal with complex information. Since distance plays a crucial role in order to differentiate between two things or sets, in this paper, we first develop a priority degree for the comparison between complex hesitant fuzzy elements (HFEs). Then a variety of distance measures are developed, namely, Complex hesitant normalized Hamming-Hausdorff distance (CHNHHD), Complex hesitant normalized Euclidean-Hausdorff distance (CHNEHD), Generalized complex hesitant normalized Hausdorff distance (GCHNHD), Complex hesitant hybrid normalized Hamming distance (CHHNHD), Complex hesitant hybrid normalized Euclidean distance (CHHNED), Generalized complex hesitant hybrid normalized distance (GCHHND) and their weighted forms. Moreover, the continuous form of the proposed distances is also developed. Further, the proposed distances are applied to medical diagnosis problems for their effectiveness and application. Furthermore, a multi-criteria decision making (MCDM) approach has been developed based on the TOPSIS method. Finally, a practical example related to the effectiveness of COVID-19 tests is presented for the application and validity of the proposed method. A comparison study was also done with the method that was already in place to see how well the new method worked. Author

Using Fused Contourlet Transform and Neural Features to Spot COVID19 Infections in CT Scan Images

The World Health Organization (WHO) claims that COVID19 is the pandemic disease of the 22nd century. The COVID19 disease is caused by a strain of coronavirus that led to the infection and death of millions of people and continues to do so unless we find mechanisms that enable healthcare providers to detect infections accurately and as early as possible. To that end, and to diagnose this lung infection, where CT scan images are usually reliable tools that physicians typically use to spot infections. Like many other research studies in the computing field, we present here a new approach for automating the process of identifying COVID19 infections in CT scans using Machine Learning. This approach uses the hybrid fast fuzzy c-means for COVID19 CT scan image segmentation. Then, the Contourlet transform and CNN feature extracted approaches are used to extract features individually from segmented CT scan images and combine them in one feature vector. For feature selection, we experimented with three feature selection techniques, namely, Principle Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Binary Differential Evaluation (BDE), where we found the latter gave the best results. For classification, we used several neural network models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier worked better. An extensive set of experiments was conducted on standard public datasets. The results suggest that our methodology gives better performance than other existing approaches with an accuracy of 99.98%.

Effect of long-term exposure of digital devices during the COVID-19 pandemic and on ocular health

Background: The study aims to compare the effect of long-term exposure to digital devices during covid-19 and before the lockdown. Objective: To assess the impact of the lockdown on digital device usage & consequently, the ocular surface health implication related to digital eye strain. Materials and Methods: An open online survey was distributed to people via social media platforms (email, Facebook, Instagram, WhatsApp, Telegram, and so on). Result: Females participated more than males, with 58.3%. With 30.76%, the >50 age group was found to participate, and at 13-31 years of age, participation was found to be more with 60%. Most individuals use digital devices for education with 44.2%. Before the lockdown, the duration of digital device usage is not there between 4 to 6 hours, but during the lockdown, it has been increased by 35% due to working from home. 76.3% of participants feel restless due to prolonged use of digital devices. Conclusion: It was discovered that before the lockdown the duration of digital device usage is not there between 4 to 6 hrs but during the lockdown, due to working from home it has increased and headache as an asymptomatic symptom is noticed more during prolonged use of digital devices in lockdown. © 2022 Innovative Publication, All rights reserved.

A review of panic buying: the mediation of illusory truth effect and persistence due to herd mentality and confirmation bias

In the early stages of the COVID-19 pandemic, the public has been experiencing severe stress and feelings of anxiety. Social media in particular has been shown in the literature to be a major contributing medium for the widespread distribution of misinformation concerning COVID-19. The rapid dissemination of fake news concerning supply shortages of certain essential items has increased pandemic-related behaviours such as panic buying. Factors that have led to panic buying include: perceived threat of an event, perceived product scarcity, fear of the unknown, and coping methods to gain control. Although these factors are prevalent reasons that induce panic buying behaviours, they do not explain the mechanisms of perception formation. It is possible that heuristics (i.e. availability, and affect), which are reinforced by social media posts, aid in the development of the illusory truth effect. This psychological phenomenon may be the root cause of the public’s false perceptions of pandemic-related events. This paper reviews the impact of the illusory truth effect as a mediator in processing misinformation from social media and the news as truths that inevitably encourages panic buying behaviour. Furthermore, this paper examines the persistence of the illusory truth effect due to herd mentality and confirmation bias in the perpetual cycle of irrational decision making. In conclusion, the illusory truth effect has been demonstrated to be a key cognitive bias that strengthens with repetitive exposure to adverse sentiments related to COVID-19, and is likely to be maintained through herd mentality and confirmation bias in social situations. Although more research must be conducted to solidify this theory, the current review aims to serve as a basis for further research on the illusory truth effect and potentiate solutions in the prevention of adherence to this effect. © 2022, University of Toronto. All rights reserved.

Comparison of Respiratory Complications of COVID-19 among Patients with Rheumatological Conditions Taking Biological Disease-Modifying Anti-Rheumatic Drugs and Conventional Synthetic Disease-Modifying Antirheumatic Drugs

Objective: To compare the respiratory complications of COVID-19 among patients with rheumatological conditions taking bDMARDs and csDMARDs at Pak Emirates Military Hospital Rawalpindi. Study Design: Comparative prospective study. Place and Duration of Study: Pak Emirates Military Hospital, Rawalpindi Pakistan from Mar to May 2020. Methodology: Patients diagnosed with COVID-19 on polymerase chain reaction having previously rheumatological conditions managed either with bDMARD or cs DMARD were included in the study. They were followed up for three weeks after the positive polymerase chain reaction. Complications leading to the use of oxygen or ICU support or death were compared in both groups of patients. Results: A total of 82 patients with any rheumatological condition managed either with bDMARD or csDMARD tested positive for covid-19 on polymerase chain reaction and were included in the final analysis. 30 (36.6%) patients were taking bDMARDs while 52 (63.4%) were taking csDMARD. In addition, 4 (4.8%) low dose oxygen therapy, 5 (6.1%) required moderate dose oxygen therapy, while 5 (6.1%) required severe dose oxygen therapy or intensive care unit support. 2 (2.4%) patients died within the three weeks. The requirement of moderate or severe dose oxygen and intensive care unit support was found statistically significantly more in the group taking csDMARDS. Conclusion: The presence of complications of COVID-19 and the requirement of oxygen and intensive care unit support were present in some of the patients taking DMARDs. Among the DMARDs, bDMARDs were less linked with complications, but large studies with better design required better results. © 2022, Army Medical College. All rights reserved.

Cyber-Physical Systems: AI and COVID-19

Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS). The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture. © 2022 Elsevier Inc. All rights reserved.

Comparing the Quality of Home and Clinic Applied Zio XT Patch in Pediatrics During the Pandemic

Background: The Zio® (Zio) XT Patch (iRhythm Technologies, Inc, San Francisco, California) is a 14-day continuous ambulatory ECG monitor. During the Covid-19 Pandemic, Zios were shipped directly to patients for self-application. The purpose of this study was to compare the quality of in-clinic (IC) to mail-home (MH) in our pediatric population. Methods: A single-center, IRB-approved study (1/1/18 - 6/1/21) of patients < 21 years of age with Zios were studied for wear and artifact time filtered out based on iRhythm's inherent algorithm. A control group of patients were age-matched from Zios placed IC and compared to MH Zios throughout the pandemic. Results: A total of 284 Zios were analyzed for total wear time and artifact filtered out. Of these, 149 were IC, and 135 were MH. Average percent of artifact of IC vs MH was 7.1% and 8.3% (p = 0.58). The average age of patients with Zios placed in clinic was older (12.84 years) than those placed at home (11.12 years, p<0.02). There was no significant difference in artifact when adjusted for age. Age was inversely associated with percent artifact with a 0.34% reduction in percent artifact for every additional year of age (p < 0.049). Location was not associated with percent artifact after controlling for age. By two proportion Z-test, there was no statistically significant difference between IC and MH Zios wasted (p = 0.66) or repeated (p = 0.96). Conclusion: In conclusion, IC and MH Zios did not demonstrate any significant difference in artifact time filtered out. This highlights the potential for home application during current pandemic and future telemedicine utilization.

Engineering Students' Readiness for Online Learning Amidst the COVID-19 Pandemic: Scale Validation and Lessons Learned from a Developing Country

The recent outbreak of the COVID-19 pandemic forced education institutes to shift to an internet-based online delivery mode. This unique situation accelerates a long-standing issue of digital inequality among the students in education and warrants a concentrated study to investigate students' readiness for learning in online environment. This study developed an instrument to meticulously measure the students' readiness for online learning in a pandemic situation. The proposed model consists of (a) motivation, (b) self-efficacy, and (c) situational factors. The proposed model was validated with the engineering students (for pilot study N = 68 and main study N = 988) from several universities in Bangladesh. To validate the underlying relationships between the latent constructs, an exploratory factor analysis (EFA) was performed followed by structural equation modelling (SEM) for the construct validity of the measurement model and to assess the model fit. The findings showed that besides motivation and self-efficacy, the situational factors describing the contextual dynamics emerging from the COVID-19 significantly influenced the student's online readiness. We argue that digital inequality is an important factor influencing student readiness for online learning.

Clinico-demographic profile of covid 19 mortality in a tertiary care centre of Madhya Pradesh

Present study was conducted in ABV Government Medical college, which was the only recognised covid hospital by Government of M.P., with tertiary care facilities in Vidisha district. This study is a record based cross sectional study done to determine various clinico-demographic profile and co-morbidities associated with mortality, among covid 19 patients who died after initiation of treatment in IPD. Mean age affected was 56.64 yrs with slight preponderance of males. Almost 64.22% patient who died were suffering from comorbidities in whom the common were hypertension (11%), diabetes mellitus (9.17%), coronary artery disease (11%), renal involvement (5.5%), obesity (4.58%) and respiratory involvement (8.25%) cases. Common signs and symptoms were fever (92%), cough and cold (90%), dyspnoea (84%), fatigue and myalgia (71%) cases. Oxygen saturation was below 80 mm of Hg in 23.8 % patients and mean duration of hospital stay hospital was 4.0 days. Respiratory support in the form of Bi-pap and C-pap was needed in 17.43% cases and endotracheal intubation was needed in 7.33% cases. Treatment of cardiogenic shock was given in 22.01% cases. © 2022

Substantial Phase Exploration for Intuiting Covid using form Expedient with Variance Sensor

This article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures. © 2022. by the authors. Licensee Agora University, Oradea, Romania.