Teaching quantitative and non-quantitative courses: Online and remote delivery during the COVID-19 pandemic

This paper presents the experiences and perspectives of two Yorkville University faculty members teaching quantitative and non-quantitative courses to BBA students remotely and online during the COVID-19 pandemic. The authors discuss new issues faced while teaching online during the crisis. Most universities have shifted their existing courses to the online remote mode of delivery without making any changes to the course design. This study examines teaching differences for quantitative and non-quantitative courses online with a view to make recommendations based on our teaching experiences for transitioning such courses to remote synchronous delivery online. This paper also explores new methods that have been applied during online teaching while conducting different assessments (e.g., quizzes and exams). The authors share their challenges and issues based on two specific courses - Statistics for Business and Introduction to Marketing, which are typical examples of quantitative and non-quantitative courses. The paper suggests teaching approaches and how to conduct assessments online for these types of courses. These recommendations invite further discussion and research into online teaching. © Springer Nature Switzerland AG 2021. All rights reserved.

PREDICTION OF MORTALITY USING CLUSTERS BASED ON CLINICAL AND LAB PARAMETERS: DATA FROM 2072 PATIENTS FROM INSPIRE COHORT

BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.

Estimation of Parameters and Stability Analysis of Coronavirus Pandemic

Despite ranking amongst the highest in medical systems in Africa and spending a substantial amount on health sector than other African nations, Algeria suffered a major blow in the first wave of the Covid-19 pandemic. Vaccine hesitancy also affected the country adversely in subsequent waves of the disease. This study estimates the number of Covid-19 cases for Algeria in January 2022 using two numerical methods Multi-step Differential Transform Method (MsDTM) and Repeated MsDTM. Stability analysis of the pandemic for the country has also been discussed in the paper.

Effectiveness of the chAdOx1 nCoV-19 vaccine in preventing COVID-19 infection

AIM: The study aimed to assess the effectiveness of the ChAdOx1 nCoV-19 vaccine in preventing laboratory-confirmed COVID-19. METHODS: It was a test-negative, case-control study conducted at Sharda Hospital, Greater Noida, India, between March 2021 and May 2021. An equal number of cases and controls were included in the study after taking proper informed consent. The individuals with positive reverse transcriptase-polymerase chain reaction test reports were taken as cases, whereas those with negative reports were included as controls. Data were analyzed and the groups were compared using multivariable logistic regression to calculate the odds ratio (OR), with adjustment for gender and presence or absence of comorbidities. The effectiveness of vaccine was calculated by the formula (1-adjusted OR) x100%. RESULTS: On analyzing the data from 560 case-control pairs, the vaccine effectiveness was calculated as 57.46% (95% confidence interval [CI]: 53.85-61.02) and 60.09% (95% CI: 56.32-63.77) for single dose and two doses, respectively. The effectiveness of complete and single-dose vaccination against the moderate-to-severe disease was calculated as 63.79% (95% CI: 58.58-68.77) and 56.19% (95% CI: 51.30-61.0), respectively. CONCLUSION: The ChAdOx1 nCoV-19 vaccine was found to be effective against COVID-19, with protection after two doses being a little more than that after a single dose. It also proved effective in protecting against the severe form of the disease.

Blockchain-based EHR System for Indian Healthcare Industry using Aadhar

In today's world, digital technologies are advancing at a rapid pace. Almost every industry has benefited from this ongoing change. In the health sector, the digitization of medical records was proposed decades ago. Whereas some developed countries have successfully adopted and implemented Electronic Health Record (EHR) systems. Developing countries like India still heavily rely on paper-based medical records. Although there are a number of systems for electronic medical record management, they have issues related to interoperability, timely access, and storage. Due to poor infrastructure and design, the current systems are not robust for communicating and tracking medical records. The need for a better EHR system was highly emphasized during the COVID-19 pandemic. The two major shortcomings of the existing system are a lack of interoperability, which causes delays in sharing the information, and a lack of standardization, due to which the data quality of the data that is shared suffers. To mitigate these issues, we need a nationwide EHR system. Another issue is the lack of a ubiquitous UPI (Unique Patient Identifier). In a country like India, the second most populated country in the world, Aadhar is the best option for UPI, which can be used for creating a national EHR system. In this paper, we have presented a framework for a standardized, interoperable, and unified EHR system based on blockchain technology with Aadhar as the UPI. Using blockchain as the base of this model provides numerous advantages over a cloud-based system, like decentralization, better security, immutability, and traceability. © 2023 IEEE.

A Novel Emr-Based Care Pathway for Nephrolithiasis: Development and 1 Year Utilization

INTRODUCTION AND OBJECTIVE: Patients with acute renal colic due to stones frequently visit the ED. With limited ED resources due to the COVID-19 pandemic, we developed a best practice management pathway within our electronic medical records (EMR) to provide consistent, expeditious and appropriate care for patients with nephrolithiasis. The objective of this study is to describe the development and 1 year outcomes of our EMR Care Pathway for nephrolithiasis. METHOD(S): Our hospital system is composed of many centers. To standardize best practice care, we convened a clinical consensus group, with key stakeholders in emergency medicine, urology, interventional and diagnostic radiology to develop a pathway for the initial work up and management of acute renal colic. AUA guidelines, current literature, and expert consensus across specialties were used to develop the pathway to guide work up and management. Risk assessment tools, and criteria for specific imaging modalities, lab work, and pain protocols were outlined. Criteria for routine discharge with follow-up, including pre-populated links for referrals, indications for urology consult, hospital admission and urgent decompression (stent versus nephrostomy tube) were provided. Data was gathered through the EMR analytics team and descriptive statistics were performed. RESULT(S): The Care Pathway was utilized 944 times from August 3, 2021-September 17, 2022 at 11 different hospitals or care centers (Table 1). Usage increased overtime (r2=0.77). The majority of usage was in the ED (892, 94.4%). A total of 194 providers utilized the Pathway with the majority being residents (64, 33.0%). The pathway included care of 505 unique patients, with 106 primary diagnosis key words triggering pathway use. 139 Urology referrals were placed through the pathway with 124 new 28 day prescriptions of tamsulosin. CONCLUSION(S): An EMR-integrated care pathway has been readily utilized in our system and may augment triage and best practice management of patients presenting with stone disease. Further studies are needed to understand the full impact on outcomes.

A Comprehensive Review on Access Control Systems amid Global Pandemic

In the contemporary time of technology, security is the utmost concern for every building automation system. Access Control Systems are the backbone of any security system being employed in any intelligent building, and can be operated in a biometric or non-biometric manner. There are various types of recognition systems available, depending upon the required level of safety and security. The ongoing pandemic has challenged and tested Access Control System in many aspects.This paper aims to review the various forms of access control systems and their viability in the context of COVID-19. It is found that some access control solutions fail to provide the required security during this global epidemic due to their contact-based operations. So, in the midst of the worldwide pandemic, a realistic integrated electronic access control system can be designed to meet the requirements of users. © 2022 IEEE.

Analysis and Prediction of Covid-19 Spread Using Numerical Method

In this paper, we have discussed the impact of Coronavirus variants in a phase of 2021-22 along with a previous phase of 2020-21 in Italy. We analyse and compare the Covid-19 scenario in Italy for the period from October 04, 2020 to January 16, 2021 with a period from October 04, 2021 to January 16, 2022. For this study, we have used repeated multi-step differential transform method (RMsDTM). Also, we have predicted the number of active cases for 10 days following the period of study.

Covid-19 and Diabetes Risk Prediction for Diabetic Patient using Advance Machine Learning Techniques and Fuzzy Inference System

Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.

Face Mask Detection Using YOLOv3

The COVID-19 is an unprecedented crisis that has resulted in several security issues and large number of casualties. People frequently use masks to protect themselves against the transmission of coronavirus. In view of the fact that specific aspects of the face are obscured, facial identification becomes extremely difficult. During the ongoing coronavirus pandemic, researchers' primary focus has been to come up with suggestions for dealing with the problem through rapid and efficient solutions, as mask detection is required in the current scenario, whether in public or in some institutions such as offices and other workplaces. Only detecting whether a person wears mask or not is not enough. There is another aspect of wearing the mask properly such that it covers all the required portion of the face to ensure there is no exposure to any viruses. To address this, we proposed a reliable technique based on image classification and object localization, which can be accomplished using YOLO v3's object detection in machine learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detection Using Convolution Neural Network

Face recognition in the industry now is playing an important role in each sector. Each person has different type of features and face;therefore, each identity is unidentical. In this COVID outbreak, a major crisis has occurred due to which preventions are to be made. One such prevention is use of a face mask which is very much important. Nowadays, various firms and organizations are using facial recognition systems for their own general purpose. We all know that it has now been a crucial task to wear a mask every time, when we go somewhere. But as we know it is not possible to keep track of who wears a mask and who does not. We make the use of AI in our daily life. We achieve this with the help of a neural network system, which we train so that it can further describe people's features. Even though the original dataset was limited, the Convolutional Neural Network (CNN) model achieved exceptional accuracy utilizing the deep learning technique. With the use of a face mask detection dataset that contains both with and without face mask photographs, we are able to recognize faces in real-time from a live webcam stream using OpenCV. We will develop a COVID-19 face mask detection system using our dataset, along with Python, OpenCV, Tensor Flow, and Keras. © 2022 IEEE.

Glial fibrillary acidic protein astrocytopathy in a patient with recent mRNA SARS-CoV-2 vaccination: COVERSHEET

Background: Literature describing triggers of GFAP astrocytopathy (GFAP-A) is limited. We report a case of GFAP-A in a patient with recent messenger ribonucleic acid (mRNA) severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) vaccination and discuss the possible pathogenesis. Case description: A 45-year-old gentleman presented with features of meningoencephalitis 31 days after the first dose and 4 days after the second dose of mRNA SARS-CoV-2 vaccination. He sequentially developed brainstem/cerebellar, autonomic and cord dysfunction. Cerebrospinal fluid was positive for GFAP autoantibody. Clinical improvement occurred after intravenous methylprednisolone and immunoglobulins. Conclusion(s): Although we are uncertain of a causal link of GFAP-A to mRNA vaccine, indirect activation of an underlying dysregulated immune milieu is plausible.Copyright © 2021 The Author(s)

Yoga Recommendation System for the Mental Well-Being of Students using Machine Learning

Many expectations placed on students by society have made stress a part of their academic lives. Youth are susceptible to the issues brought on by academic stress since they are going through a phase of transitions in both aspects i.e personal and social. Academic stress has been shown to lower academic achievement and lower motivation toward academics. Therefore, it becomes crucial to develop appropriate and effective intervention options. In recent times, due to COVID, the utilization of online health blogs and sites recommending health, exercise, and yoga has been significantly increased. The blog will provide solution to a problem and then provide precautions to common people but they lack the dynamics to suggest yoga that can be done any person or a personalized yoga by considering their health condition and not a static article. This research work intends to develop an AI model to predict the possible practices a student can do to alleviate their problem by considering their BPM, blood pressure (both systole and diastole), sleep time and some questions related to stress. The proposed stress prediction model has achieved an accuracy of 94.4% and the yoga pose recommendation system has achieved an accuracy of 97.3%. © 2022 IEEE.

Impact of Metaheuristic Techniques in Pandemic of COVID-19

The early detection and diagnosis of novel coronavirus disease 2019 (COVID-19) are required to cure the disease. Metaheuristic techniques can be used to develop an automated tool for detecting the symptoms of an infected person and provide appropriate precautionary measures. The metaheuristic-based software can be designed to analyze the radiographic patterns of infected individuals and determine the severity of COVID-19 infection. The genome structure of coronavirus can be easily understood through metaheuristic techniques. Based on the genome structure, an ef-fective drug combination can be explored by using metaheuristics for the treatment of COVID-19.Copyright © 2021 Bentham Science Publishers.

Serious Adverse Events and Fatal Outcomes Following COVID-19 Vaccination in the UK: Lessons for Other Countries

Vaccines have taken the centre stage in the fight against COVID-19 pandemic, and in reducing hospitalisation and associated mortality. Countries around the world are heavily dependent on the successful rollout of their vaccination programmes to open up the societies and re-start their economies. However, the success of any vaccine programme, to a large extent, depends upon the efficacy and safety of the vaccines. Given that UK has been way ahead in vaccinating its population, is considered a successful model compared to other countries in Europe and elsewhere and has a yellow card reporting system for adverse events, we use UK as an example to understand the side effects and fatal outcomes following vaccinations. Our results show that AstraZeneca seems to be underperforming in terms of overall reporting of minor adverse events, serious incidents and fatal outcomes following vaccination. The risk of serious anaphylactic reaction and fatal outcome was 1.36 and 1.17 times more in case of AstraZeneca vaccine when compared with Pfizer BioNTech vaccine. The analysis has implications for vaccine policies and programmes both at nation-state and global levels.

Blood, nasal and airway immune responses to SARS-COV2 vaccination in COPD patients and healthy subjects

Background: Immune response to vaccination differs between individuals. We compared SARS-COV-2 vaccine specific immune responses in COPD patients versus healthy controls (HC) following vaccination. Systemic, nasal and sputum samples were used to examine different anatomical locations. Method(s): Blood, plasma, nasal and sputum samples were collected from COPD patients (n=11) and HC (n=16) at least 3 weeks post their 2nd SARS-COV-2 vaccination. Spike-specific immunoglobulin (Ig) A and G levels in plasma, nasal and sputum samples were measured by ELISA, while cellular immunity in blood was assessed by measuring spike-protein induced IFNgamma. All subjects had no history of SARS-COV-2 infection. Immune response levels were compared to samples from unvaccinated subjects. Result(s): Anti-spike IgG and IgA levels were increased in plasma from vaccinated individuals, as was cellular immunity. IgG, but not IgA, was increased in nasal (IgG: 0.8 Vs 9.1 ng/ml p=0.02;IgA: 11.6 Vs 11.6 p=0.5) and sputum (IgG: 0.9 Vs 11.0 ng/ml p<0.01;IgA: 31.7 Vs 27.0 p=0.12) samples from vaccinated individuals. Levels of immune responses to vaccination were similar in both COPD patients and HC (Table 1). Plasma IgG levels correlated with nasal (Rho: 0.86 p<0.001) and sputum (Rho: 0.78 p<0.001) levels. Conclusion(s): Vaccination induced immune responses in the lungs, as well as blood and nose, equally in both COPD patients and healthy subjects. (Table Presented).

Remdesivir, A Potential Drug for COVID-19 Treatment: A New Hope

Objective: Coronavirus Disease-2019 (COVID-19) is a pandemic outbreak in the world and is the leading cause of Severe Acute Respiratory Syndrome (SARS). Method(s): Currently, many drugs/therapies have been tested for COVID-19, which responded sub--optimally to the patients. Remdesivir is an RNA polymerase inhibitor that found promising results in ongoing clinical trials and shows a faster recovery rate in COVID-19 patients. Currently, USF-DA approves for emergent use of this drug in severe COVID-19 patients. Result(s): In this review, we discussed a brief overview of biopharmaceutical and pharmacological aspects of Remdesivir. Moreover, the ongoing regulatory status of Remdesivir by official bodies has also been described.Copyright © 2021 Bentham Science Publishers.

Novel Coronavirus in India and Outbreaks Around World: A Statistical Analysis of Odisha, India and the World

Background: The Public Health Emergency of International Concern by the World Health Organization (WHO) declared novel Coronavirus (nCoV-2019) outbreaks in 2019 as pandemic. Method(s): This research work made an analysis of the nCoV-2019 outbreak in India solely based on a mathematical model. Result(s): The historical epidemics in the world are plague, AIDS, Swine flu, ebola, zika virus, Black Death and SARS. Considering the model used for SARS 2003, the present research on COVID-2019 estimates characteristics of the rate of infections (I) and rate of recovery(R), which leads to the estima-tion of the I and R leading to predict the number of infections and recovery. Through ruling out the un-predictable and unreasonable data, the model predicts that the number of the cumulative 2019-nCoV cases may reach from 3398458 (mid of May) to 15165863, with a peak of the unrecovered infection (2461434-15165863) occurring in late April to late July. In this paper, we predicate how the confirmed infected cases would rapidly decrease until late March to July in India. We also focus on how the Gov-ernment of Odisha (a state of India) creates history in the protective measures of COVID-19. Conclusion(s): The growing infected cases may get reduced by 70-79% by strong anti-epidemic measures. The enforcement of shutdown, lockdown, awareness, and improvement of medical and health care could also lead to about one-half transmission decrease and constructively abridge the duration of the 2019 n-CoV.Copyright © 2021 Bentham Science Publishers.

Using Deep Transfer Learning to Predict Student Engagement in Online Courses

The use of online courses is growing worldwide that has opened the door for the interested students to learn comfortably from their locations especially, during Covid-19 pandemic. However, an important aspect of traditional classroom is real-time students' feedback for content delivery and interactive sessions, which is missing in online courses. The aim of this work is to bridge this gap by providing an automatic recognition system for engagement level of the students during online courses using deep transfer learning. In this paper, a CNN based method is proposed to predict the level of engagement while watching online class sessions. The CNN based method consists of two different modalities including: (1) pre-trained network based transfer learning for feature extraction from image data, (2) support vector machine (SVM) classifier for classification. Ten different pre-trained networks are used in the proposed method. The superiority of the method is evaluated on the dataset created using images of graduate students. Of all pre-trained networks, Resnet50 and VGG16 achieved highest classification accuracy of 72.34% and 71.77% using the proposed approach respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.