SARS-CoV-2 Infection after Vaccination: A Comparative Profile between Dose 1 and Dose 2.

Since the initial shipment of vaccination campaign against SARS-CoV-2 infection, it was a major concern all over the world regarding appropriate gapping between the first and second dose and also the necessity of booster dose after being vaccinated with the second dose. This cross-sectional type of comparative study was conducted at Kuwait Bangladesh Friendship Government Hospital, from the period of March 01 2021 to August 31 2021, on 148 hospitalized patients who were vaccinated with Astra Zeneca. They were divided into two groups on the background of 1st dose and 2nd dose. Collected data were entered into SPSS-26 version and after data cleaning, descriptive analysis was done with frequency distribution. To find out the significant difference between the two groups considering clinico-demographic information, disease severity, and duration of the last dose of vaccine; the Pearson Chi-square test was done with a significance level ≤0.05. The patients from both groups were mostly male and above 60 years. There were no significant age or sex variations between the two groups. SARS-CoV-2 infection was common after 38 days of dose 1 and after 63 days of dose 2. Fever, cough, running nose, shortness of breath, fatigue, nausea, vomiting, lower oxygen saturation, radiological involvement were comparatively more in patients who got only a single dose. Mild pneumonia (70.7%) was the commonest presentation in both doses of vaccinated patients and single dose vaccinated patients mostly (45.5%) presented with severe pneumonia. Elderly clinically risks group patients were mostly hospitalized with infection after 1 month of the 1st dose and on the other hand after 2 months of completing the 2nd dose. Symptomatic infection and disease severity were more in 1st dose vaccine recipients in comparison to 2nd dose.

An Approach for Collaboration Between Different Stakeholders to Strengthen the Public Health System

Nowadays, the healthcare problem is one of the major crises in many parts of the world, especially the COVID-19 pandemic has exacerbated this to a greater extent. Many developing countries with inadequate healthcare systems are suffering greatly from this crisis to provide proper medical services. The reasons are the insufficient number of healthcare providers, costs of medical tests and equipment, lack of accessible points of care and data analysis, and lack of sufficient online healthcare facilities. However, research on the benefits of establishing e-health platforms to strengthen the conventional public-health system is limited—most of the research targets patients in specific disease groups. This paper focuses on an approach for designing a healthcare social media platform for services provisioning, consuming, enabling patients to find an alternate source of healthcare advice, and then building a collaborative health community for all kinds of people. Its usability and applicability have been experimented with as a prototype on Android-based smartphone devices. The results show six features and benefits that are distinct from existing approaches in the literature. In addition, the approach will be considered an affordable alternative to conventional healthcare in case of emergency treatment. © 2022 Slovene Society Informatika. All rights reserved.

The Impact of COVID-19 on The Teaching of Non-Medical Healthcare Professionals in Bangladesh: Observations and Implications From A Pilot Study

Objective: At the start of the Covid-19 pandemic, educational establishments, including universities, were closed. Educators in high-income countries quickly shifted all education online, building on available infrastructures and approaches. However, there were concerns in developing countries regarding the necessary skills among students and faculty as well as financial support for equipment and the internet. Consequently, a pilot was undertaken in Bangladesh to determine the impact of Covid-19 on the non-medical education system, building on similar research with healthcare professionals. Material(s) and Method(s): A purposively designed questionnaire was disseminated among eight non-medical healthcare educators in private and public universities in Bangladesh. Results and Discussion: Private university educators reported their universities readily adopted e-learning systems and resumed classes more quickly than public universities. Both private and public university educators shared similar challenges, including a lack of training on e-learning initially, variable internet connections, affordability of internet bundles, concerns with available devices, as well as mental stress of faculty and students. Private universities reduced their tuition fees, extended submission deadlines, and shared class recordings to address challenges. Public universities arranged student loans, established Covid-19 testing centers, and the trained students in biosafety practices and molecular tests to volunteer in testing facilities. Conclusion(s): Lessons learned from the pandemic emphasize introducing hybrid education systems with full technological and financial support, alongside biosafety education in the curriculum. Copyright © 2022, Ibn Sina Trust. All rights reserved.

CoVID-19 Vaccine Efficacy in Heart Transplant Recipients, a Single Center Experience

Purpose: Transplant recipients have worse CoVID-19 survival compared to the general population, and thus are recommended to be vaccinated and boosted. Determinants of vaccination efficacy have not been well studied in heart transplant patients. Method(s): This was a prospective study of heart transplant recipients vaccinated against SARS-CoV-2 with one of the 2-dose mRNA vaccine series. Antibodies (Ab) were quantified by anti-Spike ELISA pre- and post-vaccination. Clinical data was extracted from electronic medical records. Differences in Ab detection and timing of Abs were assessed for statistical significance using Fisher's exact tests for categorical variables and t-tests for continuous variables. Result(s): Of the total 54 participants recruited from Jan 2021 to present, 6 patients were Ab positive prior to vaccination and 11 did not provide a pre-vaccination sample but were Ab positive post-vaccination. Almost half of participants (48%, n=26) received a booster 3rd dose. The mean age at vaccination was 58 +/- 11, 20% (n=11) were female, 70% (n=38) were Caucasian, and median time since transplant was 4 years (interquartile range: 2-11 years). Of the 37 Ab-negative individuals enrolled, none developed detectable Abs after a single vaccine dose and only 9 (24%) developed Abs after the 2nd vaccine dose. Interestingly in this small group, 6/9 (66%) of participants had delayed seroconversion of approximately 3 months. Unexpectedly, hyperlipidemia was positively associated with a detectable antibody response (p = 0.05) after the 2nd dose. There was also a trend toward higher age (p=0.06) and BMI (p=0.08) being associated with lack of response to the 2-dose series. Importantly, of the 28 patients who had no Ab response to the 2-dose vaccination regimen, 11/17 (65%) became Ab positive after the booster, increasing the vaccine response rate in this pre-vaccine Ab-negative group to 20/37 (54%). Looking at the total cohort of 54 participants, the overall positivity rate regardless of antigen sources (i.e., including those previously infected or for whom no pre-vaccine sample is available) is 68.5% (37/54) with a trend suggesting that absence of Ab response may be associated with prednisone treatment (p=0.06). Conclusion(s): Heart transplant recipients exhibit a low response rate to the initial 2-dose mRNA vaccines (24%), but the 3rd dose induces a response in the majority of those who failed to respond to the 2-dose series increasing overall response to over 50%. Similar to observations in the general public, non-response tended to be associated with older age and higher BMI. However, further/larger studies are needed to identify key determinants of vaccine efficacy in this population to guide management.

Effect of Covid-19 Vaccination on Markers of Rejection in Heart Transplant Recipients

Purpose: Heart transplant recipients have worse survival with COVID-19 than the general population, highlighting the importance of vaccination in these patients. The impact of vaccination on rejection in transplant recipients is not well studied. This study examines the association of vaccination for COVID-19 with changes in markers for and evidence of transplant rejection. Method(s): A retrospective analysis of heart transplant recipients vaccinated for COVID-19 was conducted at a major tertiary care center in the American Midwest. Serial antibody responses were drawn after vaccination to assess vaccine response. Data from routine transplant surveillance was extracted from the electronic medical record. Markers of rejection included Allomap, Allosure, donor specific antibodies (DSA), and endomyocardial biopsies. Only patients with comparative data within 365 days of their first COVID-19 vaccination were included for analysis. Numerical data was assessed using descriptive statistics. Categorical variables were analyzed using Fisher's exact tests. Result(s): Between January 1, 2021 and September 30, 2021, 51 heart transplant recipients had COVID-19 antibody levels checked post-vaccination. There was an increase in the mean Allomap (31.19 +/- 5.27 vs 32.14 +/- 4.30), Allosure (0.04 +/- 0.08 vs 0.11 +/- 0.12%), and biopsy C4d% (3.33 +/- 8.16 vs 10.00 +/- 12.65%) levels post-vaccination compared to those pre-vaccination. Both positive and negative responders to vaccination showed this numerical increase in markers of rejection. There was no change in biopsy grades for acute cellular rejection, nor any changes in class I DSA positivity. One patient who had a positive antibody response to the vaccine had a de novo class II DSA post vaccination. No patients in this cohort had an episode of treated rejection post vaccination. Conclusion(s): Heart transplant recipients receiving COVID-19 vaccination have numerical increases in markers of rejection like Allomap, Allosure, and biopsy C4d%. These subtle changes may suggest a difference in the immunologic environment but are of unclear significance. There was no change in biopsy proven ACR or treated episodes of rejection. Further studies are warranted to investigate the effect of COVID-19 vaccination on transplant rejection. (Table Presented).

Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture

The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96% sensitivity, and 96% F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.

Note: Learn Online: High School Students' Adoption of Online Learning in Bangladesh during COVID-19 Pandemic

Online learning is playing a significant role, especially during the COVID-19 pandemic. In this study, we perform an interview study through in-depth interviews with 22 high school students of a developing country (Bangladesh) to find out about their experience and practices with online learning during the pandemic. Our findings reveal several usage strategies, challenges of the conventional usage of online learning, workarounds students adopt to address those challenges. Through the adaptability lens, we find that many students are adapting to online learning despite being in favor of it. © 2022 ACM.

An Investigation into Crime Forecast Using Auto ARIMA and Stacked LSTM

The severity of criminal activities which cause both physical and psychological damage has been increasing at an alarming rate across the globe. Realizing the significance of this problem, law enforcement agencies have developed several strategies to prevent crimes. Being slow-paced and ineffective in most cases, these prevention strategies are not robust enough to contribute in predicting crime trends for an early prevention. In this paper, we propose a regression-based model that incorporates temporal, statistical relationships and other relevant information about the data to forecast crime trends. Since, seasonal information is a powerful inclusion in an application of time series pattern, we use two popular regression methods, including an extended Autoregressive Integrated Moving Average (Auto ARIMA) and stacked Long Short-Term Memory (LSTM) to analyze crime patterns, specifically during the Covid-19 pandemic lockdown, and generate forecasts. We experimented our methods on London Crime Dataset and obtained some interesting results which can not only be useful to take necessary precautions, but also analyze crime patterns during the period of pandemic lockdowns for generating useful guidelines regarding citizens' life styles and hence, contribute to reducing the crime rates accordingly. © 2022 IEEE.

Experience Report on the Use of Breakout Rooms in a Large Online Course

In this paper, we present our experience with the use of breakout rooms in a second year undergraduate Software Design course at a large North American institution. Following the switch to remote instruction during the coronavirus pandemic, we revamped our in-person Software Design course to be delivered as a flipped online course, making extensive use of in-lecture exercises completed during breakout rooms. We report on the structure and logistics of this lecture design (for a large class of 300+ students). To gain insights into the impact of the use of breakout rooms on student experience, we conducted weekly student surveys asking for feedback on the lectures and specifically on the use of breakout rooms. Although many students had positive feelings regarding the use of breakout rooms, a significant percentage of students (an average of 47% of the survey responses each week) expressed negative feelings toward them. In an end-of-term survey, we specifically asked students about what they felt worked best for breakout rooms in terms of group size and pre-assigned versus randomized groups, and if there were any other areas that they felt needed improvement. Some of the patterns we observed were that most students liked smaller groups (2-5 people), preferred staying in the same group throughout the semester, and enjoyed the use of breakout rooms as long as others in their room were active participants. We share the details of these survey results as well as the tips and lessons that we learned through this experience. © 2022 ACM.

Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture

The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96 % sensitivity, and 96 % F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients. © 2021 Institute of Advanced Engineering and Science (IAES).

Updated results of the COVID-19 in MS global data sharing initiative validate consistent associations of anti-CD20 and other reported risk factors with severe COVID-19 outcomes

Background: As the COVID-19 pandemic continues, evidencebased clinical guidance for managing the care of people with multiple sclerosis (MS) is an ongoing concern. In recent months, data from cohorts of people with MS has indicated that certain demographic and clinical characteristics, including use of some disease- modifying therapies (DMTs), leads to worse outcomes from SARS-CoV-2 infection. The COVID-19 in MS global data sharing initiative, which now includes over 4,500 confirmed COVID- 19 cases in people with MS, gives the opportunity to corroborate previous findings with greater certainty. Methods: Clinician-reported data from 32 countries were aggregated into a dataset of 5,543 patients who had suspected or confirmed COVID-19. Demographic and clinical covariates were queried, alongside COVID-19 clinical severity outcomes. These outcomes (hospitalisation, admission to ICU, requiring artificial ventilation, and death) were assessed in patients with suspected/ confirmed COVID-19 using multilevel mixed-effects logistic regression. All models were corrected for age, sex, EDSS, and MS type. DMTs were individually compared to glatiramer acetate (GA), as well as to pooled other DMTs and natalizumab. Results: Of 5,543 patients in the clinician-reported dataset, 909 with suspected and 4,634 with confirmed COVID-19 were included in the analysis. Previous demographic findings were confirmed: male sex, older age, progressive MS, and higher disability were associated with worse outcomes from SARS-CoV-2 infection. Use of anti-CD20 DMTs (ocrelizumab and rituximab) was associated with worse COVID-19 outcomes. Compared to GA, ocrelizumab and rituximab were associated with increased risk of hospitalisation (aOR=1.61(95%CI=1.06-2.43);aOR=2.42(95%CI=1.54-3.81) and ICU admission (aOR=3.13(95%CI=1.22-8.00);aOR=4.46 (95%CI=1.64-12.09)). Rituximab was associated with increased risk of artificial ventilation (aOR=3.57(95%CI=1.38-9.20));ocrelizumab showed a positive trend (aOR=1.86(95%CI=0.76-4.55). Rituximab showed a positive trend with increased risk of death (aOR=2.74(95%CI=0.68-11.09). Associations persisted on restriction to confirmed COVID-19 cases. Conclusions: Analysing the largest international real world dataset of people with MS who have suspected or confirmed COVID- 19 confirms previous findings that male sex, older age, progressive MS, higher disability, the use of anti-CD20 medication (ocrelizumab and rituximab) are associated with worse COVID-19 outcomes.