Autoencoder-based Unsupervised Anomaly Detection for Covid-19 Screening on Chest X-Ray Images

The COVID-19 outbreak is a major global catastrophe of our time and the largest hurdle since World War II. According to WHO, as of July 2022, there are more than 571 million confirmed cases of COVID-19 and over six million deaths. The issue of identifying unexpected inputs based on trained examples of normal data is known as anomaly detection. In the case of diagnosing covid-19, Chest X-ray disorders that are hardly apparent are extremely challenging to identify. Although various well-known supervised classification methods are being applied for that purpose, however in the real scenario, healthy patients' data is tremendously available but contaminated samples are scarce. The process of gathering samples from ill patients is troublesome and takes a lengthy time. To address the issue of data imbalance in anomaly detection, this research demonstrates an unsupervised learning technique using a convolutional autoencoder in which the training phase does not include any infected sample. Being trained only with the healthy data, The patterns of the healthy samples are preserved in latent vector space and can differentiate ill samples by observing substantial divergence from the distribution of healthy data. Higher reconstruction error and lower KDE (Kernel Density Estimation) indicate affected data. By contrasting the reconstruction error and KDE of healthy data with anomalous data, the suggested technique is feasible for identifying anomalous samples. © 2022 IEEE.

Systematic Analysis of Several Deep Learning Approaches for COVID-19 Detection Using X-ray Images

COVID-19 is a virus-borne malady. A clinical study of infected COVID-19 patients found that most COVID-19 patients suffered lung infection after contracting the disease. Consequently, chest X-rays are a more effective and lower-cost imaging technique for diagnosing lung-related problems. This study used deep learning models, including MobileNetV2,DenseNet201, ResNet50, and VGG19, for COVID-19 prediction. For the study, we used chest X-ray image data for binary classification of COVID-19. 7207 chest X-ray image data were obtained from the Kaggle repository, with 5761 being utilized for training and 1446 being used for validation. A comparative analysis was conducted among the models and examined their accuracy. It has been determined that the DenseNet201 models achieved the highest accuracy of 93.02% for detecting COVID-19 in the lowest compilation time of 27secs. The models, MobileNetV2, ResNet50, and VGG19 had the accuracy rate of 77.28%, 65.86% and 74.92%, respectively. The research indicates that the DenseNet201 model is the most effective in detecting COVID-19 using x-ray imaging. © 2022 IEEE.

COVID-19 Detection Through Deep Learning Algorithms using Chest X-ray Images

COVID-19 became a pandemic affecting the lives of every human globally by the end of 2019. The disease impaired the lungs of infected patients. Precise prediction and diagnosis of COVID-19 disease are challenging due to its resemblance to viral pneumonia. Using multiple deep learning approaches, the researchers used chest X-ray (CXR) imaging to diagnose COVID-19. The X-ray image dataset from Kaggle is used for the study by selecting the COVID-19 and normal class. InceptionV3, MobileNetV2, VGG19,VGG16 and ResNet50 are the five neural networks used for binary classification of COVID-19. The accuracy of MobileNetV2 surpasses that of the remainder of the model by 93.02%. However, it has a compilation time of 1836 seconds per epoch. Besides, VGG16 has an accuracy of 92.37%, with a compilation time of 603 seconds per epoch. Compared to these models, Inceptonv3, Resnet50 and VGG19 perform with an accuracy score of 86.42%, 68.34% and 91.79%. Applying deep learning techniques to COVID-19 radiological imaging holds great promise for enhancing the accuracy of diagnosis when in comparison to the gold standard RT-PCR test and assisting healthcare professionals in making decisions quickly © 2022 IEEE.

Online Detection of Attentiveness of Students with Special Needs

In this COVID-19 pandemic era, students with Autism Spectrum Disorder (ASD) are struggling to adapt to classes in the online environment using Google Meet or Zoom. Failing to keep sustained attention in the class is a common problem for students with ASD. In face-to-face classes, teachers can track a student's behavior and activity to infer the student's attentiveness level and act accordingly. However, it becomes difficult for a teacher to monitor the attentiveness level of multiple students simultaneously on online platforms like Zoom. Detecting the attentiveness level of a student and notifying the teacher in an automated way can play a crucial role in improving the learning outcome. In this paper, we propose the first deep learning based attentiveness level prediction technique for students with ASD. Our model detects the behavior (e.g., unusual movement, gaze etc.) and activities from real-time videos and uses them as features to classify the attentiveness level as low, mid and high. Existing state-of-the-art techniques to detect the attentiveness level of typically developed students using gaze or facial expression cannot be trivially extended for students with ASD as they do not exhibit regular and consistent behavior. We collect video data belonging to different classes covering various types of activities over a long period, train our classifier, and run extensive experiments to validate the prediction performance. Our solution outperforms existing baselines by a large margin.

Development of Potent SARS-CoV 3CL Protease Inhibitors: In silico Analogues Designing of Darunavir, Molecular Modelling and Molecular Dynamic Simulation

COVID pandemic initiated in early 2019 and the origin from where it initiated was Wuhan city of China. It changed the whole world. A huge population died due to COVID-19 in spite of taking precautions. New treatments and vaccines are introduced for the treatment and prevention. Among successful treatments, antivirals were found effective against COVID-19. But there is a need to find derivatives, which could be more effective for the treatment of COVID-19. The current research is focused on computational studies on one of the antiviral, darunavir. A computational strategy, molecular docking and molecular dynamic simulation techniques is presented to discover the potent analogues of darunavir for inhibiting protease 3CLpro of SARS-CoV2. The newly discovered X-ray structure (PDB ID: 6LU7) was selected for docking study and generated analogues were docked. The docking results showed that the compounds were bound in the active site of receptor with good binding affinity. It was concluded that compounds D8 and D15 were have good binding affinity value of -9.85 and -8.95 kcal/mol, respectively and these compounds were selected for molecular dynamic simulation (MDS) study to check their stability in pocket of receptor. © 2022 Chemical Publishing Co.. All rights reserved.

Bangladesh and Covid-19: Reversals and Resilience*

This article examines two primary data sets to identify the effect of the Covid-19 pandemic on different sectors and vulnerable populations in Bangladesh. It attempts to identify how the trends in sectors such as agriculture, ready-made garments (RMGs), education, employment among youth, and women’s participation have changed due to the pandemic compared to pre-Covid-19 levels. The results show that the agriculture and RMG sectors demonstrated resilience due to sustained government policies. In contrast, the other sectors, such as education, youth employment, and women’s participation in the labour market, have been negatively affected, leaving a long-term consequence for the country’s development. The article concludes with suggestions for inclusive and targeted policies, and community-based approaches to pre-empt new challenges to make development progress in Bangladesh. © 2022 The Authors. IDS Bulletin , Institute of Development Studies. and Crown 2022.

Guillain-Barre Syndrome As An Emerging Side Effect of SARS-CoV-2 Vaccination: A Literature Review

Objective: Our review aims to study the significance of the association between the manifestations of Guillain-Barre syndrome (GBS) and COVID-19 immunization, as well as provide medical practitioners with relevant clinical information through a detailed summary of the current cases of GBS related to the COVID-19 vaccines. Additionally, we will shed light on the impact of associated demographic risk factors such as age, gender, and comorbid conditions in the development of GBS post-vaccination. Background: Guillain-Barre syndrome (GBS) is a rare and potentially fatal post-infectious, immune-mediated neuropathy characterized by rapidly progressive weakness and ascending paralysis. As an adverse reaction to the COVID-19 vaccines, GBS is becoming an arising catastrophe increasingly reported as a complication of the COVID-19 vaccines. Design/Methods: A literature search was conducted across four databases: PubMed, PubMed Central, Medline (through PubMed), and Google Scholar using predefined keywords. These keywords included “Guillain Barre Syndrome, ” “COVID-19 vaccination”, “COVID-19”. The search criteria were set to filter cases of GBS in post-COVID-19 vaccination, reported between March 2020 to October 2021. Results: A total of eighteen articles were selected from peer-reviewed journals which documented twenty-eight patients (ages ranged between 20-82 years old) that had developed GBS after receiving COVID-19 vaccinations;fifteen males and thirteen females. GBS side effects were reported with five COVID-19 vaccines including Pfizer, Moderna, Janssen, AstraZeneca (now called Vaxzevria), and a vector-based vaccine. In addition, the average duration between COVID-19 vaccine administration and GBS symptoms onset was noted to be 12.46 days. Conclusions: Although it is too early to draw conclusions concerning GBS following COVID-19 vaccination, we recommend monitoring for cases suggestive of GBS following vaccination and implementing post-vaccination surveillance to ensure adequate data gathering of this outcome, as well as to determine its cause. Additionally, we encourage even further large-scale research into this area.

PSYCHOMETRIC EVALUATION OF THE MODIFIED “FEAR OF COVID-19” SCALE

This study was designed to modify the recently developed “Fear of COVID-19” scale (FCV-19S) as a diagnostic criterion and to evaluate its psychometric properties and potential to predict risk of psychological problems. Through an e-questionnaire, data for this study were collected from 1,317 university students from 49 universities in Bangladesh. The modified “Fear of COVID-19” scale (MFCV-19S) showed good internal consistency (ω =.867) and concurrent validity;there was significant association with anxiety and depression. The unidimensionality was confirmed by an acceptable average variance extracted (0.49) and construct reliability (.87). The MFCV-19S differentiates fairly between persons with and without anxiety disorder, using an optimized cut score of ≥ 8 (93% sensitivity and 78% specificity). The multivariate analysis also suggested that MFCV-19S can significantly predict risk of mental health problems. The results indicated that the MFCV-19S is an efficient and valid psychometric tool for screening fear of COVID-19 among students and could be used for general people © Copyright: The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 <http://creativecommons.org/licenses/by/4.0/>)

Planning a modular intensive care unit for patients of the COVID-19 pandemic

Introduction: The COVID-19 pandemic has provided opportunity to the Armed Forces Medical Services (AFMS) healthcare institutions to plan and execute their surge capacity facilities and identify areas for improvements in planning in the future. Material and Methods: Available medical literature on the experiences of other countries in activating surge capacities in healthcare for the pandemic were examined in detail as were existing guidelines for establishing Intensive Care Units (ICU). Personal communications with peers to understand difficulties faced in activating surge capacities were also factored in. Results: Based on the findings from these sources, a plan to establish ten-bedded ICU units specifically for COVID-19 is evolved. The best practices and latest guidelines and experiences have been collated and modified suitably to suit the AFMS in this aspect. Conclusion: Planning ICUs in ten-bedded modular units will enable the AFMS to cater to surge capacities in the future for all situations where sudden increase in number of patients is anticipated.

Risk factors associated with morbidity and mortality outcomes of COVID-19 patients on the 28th day of the disease course: a retrospective cohort study in Bangladesh.

Diverse risk factors intercede the outcomes of coronavirus disease 2019 (COVID-19). We conducted this retrospective cohort study with a cohort of 1016 COVID-19 patients diagnosed in May 2020 to identify the risk factors associated with morbidity and mortality outcomes. Data were collected by telephone-interview and reviewing records using a questionnaire and checklist. The study identified morbidity and mortality risk factors on the 28th day of the disease course. The majority of the patients were male (64.1%) and belonged to the age group 25-39 years (39.4%). Urban patients were higher in proportion than rural (69.3% vs. 30.7%). Major comorbidities included 35.0% diabetes mellitus (DM), 28.4% hypertension (HTN), 16.6% chronic obstructive pulmonary disease (COPD), and 7.8% coronary heart disease (CHD). The morbidity rate (not-cured) was 6.0%, and the mortality rate (non-survivor) was 2.5%. Morbidity risk factors included elderly (AOR = 2.56, 95% CI = 1.31-4.99), having comorbidity (AOR = 1.43, 95% CI = 0.83-2.47), and smokeless tobacco use (AOR = 2.17, 95% CI = 0.84-5.61). The morbidity risk was higher with COPD (RR = 2.68), chronic kidney disease (CKD) (RR = 3.33) and chronic liver disease (CLD) (RR = 3.99). Mortality risk factors included elderly (AOR = 7.56, 95% CI = 3.19-17.92), having comorbidity (AOR = 5.27, 95% CI = 1.88-14.79) and SLT use (AOR = 1.93, 95% CI = 0.50-7.46). The mortality risk was higher with COPD (RR = 7.30), DM (RR = 2.63), CHD (RR = 4.65), HTN (RR = 3.38), CKD (RR = 9.03), CLD (RR = 10.52) and malignant diseases (RR = 9.73). We must espouse programme interventions considering the morbidity and mortality risk factors to condense the aggressive outcomes of COVID-19.