Cerebral venous sinus thrombosis (CVST), an unusual presentation of COVID-19: A case report from Bangladesh

Newly recognized pandemic infectious disease COVID-19 (Corona-virus disease) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This viral infection causes hypercoagulability and inflammation leading to increased incidence of both arterial and venous thrombotic events (VTEs). Therefore, patients infected with this novel virus seem to be at higher risk of thrombotic events (TEs) resulting in thromboembolic diseases, especially stroke and pulmonary embolism, or even cerebral venous sinus thrombosis (CVST). We report a case of 42-year-old female, presented with features of venous thrombotic events (extensive dural venous sinus thrombosis) and was subsequently found to have COVID-19 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) test. The case report indicates CVST might be an unusual manifestation of COVID-19. Cerebral venous sinus thrombosis even presents as an initial symptom of COVID-19 without significant respiratory symptoms. Early diagnosis and treatment with thrombolytic agent in case of SARS-CoV-2 infection result in reduced morbidity and mortality. We recommend further studies to establish SARS-CoV-2 virus (the COVID-19 disease) as a known risk factor for CVST. © 2020 The author (s).

Continuous Sign Language Interpretation to Text Using Deep Learning Models

The COVID-19 pandemic has obligated people to adopt the virtual lifestyle. Currently, the use of videoconferencing to conduct business meetings is prevalent owing to the numerous benefits it presents. However, a large number of people with speech impediment find themselves handicapped to the new normal as they cannot communicate their ideas effectively, especially in fast paced meetings. Therefore, this paper aims to introduce an enriched dataset using an action recognition method with the most common phrases translated into American Sign Language (ASL) that are routinely used in professional meetings. It further proposes a sign language detecting and classifying model employing deep learning architectures, namely, CNN and LSTM. The performances of these models are analysed by employing different performance metrics like accuracy, recall, F1- Score and Precision. CNN and LSTM models yield an accuracy of 93.75% and 96.54% respectively, after being trained with the dataset introduced in this study. Therefore, the incorporation of the LSTM model into different cloud services, virtual private networks and softwares will allow people with speech impairment to use sign language, which will automatically be translated into captions using moving camera circumstances in real time. This will in turn equip other people with the tool to understand and grasp the message that is being conveyed and easily discuss and effectuate the ideas. © 2022 IEEE.

COVID-19 Reinfection and Disease Severity in the New York City Health + Hospitals System

Background. Though reinfection with SARS-CoV-2 is well documented, there remains uncertainty about the potential for more severe symptoms with reinfections compared to index infections. Methods. Patients who received SARS-CoV-2 PCR testing between March 1, 2020 and March 1, 2021 at New York City Health and Hospitals (NYC H+H) facilities and had two positive tests>=90 days apart were included in the analysis. Clinical and demographic data were extracted from the electronic medical record. Manual chart review was done to confirm symptomatology, assess COVID-19 related hospital admissions, and determine WHO disease severity. Patients were then classified as unlikely reinfection, possible reinfection, or probable reinfection based on symptomatology, PCR and antibody testing, and lack of alternative diagnoses. Patients were classified as 'unable to be assessed' if symptomatology could not be assessed for both episodes of PCR positivity. Results. During our study timeframe, 1,255,584 unique patients received at least one SARS-CoV-2 PCR test, 265 of whom had two positive tests>=90 days apart. We categorized 20 patients as unable to be assessed, 28 as unlikely reinfection (1 persistent PCR positivity, 27 unlikely true infection at index or second PCR-positive episode), and 217 as possible or probable reinfection. Of the 217, at their index episode 79 had an asymptomatic infection (36.4%) and 17 were severe or critical (7.8%). At their second episode, 162 patients had an asymptomatic infection (74.7%), and 5 were severe or critical (2.3%). Only 24 patients with possible/probable reinfection had a more severe COVID reinfection than index infection, and 20 of the 24 had asymptomatic index infections. Three patients were hospitalized at both episodes, and two deaths possibly attributable to COVID-19 reinfection were noted in this cohort. Figure 3: Change in WHO disease severity classification from index to second infection among probable/possible reinfection cases (n=217) Red indicates increase in disease severity from index to reinfection (n=24), blue indicates decrease in disease severity from index to reinfection (n=100), white indicates no change (n=74) and gray indicates unable to assess disease severity at index or second infection (n=19). Conclusion. COVID-19 reinfection was rare in a high incidence setting among patients tested at NYC H+H facilities. Disease severity was generally milder in reinfection, although severe and critical disease occurred in a small number of patients.These findings from earlier in the pandemic (presumably wild-type and alpha variant) provide data for comparison in understanding how reinfection is evolving with newer variants.

Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective

This research focuses on the education-based online learning platform. Due to the coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?". © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Persistent dyspnea after recovery from COVID-19: two case reports from Bangladesh

Globally, millions of documented SARS-CoV-2 infections with hundreds of thousands of deaths already reported. The majority of the fatal events have been reported in adults older than 70 years and those who have multiple co-morbidities. Despite the misery fatality of the virus, a significant number of peoples recovered from critical conditions also. Mild cases improved significantly with symptomatic management with strict maintenance of isolation. Therefore, many people believed that COVID-19 is a short-term illness, mild cases recovered completely within 2 weeks and severe or critical illness may require 3-6 weeks for complete recovery. However, the latest issue coming forward is delayed recovery in the surviving patients from severe or moderate COVID presenting with multisystem complications. We reported two cases of post COVID complications, newly named as "long COVID syndrome". We described the common symptoms two patients experienced following recovery from acute phase of COVID-19 and how they were managed. We also discussed on the pathogenesis and management plan of common symptoms persisting after recovery of COVID-19.

Prediction Model for Mortality Analysis of Pregnant Women Affected with COVID-19

COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus's effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonate's health. This paper aims to develop a predictive model to estimate the possibility of death for a COVID-diagnosed mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia, and the diagnosis of pneumonia. The machine learning models that have been used in our study are support vector machine, decision tree, random forest, gradient boosting, and artificial neural network. The models have provided impressive results and can accurately predict the mortality of pregnant mother's with a given input. The precision rate for 3 models(ANN, Gradient Boost, Random Forest) is 100% The highest accuracy score(Gradient Boosting, ANN) is 95%, highest recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient Boosting, ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus. The model can be utilized by health workers globally to list down emergency patients, which can ultimately reduce the death rate of COVID-19 diagnosed pregnant mothers. © 2021 IEEE.

COMMUNITY PEOPLE PREPAREDNESS AND RESPONSE ON PREVENTION AND CONTROL OF COVID-19 BEST PRACTICE IN BANGLADESH

PURPOSE: The major objectives of the study were to assess the knowledge, attitude & practice (KAP) towards community preparedness and response on prevention of COVID-19 among the community. METHOD: A sample survey was conducted to collect data from people admitted in a district level tertiary hospital for treatment of various health complications during COVID-19 pandemic. A total of 300 randomly selected patients and their attendants were interviewed in the hospital setting. RESULTS: The mean knowledge score was 18.73 out of 24 and the main sources of information were TV (86.5%), radio (13%), newspaper (13%), social media (13.5%), friends/relatives (14%), formal healthcare providers (6%) and religious leaders (3%). Knowledge was significantly poor among aged people, women, less educated and those on low incomes. The Majority of participants (79%) suggested wearing facemasks as effective tools to prevent COVID-19 from spreading, 56% mentioned maintaining of physical or social distance as crucial to prevent the infection. We found strong relationship between monthly total family expenses and wearing of facemasks by gender to prevent the COVID-19 (x2= 18.405;Cramer's V= .17, df = 8;sig;P= < .018). Similarly maintaining physical/social distance to prevent COVID-19 is also related to respondents' economic strata (x2= 43.741;Cramer's V= .14, df = 20;Sig;P= < .002). CONCLUSIONS: An awareness program on COVID-19 is very important to prevent the spread of the virus. Effective communication intervention with increasing treatment facilities is essential for prevention and control of COVID-19. Government and development agencies should prioritize the COVID-19 response program with regular health care services.

COVID-19 complicated with arterial thrombosis resulting acute limb ischemia: a case report from Bangladesh. (Special Issue: Pandemic COVID19.)

COVID-19 (Corona virus disease 2019), which starts from Wuhan, China on December, 2019 spread rapidly to different countries of the world including Bangladesh. It affects huge impact on health care system. It's a new disease with multisystem involvement. Physicians are experiencing new presentation of different cases and rare complication including arterial thrombosis. Few data is available regarding arterial thrombosis in SARS-CoV-2 infected patients. We are currently fighting with a 60 year old lady suffering from COVID-19 pneumonia with other co-morbidities developed severe arterial occlusion of right leg despite of taking anti platelet for long time for another cause. Patient developed irreversible right lower limb ischemia not improving with continuous infusion of unfractionated heparin followed by severe pulmonary embolism. So further study and recommendations will need to evaluate the cases and treatment in COVID-19 Patients with rare presentation.