Role of tyrosine kinase inhibitor in chronic myeloid leukemia patients with SARS-CoV-2 infection: A narrative Review.

Severe acute respiratory syndrome (SARS) caused by a novel coronavirus-2 (CoV-2), also known as COVID-19, has spread rapidly worldwide since it is recognized as a public health emergency and has now been declared a pandemic on March 11, 2020, by the World Health Organization. The genome of SARS-CoV-2 comprises a single-stranded positive-sense RNA approximately 27 to 30 kb in size. The virus is transmitted through droplets from humans to humans. Infection with the SARS virus varies from asymptomatic to lethal, such as fever, cough, sore throat, and headache, but in severe cases, pneumonia and acute respiratory distress syndrome. Recently, no specific and effective treatment has been recommended for patients infected with the SARS virus. However, several options can be investigated to control SARS-CoV-2 infection, including monoclonal antibodies, interferons, therapeutic vaccines, and molecular-based targeted drugs. In the current review, we focus on tyrosine kinase inhibitor management and their protective role in SARS-CoV-2 patients with chronic myelogenous leukemia.

COVID-19 morbidity in Afghanistan: a nationwide, population-based seroepidemiological study.

OBJECTIVE: The primary objectives were to determine the magnitude of COVID-19 infections in the general population and age-specific cumulative incidence, as determined by seropositivity and clinical symptoms of COVID-19, and to determine the magnitude of asymptomatic or subclinical infections. DESIGN, SETTING AND PARTICIPANTS: We describe a population-based, cross-sectional, age-stratified seroepidemiological study conducted throughout Afghanistan during June/July 2020. Participants were interviewed to complete a questionnaire, and rapid diagnostic tests were used to test for SARS-CoV-2 antibodies. This national study was conducted in eight regions of Afghanistan plus Kabul province, considered a separate region. The total sample size was 9514, and the number of participants required in each region was estimated proportionally to the population size of each region. For each region, 31-44 enumeration areas (EAs) were randomly selected, and a total of 360 clusters and 16 households per EA were selected using random sampling. To adjust the seroprevalence for test sensitivity and specificity, and seroreversion, Bernoulli's model methodology was used to infer the population exposure in Afghanistan. OUTCOME MEASURES: The main outcome was to determine the prevalence of current or past COVID-19 infection. RESULTS: The survey revealed that, to July 2020, around 10 million people in Afghanistan (31.5% of the population) had either current or previous COVID-19 infection. By age group, COVID-19 seroprevalence was reported to be 35.1% and 25.3% among participants aged ≥18 and 5-17 years, respectively. This implies that most of the population remained at risk of infection. However, a large proportion of the population had been infected in some localities, for example, Kabul province, where more than half of the population had been infected with COVID-19. CONCLUSION: As most of the population remained at risk of infection at the time of the study, any lifting of public health and social measures needed to be considered gradually.

Dermatological morbidity induced by the use of personal protective measures in frontline doctors during COVID-19 pandemic

Background Frontline doctors performing duties during Covid-19 pandemic have to use the personal protective equipment to avoid exposure and decrease the risk of Covid-19 infection. These protective measures can lead to various cutaneous manifestations and problems which affect their working. Objective To assess dermatological morbidity due to use of personal protective equipment (PPE). Methods This descriptive observational study was conducted on 220 doctors performing duties on frontline in Covid-19 pandemic. Data was collected through e- questionnaire regarding demography, daily duty, daily PPE wearing time, cutaneous manifestation, their type and site. Participants voluntarily allowed and submitted the questionnaire through cell phones. Results 52% of frontline doctors using PPEs while performing duties in Covid-19 pandemic showed dermatological morbidity. Most of them belonged to the age group >35 years, males, married, having postgraduate qualification. Most of the frontline doctors having dermatological manifestation were performing regular OPD duties for > 4 hours. Presence of comorbidities and hand washing for more than 10 times were also associated factors. Conclusion There is 52% dermatological morbidity in frontline doctors using PPEs while performing duties in Covid-19 pandemic and it was statistically significantly associated with male gender, postgraduate qualification, >4 hours/ day OPD duty, presence of comorbidities and hand washing >10 times per day.

An LSTM based forecasting for major stock sectors using COVID sentiment

Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline, pharmaceutical, e-commerce, technology, and hospitality. We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory (LSTM) model to improve stock prediction. The LSTM has the advantage of analyzing relationship between time-series data through memory functions. The performance of the system is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model. © 2021 Tech Science Press. All rights reserved.