ABSTRACT
Several air quality parameters such as particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) are considered as the major pollutants which can impose a significant threat to human health and surrounding environment. In this study, seasonal and temporal variations were analyzed for both gaseous air pollutants and particulate matter to investigate the trend analysis of ambient air quality of Chattogram city, a commercial hub of Bangladesh. Air quality data for six selected parameters (PM2.5, PM10, CO, SO2, NO2, and O3) were collected from Continuous Air Monitoring Stations (CAMS) during the period 2013 to 2021 for each pollutant. Air Quality Index (AQI) for each tested pollutant was determined as well as pollution level sharing among the pollutants was also investigated in this work. Results of this study showed that particulate matters (PM2.5 and PM10) were the most responsible pollutants that contributed significantly to air pollution levels in the city. The yearly average AQI was observed to be in the caution (unhealthy for sensitive groups) (100-150) category during the period from 2013 to 2021. Trend analysis showed that there is an ups and downs trend in the AQI level in the city that may be triggered by some interventions taken and Covid-19 pandemic situations. Overall, seasonal variation had a considerable effect on the concentration of pollutants. For each year, the highest concentration of PM2.5 and PM10 was recorded in winter season while the lowest was reported in monsoon season. This study will assist the researchers and policymakers in taking the required steps to take preventive measures in reducing air pollution levels for the studied area. © 2023 Author(s).
ABSTRACT
Background: Currently, the present world is facing a new deadly challenge from a pandemic disease called COVID-19, which is caused by a coronavirus named SARS-CoV-2. To date, no drug or vaccine can treat COVID-19 completely, but some drugs have been used primarily, and they are in different stages of clinical trials. This review article discussed and compared those drugs which are running ahead in COVID-19 treatments. Method(s): We have explored PUBMED, SCOPUS, WEB OF SCIENCE, as well as press releases of WHO, NIH and FDA for articles related to COVID-19 and reviewed them. Result(s): Drugs like favipiravir, remdesivir, lopinavir/ritonavir, hydroxychloroquine, azithromycin, ivermectin, corticosteroids and interferons have been found effective to some extent, and partially approved by FDA and WHO to treat COVID-19 at different levels. However, some of these drugs have been disapproved later, although clinical trials are going on. In parallel, plasma therapy has been found fruitful to some extent too, and a number of vaccine trials are going on. Conclusion(s): This review article discussed the epidemiologic and mechanistic characteristics of SARS-CoV-2, and how drugs could act on this virus with the comparative discussion on progress and drawbacks of major drugs used till date, which might be beneficial for choosing therapies against COVID-19 in different countries.Copyright © 2022 Bentham Science Publishers.
ABSTRACT
Blood cell identification and counting is critical for doctors and physicians nowadays in order to diagnose and treat a variety of disorders. Platelet identification and counting are frequently performed in the context of many types of sickness such as COVID-19 and others. However, it is frequently costly and time intensive. Additionally, it is not widely available. From this vantage point, it is necessary to develop an efficient technical model capable of detecting and counting three fundamental types of blood cells: platelets, red blood cells, and white blood cells. Thus, this study proposes a deep learning-based model based on the YOLOv5 model with a precision of 0.799. The model consists of thre different layers such as backbone, neck and output layer The model is extremely capable of detecting and counting individual blood cells. Doctors, physicians, and other professionals will be able to detect and count blood cells using real-time images. It will significantly minimise the cost and time associated with detecting and counting blood cells by utilizing real-time blood images. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The hard times of COVID 19 have shown mankind some harsh reality. Numerous sectors including education have been terribly victimized by the grasp of the pandemic. Schools, colleges, and universities were closed down for enumerable days and the students were detached from their studies. The dropout rate from university for the students owing to financial and circumstantial facts has been a vital concern. In our research, we conduct an effective study on the dropout rate of university students. We try to find out the inherent reasons for their dropout and try to come up with an effective solution. By an online survey, we have collected data from 422 Bangladeshi undergraduate students. After training and testing the dataset with several popular algorithms such as SVM, Logistic regression, Random Forest, Decision Tree, etc., the best methods for predicting dropout among Bangladeshi undergraduates were discovered. © 2022 IEEE.
ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is the most important emerging pathogen since it was discovered in late 2019, infecting millions of people worldwide. The human body's defence against this new viral respiratory infection depends on the immune response of each person with a crucial impact on the appearance of clinical signs. Therefore, it is important to identify endogenous molecules with a fundamental role in severe pulmonary inflammation associated with SARS-CoV-2 infection. The impact of high mobility group proteins (HMGBs) in the pathogenesis of coronavirus disease 2019 (COVID-19) was recently proposed. There is also recent evidence that HMGBs, particularly HMGB1–2, play important roles in the replication of viral genomes. Moreover, HMGB1–4 proteins appear to be associated with inflammatory processes in the pathogenesis of many other viral diseases and disorders, including lung disease, ischemia-reperfusion-injury, sepsis, coagulopathy, trauma, neurological disorders, and cancer. This article presents the possible roles of HMGB1 in SARS-CoV-2 replication and its involvement in the pathogenesis of clinical severe pulmonary manifestations;these data can be useful in further virologic studies and the finding of new potential therapeutic targets in COVID-19.
ABSTRACT
Recently a global terror has taken place around all over the world named of COVID-19 disease. The main cause of this disease is SARS-CoV-2 virus. A huge number of population of the world is losing lives daily for this terrible virus. But in the global survey for this disease we found that the people also getting recovered from this frightening disease. The most important thing that works behind the recovery against this virus is the immunity power of human body. Immunity power is not same for all human body. Immunity power of human depends on the food habit of them. In this research we will try to determine the probability of COVID-19 recovered in South Asian Countries based on healthy diet pattern using data mining and various machine learning algorithms. We have used Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are the several machine learning algorithms to predict the recovery rate of Covid-19 affecting patients.
ABSTRACT
Background and Aims: The pandemic outbreak of COVID-19 has been devastating not only for its direct impact on lives and physical health settings, but also on mental health status. Due to the pandemic, the majority of the individuals exposed to an unprecedented stressful lockdown situation with unknown duration worldwide. This study aimed to evaluate the psychological health (prevalence of perceived stress, anxiety, and depression as well as insomnia symptoms) impact of COVID-19 lockdown on the general peoples and assess the mental health education users. Methods: Following the previous literatures, semi-structured questionnaires were developed and snowball sampling technique was applied in this qualitative study. Data were analyzed via SPSS Statistical software;while p<0.05 was considered significant. Results: Our result revealed that, among 50% anxiety symptoms, mild, moderate and severe level were of 28%, 17 % and 5%, respectively whereas the percentages of minimal, moderate and severe symptoms were found to be 8.4%, 2.9% and 1.6%, corresponding among 12.9% depressed people whereas sub-threshold, moderate and severe clinical symptoms (15%, 6 % and 2%, respectively) among 23.3% insomnia. However, self-reported stress as well as mental dysfunction showed significant value of 54.4% and 1.3%, respectively in contrasting with 23.3% demonstrated normal psychic behavior. Dual psychiatric problems including stress and anxiety, anxiety and insomnia and depression and insomnia symptoms were reported by 37%, 15% and 6% participants, respectively. There was insignificant (p<0.05) mental health effects in response to gender variations. 21%, 16%, 2% and 5% exhibited stress, anxiety, depression and insomnia among 30% female, and the results were 35%, 34%, 11% and 18% among 69.6% male respondents. The study reported that, 47.4% respondents used health education related to decreasing pandemic mental distress syndromes. Conclusion: An integrated approach in few settings including mental health educations, community connectedness and effective health policies are needed not only to manage psychological pandemic health problems and for strengthening the overall health systems.