Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023.

BACKGROUND: Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. MATERIALS AND METHODS: The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. RESULTS: Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). CONCLUSION: This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient's CVD prognosis.

Impact of environmental factors on COVID-19 transmission: spatial variations in the world.

The COVID-19 pandemic caused enormous destruction to global health and the economy and has surged worldwide with colossal morbidity and mortality. The pattern of the COVID infection varies in diverse regions of the world based on the variations in the geographic environment. The multivariate generalized linear regression models: zero-inflated negative binomial regression, and the zero-inflated Poisson regression model, have been employed to determine the significant meteorological factors responsible for the spread of the pandemic in different continents. Asia experienced a high COVID-19 infection, and death was extreme in Europe. Relative humidity, air pressure, and wind speed are the salient factors significantly impacting the spread of COVID-19 in Africa. Death due to COVID-19 in Asia is influenced by air pressure, temperature, precipitation, and relative humidity. Air pressure and temperature substantially affect the spread of the pandemic in Europe.

There is a substantial variation of the impacts of environmental variables on the spread of the COVID-19 pandemic in different parts of the world.Temperature and air pressure significantly impact the number of infections and death due to COVID-19 in Europe.Relative humidity, temperature, sky clearness, and wind speed posed significant positive effects on COVID-19 in AfricaThe spread of COVID-19 infection and death is maximum in low temperatures.The confounding effect of the maximum number of meteorological factors minimizes the transmission of the pandemic.

Phase-separated protein droplets of amyotrophic lateral sclerosis-associated p62/SQSTM1 mutants show reduced inner fluidity.

Several amyotrophic lateral sclerosis (ALS)-related proteins such as FUS, TDP-43, and hnRNPA1 demonstrate liquid-liquid phase separation, and their disease-related mutations correlate with a transition of their liquid droplet form into aggregates. Missense mutations in SQSTM1/p62, which have been identified throughout the gene, are associated with ALS, frontotemporal degeneration (FTD), and Paget's disease of bone. SQSTM1/p62 protein forms liquid droplets through interaction with ubiquitinated proteins, and these droplets serve as a platform for autophagosome formation and the antioxidative stress response via the LC3-interacting region (LIR) and KEAP1-interacting region (KIR) of p62, respectively. However, it remains unclear whether ALS/FTD-related p62 mutations in the LIR and KIR disrupt liquid droplet formation leading to defects in autophagy, the stress response, or both. To evaluate the effects of ALS/FTD-related p62 mutations in the LIR and KIR on a major oxidative stress system, the Keap1-Nrf2 pathway, as well as on autophagic turnover, we developed systems to monitor each of these with high sensitivity. These methods such as intracellular protein-protein interaction assay, doxycycline-inducible gene expression system, and gene expression into primary cultured cells with recombinant adenovirus revealed that some mutants, but not all, caused reduced NRF2 activation and delayed autophagic cargo turnover. In contrast, while all p62 mutants demonstrated sufficient ability to form liquid droplets, all of these droplets also exhibited reduced inner fluidity. These results indicate that like other ALS-related mutant proteins, p62 missense mutations result in a primary defect in ALS/FTD via a qualitative change in p62 liquid droplet fluidity.

A review of the impact of environmental factors and pollutants on covid-19 transmission.

The coronavirus disease (COVID-19) caused an unprecedented loss of life with colossal social and economic fallout over 237 countries and territories worldwide. Environmental conditions played a significant role in spreading the virus. Despite the availability of literature, the consecutive waves of COVID-19 in all geographical conditions create the necessity of reviewing the impact of environmental factors on it. This study synthesized and reviewed the findings of 110 previously published articles on meteorological factors and COVID-19 transmission. This study aimed to identify the diversified impacts of meteorological factors on the spread of infection and suggests future research. Temperature, rainfall, air quality, sunshine, wind speed, air pollution, and humidity were found as investigated frequently. Correlation and regression analysis have been widely used in previous studies. Most of the literature showed that temperature and humidity have a favorable relationship with the spread of COVID-19. On the other hand, 20 articles stated no relationship with humidity, and nine were revealed the negative effect of temperature. The daily number of COVID-19 confirmed cases increased by 4.86% for every 1 °C increase in temperature. Sunlight was also found as a significant factor in 10 studies. Moreover, increasing COVID-19 incidence appeared to be associated with increased air pollution, particularly PM10, PM2.5, and O3 concentrations. Studies also indicated a negative relation between the air quality index and the COVID-19 cases. This review determined environmental variables' complex and contradictory effects on COVID-19 transmission. Hence it becomes essential to include environmental parameters into epidemiological models and controlled laboratory experiments to draw more precious results.

The effects of meteorological factors on the COVID-19 omicron variant in Bangladesh.

The COVID-19 omicron variant is exceptionally complicated and uncertain due to its rapid transmission and volume of infections. This study examines the impact of climatic factors on daily confirmed cases of COVID-19 omicron variant in Bangladesh. The secondary data of daily confirmed cases from 1 January 2022, to 31 March 2022, of eight distinct geographic divisions have been used for the current study. The multivariate generalized linear negative binomial regression model was applied to determine the effects of climatic factors on omicron transmission. The model revealed that the maximum temperature (Odds: 0.67, p < 0.05), sky clearness (Odds: 0.05, p < 0.05), wind speed (Odds: 0.76, p < 0.05), relative humidity (Odds: 1.02, p < 0.05), and air pressure (Odds: 0.27, p < 0.05) significantly impacted COVID-19 omicron transmission in Bangladesh. The study's findings can assist the concerned authorities and decision-makers take necessary measures to control the spread of omicron cases in Bangladesh.

Selective autophagy.

While starvation-induced autophagy is thought to randomly degrade cellular components, under certain circumstances autophagy selectively recognizes, sequesters, and degrades specific targets via autophagosomes. This process is called selective autophagy, and it contributes to cellular homeostasis by degrading specific soluble proteins, supramolecular complexes, liquid-liquid phase-separated droplets, abnormal or excess organelles, and pathogenic invasive bacteria. This means that autophagy, like the ubiquitin-proteasome system, strictly regulates diverse cellular functions through its selectivity. In this short review, we focus on the mechanism of "selective" autophagy, which is rapidly being elucidated.

Association between the salinity level with miscarriage and unintended pregnancy in Bangladesh: Impact of salinity level on miscarriage and unintended pregnancy in Bangladesh.

Miscarriage is a significant public health concern worldwide, particularly in developing nations like Bangladesh. Moreover, people in coastal areas are more affected by miscarriage as compared to other areas. Increasing sea levels and salinity is the main reason for this discrepancy. This study aimed to investigate the association between different salinity levels (S1, S2, S3, S4, and S5) and miscarriage and unintended pregnancy. The outcome variables are pregnancy-related outcomes (miscarriage, unintended pregnancy), and the independent variables are different salinity levels. A frequency table and correlation analysis were done to find the descriptive scenarios of miscarriage, unintended pregnancy, and salinity levels. We found 621 miscarriage patients and 2271 unintended pregnant patients in our study. Furthermore, the Poisson regression model was used to observe the incidence of miscarriage and unintended pregnancy for different salinity levels. A higher amount of miscarriage and unintended pregnancy rate was found in Dhaka and Khulna, while these rates were lower in Barisal and Chittagong. However, the salinity levels were highest in Barisal and Khulna. Both miscarriage and unintended pregnancy are highly and negatively correlated with salinity levels. The Poisson regression model shows that the salinity levels s1-s5 are strongly associated with miscarriage. Lower and moderate levels of salinity are strongly associated with miscarriage than higher levels of salinity. Again, the average number of miscarriages decreases with the salinity levels. Likewise, unintended pregnancy was also negatively associated with salinity levels. However, it only reported a significant association with lower and moderate salinity levels, and higher salinity levels did not affect unintended pregnancy. Taking initiatives for raising awareness from government and non-government organizations, setting up deep tube water pumps extensively, and properly treating coastal areas women during pregnancy would be the ideal next step to reduce the miscarriage and unintended pregnancy rate in coastal zones in Bangladesh.

Systematic review and meta-analysis of the potential threats to respiratory health from microbial Bioaerosol exposures.

Infectious diseases are a part of everyday life, and acute respiratory diseases are the most common. Many agents carrying out respiratory infections are transmitted as bioaerosols through the air, usually, particulate matter containing living organisms. The purpose of the study is to conduct a systematic review and meta-analysis to assess the likelihood that people exposed to bioaerosols may experience severe respiratory diseases. Nine digital databases and bibliographies were assessed for papers conducted between January 1960 and April 2021. A total of 35 health and exposure studies were included from 825 studies for the systematic review, while only 17 contented the meta-inclusion analysis's criteria. This systematic review found higher bacterial bioaerosol concentrations in poultry farms, waste dumpsites, composting plants, and paper industries. The meta-analysis's Standard Mean Difference (SMD) measurement indicates a substantially positive association between bioaerosol exposure and respiratory disease outcomes in targeted populations. The value is 0.955 [95% CI, range 0.673-1.238; p < 0.001]. As per the Risk of Bias (ROB) findings, most of findings (30 out of 35 [85.71%]) were judged to have low ROB. From the random effect probit model, the total relative risk is 1.477 (95% CI, range 0.987-2.211), indicating a higher risk of respiratory diseases from bioaerosol exposure than the control groups. The total risk difference is 0.121 (95% CI, -0.0229 to 0.264), which means intervention groups may have a higher risk of respiratory diseases from continuous bioaerosol exposure than the control groups. The dose-response relationship revealed a strong positive linear coefficient correlation between bacterial & fungal bioaerosol exposure to respiratory health. Based on self-reported outcomes in those studies, The systematic review and meta-analysis stated that bioaerosol exposure had an effect on pulmonary health.

Socioeconomic, demographic, and cultural determinants of delivery by caesarian section in Ethiopia: Evidence from Ethiopia Mini Demographic and Health Survey 2019.

Delivery by cesarean section is a surgical procedure of delivery to a newborn baby, and the process is applied when vaginal delivery is unsafe. This study aims to identify the socioeconomic, demographic, and cultural factors that significantly impact the delivery by caesarean section. The 2019 Ethiopia Mini Demographic and Health Survey (2019 EMDHS) data were used to conduct this research, and this study considered 2872 ever-married women all over the country who delivered in the clinical setting. Firstly, a frequency distribution table has been constructed to understand the characteristics of the selected explanatory and study variables. Then Chi-square test identifies the association between various socioeconomic and demographic factors and delivery by the caesarian section. Finally, the Binary Logistic Regression was used to determine the factors that substantially impact the caesarian section among women in Ethiopia. The Chi-square test of association showed that mother's age, type of residence, highest education level, religion, socioeconomic status, total children ever born, use of contraception, age of mothers at first birth, and preceding birth interval were significantly associated with the caesarian section. The multivariate binary logistic regression analysis revealed that the mother's current age (Age Group: 31-40; Odds Ratio: 2.487, p<0.05) and religion (Muslim; Odds Ratio: 0.599, p<0.05) substantially influenced the study feature. Moreover, the highest educational level (Secondary and higher; Odds Ratio: 1.581, p<0.05), and the preceding birth interval (>40 months; Odds Ratio: 0.682, p<0.05) were also found to have considerable impacts on the caesarian section. Furthermore, the total number of children ever born (>5; Odds Ratio: 0.498, p<0.05) significantly impacts the delivery by caesarean section in Ethiopia. This study's results would be useful to policymakers to take necessary steps to reduce unnecessary delivery by caesarian section and ensure a safer newborn delivery process.

Socioeconomic and demographic risk factors of autism spectrum disorder among children and adolescents in Bangladesh: Evidence from a cross-sectional study in 2022.

Autism spectrum disorder (ASD) is the assorted uneven conditions of the human brain that lead to developmental disabilities. This cross-sectional study aimed to identify the substantial risk factors of ASD among children in Bangladesh. The data were collected using convenience sampling through a questionnaire filled up by the trained interviewers. Mann-Whitney U and Kruskal-Wallis H tests were applied as bivariate analysis, and generalized beta regression was performed to determine the significant risk factors of autism spectrum disorder. The odds ratio (OR) along with 95% confidence interval (CI) were the measuring parameters of the risk factors of ASD. The result revealed that later birth order children have more risk of ASD (OR = 1.13, CI: 1.014-1.264, p = 0.027) compared to the children whose birth order is first. Premature birth of the child (OR: 0.87, CI: 0.76-1.00, p = 0.05) and father's age (OR: 0.86, CI: 0.76-0.97, p = 0.020) substantially affects ASD. The maternal history of specific illness (diabetes, thyroiditis, and hypertension) during pregnancy also significantly affect ASD (OR: 1.34, CI: 1.14-1.61, p = 0.002). The results of this study would assist policymakers in taking necessary steps to reduce the incidence of this disorder by targeting the potential risk factors.

Perception and determinants of Social Networking Sites (SNS) on spreading awareness and panic during the COVID-19 pandemic in Bangladesh.

Introduction: The COVID-19 pandemic is an unprecedented and unique fallout worldwide and creates colossal disruption in human survival. During the pandemic, social networking sites (SNS) played a significant role in disseminating news related to the pandemic. Methods: This research is based on primary data collected from 400 successful respondents via online Google Form. Bivariate Pearson's Chi-square and multivariate binary logistic regression analysis were performed to determine the impact of the explanatory variables on the study variables. Results: This study reveals that most respondents (n = 360, 90 %) use SNS to get up-to-date news, and 72.5 % (n = 290) read health-related information. The highest number of participants (n = 386, 96.5 %) were Facebook users. Multivariate binary logistic regression reveals that "reading news on SNS" and "sharing information related to COVID-19 on social media" significantly influence the spread of awareness of COVID-19. "Unauthentic news sources" and "stop using social media to stay away from panic" also have a substantial impact on the spread of panic during the COVID-19 pandemic. Conclusion: SNS has become an inevitable medium of information carrier nowadays. Social media users are found significantly aware of the COVID-19 pandemic. The findings of this study might assist the concerned persons in taking the necessary steps to propagate authentic news and regulate appropriate policies to prevent spreading misinformation.