Background and objectives Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an ...automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia. Materials and methods The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB) were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN. Results The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN. Conclusions The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.
Low birth weight (LBW) is a major risk factor of child mortality and morbidity during infancy (0-3 years) and early childhood (3-8 years) in low and lower-middle-income countries, including ...Bangladesh. LBW is a vital public health concern in Bangladesh. The objective of the research was to investigate the socioeconomic inequality in the prevalence of LBW among singleton births and identify the significantly associated determinants of singleton LBW in Bangladesh. The data utilized in this research was derived from the latest nationally representative Bangladesh Demographic and Health Survey, 2017-18, and included a total of 2327 respondents. The concentration index (C-index) and concentration curve were used to investigate the socioeconomic inequality in LBW among the singleton newborn babies. Additionally, an adjusted binary logistic regression model was utilized for calculating adjusted odds ratio and p-value (<0.05) to identify the significant determinants of LBW. The overall prevalence of LBW among singleton births in Bangladesh was 14.27%. We observed that LBW rates were inequitably distributed across the socioeconomic groups (C-index: -0.096, 95% confidence interval: -0.175, -0.016, P = 0.029), with a higher concentration of LBW infants among mothers living in the lowest wealth quintile (poorest). Regression analysis revealed that maternal age, region, maternal education level, wealth index, height, age at 1st birth, and the child's aliveness (alive or died) at the time of the survey were significantly associated determinants of LBW in Bangladesh. In this study, socioeconomic disparity in the prevalence of singleton LBW was evident in Bangladesh. Incidence of LBW might be reduced by improving the socioeconomic status of poor families, paying special attention to mothers who have no education and live in low-income households in the eastern divisions (e.g., Sylhet, Chittagong). Governments, agencies, and non-governmental organizations should address the multifaceted issues and implement preventive programs and policies in Bangladesh to reduce LBW.
Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their ...growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction. This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy. The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight. This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh's U5 children.
BackgroundTobacco production continues to increase in low-income and middle-income countries including in Bangladesh. It has spreads to different parts of Bangladesh and is now threatening food ...cultivation, the environment and health. The aim of this study is to determine the factors those are influenced farmers’ decisions to grow tobacco.MethodsWe surveyed 371 tobacco farmers using a simple random sampling in the Meherpur district of Bangladesh. Binary logistic regression was used to examine the variables affecting farmers’ decision to cultivate tobacco.ResultsApproximately 87.0% of the respondents were contract farmers with different tobacco companies. Almost 83.3% of the farmers had intentions to continue tobacco farming. Binary logistic regression results suggest that company’s incentives to farmers, farmers’ profitability, a guaranteed market for the tobacco crop and economic viability were the variables most affecting the decision to cultivate tobacco.ConclusionsGovernments seeking to shift farmers away from tobacco will need to consider how to address the dynamics revealed in this research.
Hepatitis B virus (HBV) infection is a leading cause of liver diseases including cirrhosis and hepatocellular carcinoma. Human leukocyte antigens (HLAs) play an important role in the regulation of ...immune response against infectious organisms, including HBV. Recently, several genome-wide association (GWAS) studies have shown that genetic variations in HLA genes influence disease progression in HBV infection. The aim of this study was to investigate the role of HLA genetic polymorphisms and their possible role in HBV infection in Saudi Arabian patients. Variations in HLA genes were screened in 1672 subjects who were divided according to their clinical status into six categories as follows; clearance group, inactive carriers, active carriers, cirrhosis, hepatocellular carcinoma (HCC) patients and uninfected healthy controls. Three single nucleotide polymorphisms (SNPs) belonged to HLA-DQ region (rs2856718, rs7453920 and rs9275572) and two SNPs belonged to HLA-DP (rs3077 and rs9277535) were studied. The SNPs were genotyped by PCR-based DNA sequencing (rs2856718) and allele specific TaqMan genotyping assays (rs3077, rs7453920, rs9277535 and rs9275572). The results showed that rs2856718, rs3077, rs9277535 and rs9275572 were associated with HBV infection (p = 0.0003, OR = 1.351, CI = 1.147-1.591; p = 0.041, OR = 1.20, CI = 1.007-1.43; p = 0.045, OR = 1.198, CI = 1.004-1.43 and p = 0.0018, OR = 0.776, CI = 0.662-0.910, respectively). However, allele frequency of rs2856718, rs7453920 and rs9275572 were found more in chronically infected patients when compared to clearance group infection (p = 0.0001, OR = 1.462, CI = 1.204-1.776; p = 0.0178, OR = 1.267, CI = 1.042-1.540 and p = 0.010, OR = 0.776, CI = 0.639-0.942, respectively). No association was found when polymorphisms in HLA genes were compared in active carriers versus cirrhosis/HCC patients. In conclusion, these results suggest that variations in HLA genes could affect susceptibility to and clearance of HBV infection in Saudi Arabian patients.
•A cross-sectional survey was conducted among 400 people aged ≥65 years to determine the associated risk factors for depressive symptoms among older people in Bangladesh.•The prevalence of depressive ...symptoms was 55.5%, and 23.0% mild, 19.0% moderate, and 13.5% having severe levels of depressive symptoms.•Older age, sex, residence, marital status, presence of co-morbidities, visual impairment, previous falls, loneliness, and fear of falling were the significant determinants for developing depressive symptoms.
Depressive symptoms are common among older people which are associated with disability, morbidity and mortality. The aim of this study was to determine the associated risk factors for depressive symptoms among older people in Bangladesh.
A cross-sectional survey was conducted among 400 people aged ≥65 years from the Meherpur district in Bangladesh. Depressive symptoms were measured by the 15-item Geriatric Depression Scale and categorized into: no depressive symptoms, mild, moderate and severe depressive symptoms. Information was also collected on socio-economic and demographic characteristics, health problems, feeling of loneliness, history of falls and concern about falling. Chi-square test of association and multinomial logistic regression was performed to reveal the determinants of depressive symptoms.
Just over half of the sample were female, aged 70+ years, and lived in rural areas. The prevalence of depressive symptoms was 55.5%, and 23.0% mild, 19.0% moderate, and 13.5% having severe levels of depressive symptoms. Older age, sex, residence, marital status, presence of co-morbidities, visual impairment, previous falls, loneliness, and fear of falling were the significant determinants for developing depressive symptoms.
A convenience sampling method was used for data collection among older people from selected communities in a district of Bangladesh. The results do not represent the entire population of Bangladesh. Besides, it was a cross-sectional study, and causality cannot be determined.
Depressive symptoms among older people in Bangladesh is prevalent, and needs to be addressed. Public health programs and strategies are needed to reduce depressive symptoms among older adults in Bangladesh.
The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large ...volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.
Equine influenza is a major cause of respiratory infections in horses and can spread rapidly despite the availability of commercial vaccines. This study aimed to screen the incidence of equine ...influenza virus (EIV) and molecularly characterize the haemagglutinin and neuraminidase from positive EIV field samples collected from Saudi Arabia. Six-hundred twenty-one horses from 57 horse barns were screened for the presence of the clinical signs, suggestive for equine influenza, from different parts of Saudi Arabia. Nasopharyngeal swabs were collected from each horse showing respiratory distress. Samples from the same horse barn were pooled together and screened for the presence of the influenza A virus using quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR). Selective positive samples were subjected to full-length genome sequencing using MiSeq Illumina. Out of the total 57 pools, 39 were found positive to EIV using qRT-PCR. Full-length gene sequences were compared with representative EIV strains selected from the GenBank database. Phylogenetic analysis of the HA and NA genes revealed that the identified virus strains belong to H3N8 clade 1 of the Florida sublineage and were very similar to viruses identified in USA in 2019, with no current evidence for reassortment. This is one of the first reports providing detailed description and characterization of EIVs in Saudi Arabia. Detailed surveillance and genetic information sharing could allow genetic evolution of equine influenza viruses to be monitored more effectively on a global basis and aid in refinement of vaccine strain selection for EIV.
Diabetic retinopathy (DR) is a global health concern among diabetic patients. The objective of this study was to propose an explainable machine learning (ML)-based system for predicting the risk of ...DR.
This study utilized publicly available cross-sectional data in a Chinese cohort of 6374 respondents. We employed boruta and least absolute shrinkage and selection operator (LASSO) based feature selection methods to identify the common predictors of DR. Using the identified predictors, we trained and optimized four widly applicable models (artificial neural network, support vector machine, random forest, and extreme gradient boosting (XGBoost) to predict patients with DR. Moreover, shapely additive explanation (SHAP) was adopted to show the contribution of each predictor of DR in the prediction.
Combining Boruta and LASSO method revealed that community, TCTG, HDLC, BUN, FPG, HbAlc, weight, and duration were the most important predictors of DR. The XGBoost-based model outperformed the other models, with an accuracy of 90.01%, precision of 91.80%, recall of 97.91%, F1 score of 94.86%, and AUC of 0.850. Moreover, SHAP method showed that HbA1c, community, FPG, TCTG, duration, and UA1b were the influencing predictors of DR.
The proposed integrating system will be helpful as a tool for selecting significant predictors, which can predict patients who are at high risk of DR at an early stage in China.
Viral mutations acquired during the course of chronic hepatitis B virus (HBV) infection are known to be associated with the progression and severity of HBV-related liver disease. This study of ...HBV-infected Saudi Arabian patients aimed to identify amino acid substitutions within the precore/core (preC/C) region of HBV, and investigate their impact on disease progression toward hepatocellular carcinoma (HCC). Patients were categorized according to the severity of their disease, and were divided into the following groups: inactive HBV carriers, active HBV carriers, liver cirrhosis patients, and HCC patients. Two precore mutations, W28
and G29D, and six core mutations, F24Y, E64D, E77Q, A80I/T/V, L116I, and E180A were significantly associated with the development of cirrhosis and HCC. Six of the seven significant core mutations that were identified in this study were located within immuno-active epitopes; E77Q, A80I/T/V, and L116I were located within B-cell epitopes, and F24Y, E64D, and V91S/T were located within T-cell epitopes. Multivariate risk analysis confirmed that the core mutations A80V and L116I were both independent predictors of HBV-associated liver disease progression. In conclusion, our data show that mutations within the preC/C region, particularly within the immuno-active epitopes, may contribute to the severity of liver disease in patients with chronic hepatitis. Furthermore, we have identified several distinct preC/C mutations within the study population that affect the clinical manifestation and progression of HBV-related disease. The specific identity of HBV mutations that are associated with severe disease varies between different ethnic populations, and so the specific preC/C mutations identified here will be useful for predicting clinical outcomes and identifying the HBV-infected patients within the Saudi population that are at high risk of developing HCC.
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