METHODS AND RESULTS: The amplification of genomic DNA with 32 ISSR markers detected an average of 97.64% polymorphism while 35.15% and 51.08% polymorphism per population and geographical zone, respectively. Analysis of molecular variance revealed significant variation within population 75% and between population 25% whereas within region 84% and between region 16%. The Bidillali exposed greater number of locally common band i.e., NLCB (≤ 25%) = 25 and NLCB (≤ 50%) = 115 were shown by Cancaraki while the lowest was recorded as NLCB (≤ 25%) = 6 and NLCB (≤ 50%) = 72 for Roko and Maibergo, accordingly. The highest PhiPT value was noted between Roko and Katawa (0.405*) whereas Nei's genetic distance was maximum between Roko and Karu (0.124). Based on Nei's genetic distance, a radial phylogenetic tree was constructed that assembled the entire accessions into 3 major clusters for further confirmation unrooted NJ vs NNet split tree analysis based on uncorrected P distance exposed the similar result. Principal coordinate analysis showed variation as PC1 (15.04%) > PC2 (5.81%).
CONCLUSIONS: The current study leads to prompting the genetic improvement and future breeding program by maximum utilization and better conservation of existing accessions. The accessions under Cancaraki and Jatau are population documented for future breeding program due to their higher genetic divergence and homozygosity.
OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.
METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.
RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.
CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.
OBJECTIVE: Explore the relationship between domestic violence on the decision-making power of married women in Myanmar.
DESIGN: Cross-sectional.
SETTING: National, both urban and rural areas of Myanmar.
PATIENTS AND METHODS: Data from the Myanmar Demographic and Health Survey 2015-16 were used in this analysis. In that survey, married women aged between 15 to 49 years were selected for interview using a multistage cluster sampling technique. The dependent variables were domestic violence and the decision-making power of women. Independent variables were age of the respondents, educational level, place of residence, employment status, number of children younger than 5 years of age and wealth index.
MAIN OUTCOME MEASURES: Domestic violence and decision-making power of women.
SAMPLE SIZE: 7870 currently married women.
RESULTS: About 50% respondents were 35 to 49 years of age and the mean (SD) age was 35 (8.4) years. Women's place of residence and employment status had a significant impact on decision-making power whereas age group and decision-making power of women had a relationship with domestic violence.
CONCLUSION: Giving women decision making power will be indispensable for the achievement of sustainable development goals. Government and other stakeholders should emphasize this to eliminate violence against women.
LIMITATIONS: Use of secondary data analysis of cross-sectional study design and cross-sectional studies are not suitable design to assess this causality. Secondly the self-reported data on violence may be subject to recall bias.
CONFLICT OF INTEREST: None.
DESIGN: Systematic review and regression analysis.
ELIGIBILITY: Medication adherence levels studied at primary, secondary and tertiary care settings. Self-reported measures with scoring methods were included. Studies without proxy measures were excluded.
DATA SOURCES: Using detailed searches with key concepts including questionnaires, reliability and validity, and restricted to English, MEDLINE, EMBASE, CINAHL, International Pharmaceutical Abstracts, and Cochrane Library were searched until 01 March 2022. Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA-2020) checklist was used.
DATA ANALYSIS: Risk of bias was assessed via COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN-2018) guidelines. Narrative synthesis aided by graphical figures and statistical analyses.
OUTCOME MEASURES: Process domains [behaviour (e.g., self-efficacy), barrier (e.g., impaired dexterity) or belief (e.g., perception)], and overall outcome domains of either intentional (I), unintentional (UI), or mixed non-adherence.
RESULTS: Paper summarises evidence from 59 studies of PROMs, validated among patients aged 18-88 years in America, the United Kingdom, Europe, Middle East, and Australasia. PROMs detected outcome domains: intentional non-adherence, n=44 (I=491 criterion items), mixed intentionality, n=13 (I=79/UI=50), and unintentional, n=2 (UI=5). Process domains detected include belief (383 criterion items), barrier (192) and behaviour (165). Criterion validity assessment used proxy measures (biomarkers, e-monitors), and scoring was ordinal, dichotomised, or used Visual Analogue Scale. Heterogeneity was revealed across psychometric properties (consistency, construct, reliability, discrimination ability). Intentionality correlated positively with negative beliefs (r(57)=0.88) and barriers (r(57)=0.59). For every belief or barrier criterion-item, PROMs' aptitude to detect intentional non-adherence increased by β=0.79 and β=0.34 units, respectively (R2=0.94). Primary care versus specialised care predicted intentional non-adherence (OR 1.9; CI 1.01 to 2.66).
CONCLUSIONS: Ten PROMs had adequate psychometric properties. Of the ten, eight PROMs were able to detect total, and two PROMs were able to detect partial intentionality to medication default. Fortification of patients' knowledge and illness perception, as opposed to daily reminders alone, is most imperative at primary care levels.