Medicine
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Prediction models were developed to assess the risk of cardiovascular disease (CVD) based on micronutrient intake, utilizing data from 90,167 UK Biobank participants. Four machine learning models were employed to predict CVD risk, with performance evaluation metrics including area under the receiver operating characteristic curve (AUC), accuracy, recall, specificity, and F1-score. The eXtreme Gradient Boosting (XGBoost) model was utilized to rank the importance of 11 micronutrients in cardiovascular health. ⋯ The XGBoost model demonstrated the highest performance with an AUC of 0.952, highlighting potassium, vitamin E, and vitamin C as key predictors of CVD risk. Subgroup analysis revealed a stronger correlation between calcium intake and CVD risk in older adults and those with higher BMI, while vitamin B6 intake showed a link to CVD risk in women. Overall, the XGBoost model emphasized the significance of potassium, vitamin E, and vitamin C intake as primary predictors of CVD risk in adults, with age, sex, and BMI potentially influencing the importance of micronutrient intake in predicting CVD risk.
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Evaluate the relationship between blood lead (Pb) levels and other biomedical markers and the risk of diabetes in gasoline station workers. The participants were separated into 2 groups: group A consisted of 26 workers from gasoline filling stations, while group B comprised 26 healthy individuals. Serum levels of malondialdehyde, IL-1β, visfatin, insulin, fasting blood sugar, and vitamin D were assessed. ⋯ The levels of visfatin (23.19 ± 0.96 vs 3.88 ± 0.58, ng/mL), insulin (22.14 ± 1.31 vs 11.26 ± 0.75, mU/L), fasting blood sugar (118.4 ± 26.1 vs 82.7 ± 9.2, gm/dL), malondialdehyde (6.40 ± 0.27 vs 1.62 ± 0.21, nmol/mL), and IL-1β (330.25 ± 10.34 vs 12.35 ± 1.43, pg/mL) were significantly higher in group A, meanwhile; vitamin D (11.99 ± 1.55 vs 35.41 ± 3.16, ng/mL) were significantly lower in group A. A positive association exists between blood Pb levels and increased inflammatory markers. Lead exposure increases serum insulin and fasting blood sugar, which suggests that it is diabetogenic and that increased inflammation is a possible cause.
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Observational Study
Predictors of health-related quality of life (HRQoL) for caregivers of children with developmental disabilities in Saudi Arabia: An observational study.
To examine predictors of health-related quality of life (HRQoL) for caregivers of children with developmental disabilities, a cross-sectional design was used. Participants were primary caregivers of children with developmental disabilities. Caregivers completed a demographic form about the child and the family, and the Arabic version of Patient-Reported Outcomes Measurement Information System-Profile 29 (PROMIS-29 v2.0). ⋯ However, the models did not significantly predict depression, fatigue, or sleep disturbance, P > .05. HRQoL is a complex construct and is influenced by multiple child and family factors. Implications of the study emphasize the importance of regular HRQoL screening for caregivers, the development of efficient referral systems for support services, and the exploration of respite care options.
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The association between interleukins and osteoporosis has attracted much attention these days. However, the causal relationship between them is uncertain. Hence, this study performed a Mendelian randomization (MR) analysis to investigate the causal effects of interleukins on osteoporosis. ⋯ In the sensitivity analysis, the results did not show the presence of pleiotropy and heterogeneity. Therefore, the results were robust for the MR analysis. This study revealed that interleukin-7 was positively related to osteoporosis and that other interleukins were not related to osteoporosis.
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Observational Study
Bioinformatics analysis of the tumor microenvironment in melanoma - Constructing a prognostic model based on CD8+ T cell-related genes: An observational study.
This research endeavor seeks to explore the microenvironment of melanoma tumors and construct a prognostic model by focusing on genes specific to CD8+ T cells. The single-cell sequencing data of melanoma underwent processing with the Seurat package, subsequent to which cell communication network analysis was conducted using the iTALK package and transcription factor analysis was performed using the SCENIC package. Univariate COX and LASSO regression analyses were utilized to pinpoint genes linked to the prognosis of melanoma patients, culminating in the creation of a prognostic model through multivariate COX analysis. ⋯ The findings provide valuable insights into the tumor microenvironment of melanoma and establish a reliable prognostic model. The integration of multi-omics and immune infiltration analyses enhances our understanding of the pathogenesis of melanoma. The identification of specific genes holds promise as potential biomarkers for individuals with melanoma, serving as important indicators for predicting patient outcomes and determining their response to immunotherapy.