Ying Huang

Background/Objectives: Cardiovascular diseases are the primary cause of global morbidity and mortality, with cardiovascular health (CVH) remaining well below the ideal level and showing minimal improvement in the U.S. population over recent years. Bisphenol A (BPA), a pervasive environmental contaminant, has emerged as a potential contributor to adverse cardiovascular outcomes. This cross-sectional study delves into the impact of BPA exposure on achieving optimal CVH, as assessed by the Life’s Essential 8 metric, among U.S. adults. Methods: Analyzing data from 6635 participants in the National Health and Nutrition Examination Survey (NHANES) collected between 2005 and 2016, BPA exposure was quantified through urinary BPA levels, while optimal CVH was defined using the American Heart Association’s Life’s Essential 8 criteria, scoring between 80 and 100. Multivariable logistic regression and propensity score matching were employed to evaluate the association between BPA exposure and CVH. Results: This study reveals that individuals in the highest tertile of urinary BPA levels were 27% less likely to attain optimal CVH compared with those in the lowest tertile (OR, 0.73; 95% CI: 0.59–0.92). This negative association persisted across diverse demographics, including age, sex, and race, mirrored in the link between urinary BPA levels and health factor scores. Conclusions: The findings underscore the potential benefits of reducing BPA exposure in enhancing the prevalence of optimal CVH and mitigating the burden of cardiovascular disease. Given the widespread use of BPA, ongoing monitoring of BPA’s impact on CVH is essential. Further studies are necessary to elucidate the long-term and causative connections between BPA and CVH. These insights contribute to understanding the complex interplay between environmental factors and CVH outcomes, informing targeted interventions to mitigate cardiovascular disease risk within the population.

Background: Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features. Methods: CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets. Results: The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R2 values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively. Conclusions: The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.