BackgroundPreeclampsia is a leading contributor to maternal and perinatal morbidity and mortality. In mice experiments, manganese (Mn) and selenium (Se) are protective whereas cadmium (Cd) is promotive for preeclampsia. Epidemiologic findings on these chemical elements have been inconsistent. To confirm experimental findings in mice, we examined associations of trace minerals (Mn and Se) and heavy metals (Cd, lead [Pb], and mercury [Hg]) with preeclampsia in a birth cohort.Methods and ResultsA total of 1274 women from the Boston Birth Cohort (enrolled since 1998) had complete data on the exposures and outcome. We measured Mn, Se, Cd, Pb, and Hg from red blood cells collected within 24 to 72 hours after delivery. We ascertained preeclampsia diagnosis from medical records. We used Poisson regression with robust variance models to estimate prevalence ratios (PRs) and 95% CIs. A total of 115 (9.0%) women developed preeclampsia. We observed evidence of a dose–response trend for Mn (P for trend<0.001) and to some extent for Cd (P for trend=0.009) quintiles. After multivariable adjustment, a 1 SD increment in Mn was associated with 32% lower risk of developing preeclampsia (PR=0.68; 95% CI, 0.54–0.86), whereas a 1 SD increment in Cd was associated with 15% higher risk of preeclampsia (PR=1.15; 95% CI, 0.98–1.36). Null associations were observed for Se, Pb, and Hg.ConclusionsFindings from our cohort, consistent with evidence from mice experiments and human studies, indicate that women with lower blood concentration of Mn or higher Cd are more likely to develop preeclampsia.

Purpose: Quantification of body tissue composition is important for research and clinical purposes, given the association between the presence and severity of several disease conditions, such as the incidence of cardiovascular and metabolic disorders, survival after chemotherapy, etc., with the quantity and quality of body tissue composition. In this work, we aim to automatically segment four key body tissues of interest, namely subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, and skeletal structures from body-torso-wide low-dose computed tomography (CT) images. Method: Based on the idea of residual Encoder-Decoder architecture, a novel neural network design named ABCNet is proposed. The proposed system makes full use of multiscale features from four resolution levels to improve the segmentation accuracy. This network is built on a uniform convolutional unit and its derived units, which makes the ABCNet easy to implement. Several parameter compression methods, including Bottleneck, linear increasing feature maps in Dense Blocks, and memory-efficient techniques, are employed to lighten the network while making it deeper. The strategy of dynamic soft Dice loss is introduced to optimize the network in coarse-to-fine tuning. The proposed segmentation algorithm is accurate, robust, and very efficient in terms of both time and memory. Results: A dataset composed of 38 low-dose unenhanced CT images, with 25 male and 13 female subjects in the age range 31-83 yr and ranging from normal to overweight to obese, is utilized to evaluate ABCNet. We compare four state-of-the-art methods including DeepMedic, 3D U-Net, V-Net, Dense V-Net, against ABCNet on this dataset. We employ a shuffle-split fivefold cross-validation strategy: In each experimental group, 18, 5, and 15 CT images are randomly selected out of 38 CT image sets for training, validation, and testing, respectively. The commonly used evaluation metricsprecision, recall, and F1-score (or Dice)are employed to measure the segmentation quality. The results show that ABCNet achieves superior performance in accuracy of segmenting body tissues from body-torso-wide low-dose CT images compared to other state-of-the-art methods, reaching 92-98% in common accuracy metrics such as F1-score. ABCNet is also time-efficient and memory-efficient. It costs about 18 h to train and an average of 12 sec to segment four tissue components from a body-torso-wide CT image, on an ordinary desktop with a single ordinary GPU. Conclusions: Motivated by applications in body tissue composition quantification on large population groups, our goal in this paper was to create an efficient and accurate body tissue segmentation method for use on body-torso-wide CT images. The proposed ABCNet achieves peak performance in both accuracy and efficiency that seems hard to improve any more. The experiments performed demonstrate that ABCNet can be run on an ordinary desktop with a single ordinary GPU, with practical times for both training and testing, and achieves superior accuracy compa...

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Background Preeclampsia is a major cause of maternal and fetal morbidity and mortality. Given its large public health burden, there is a need to identify modifiable factors that can be targeted for preeclampsia prevention. In this study, we examined whether a Mediterranean‐style diet is protective for preeclampsia in a large cohort of racially and ethnically diverse, urban, low‐income women. Methods and Results We used data from the Boston Birth Cohort. Maternal sociodemographic and dietary data were obtained via interview and food frequency questionnaire within 24 to 72 hours postpartum, respectively. Additional clinical information, including physician diagnoses of preexisting conditions and preeclampsia, were extracted from medical records. We derived a Mediterranean‐style diet score from the food frequency questionnaire and performed logistic regression to examine the association of the Mediterranean‐style diet score with preeclampsia. Of 8507 women in the sample, 848 developed preeclampsia. 47% were Black, 28% were Hispanic, and the remaining were White/Other. After multivariable adjustment, greatest adherence with MSD was associated with lower preeclampsia odds (adjusted odds ratio comparing tertile 3 to tertile 1, 0.78; 95% CI, 0.64–0.96). A subgroup analysis of Black women demonstrated a similar benefit with an adjusted odds ratio comparing tertile 3 to tertile 1 of 0.74 (95% CI, 0.76–0.96). Conclusions Self‐report of higher adherence to a Mediterranean‐style diet is associated with lower preeclampsia odds, and benefit of this diet is present among Black women as well.

Context Sex hormones have been linked with presence and severity of nonalcoholic fatty liver disease (NAFLD) in adults, but it is unknown if they impact severity of pediatric NAFLD Objective To examine associations of circulating sex hormone binding globulin (SHBG), estrogens, and androgens with key histologic features of pediatric, biopsy-confirmed NAFLD Design Baseline assessment of longitudinal cohorts and randomized clinical trials Setting Nonalcoholic Steatohepatitis Clinical Research Network Patients Children and adolescents ≤ 18 years with liver biopsy-confirmed NAFLD in U.S Main Outcome Measures We assayed SHBG, estrone, estradiol, dehydroepiandrosterone (DHEAS), androstenedione, and testosterone in relation to grade/stage of steatosis, portal inflammation, hepatic ballooning, fibrosis, and nonalcoholic steatohepatitis (NASH) severity using linear regression Results Mean age of 573 children at the time of biopsy was 13.1 years (SD 2.8). Lower SHBG was inversely associated with steatosis severity in boys and girls (P = 0.001), and with portal inflammation in girls only (P for sex interaction &lt; 0.001). Higher testosterone was related to improved features of steatosis and fibrosis (P for sex interaction = 0.003 and 0.01, respectively) in boys, but detrimental in girls. In boys and girls, higher estrone, estradiol and testosterone were associated with lower portal inflammation grade; higher estradiol was positively associated with hepatic ballooning severity; DHEAS was inversely associated with hepatic ballooning and NASH severity (all P &lt; 0.05). Androstenedione was not associated with NAFLD features Conclusions Largely consistent with findings in adults, sex hormones are associated with distinct histologic features of NAFLD in children and adolescents. These hormone levels relate to differences with gender and pubertal change

Background Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end‐to‐end deep learning (DL) networks, are weak in garnering high‐level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge. Purpose We formulate a hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation. Methods The system employs five modules: (i) body region recognition, which automatically trims a given image to a precisely defined target body region; (ii) NI‐based automatic anatomy recognition object recognition (AAR‐R), which performs object recognition in the trimmed image without DL and outputs a localized fuzzy model for each object; (iii) DL‐based recognition (DL‐R), which refines the coarse recognition results of AAR‐R and outputs a stack of 2D bounding boxes (BBs) for each object; (iv) model morphing (MM), which deforms the AAR‐R fuzzy model of each object guided by the BBs output by DL‐R; and (v) DL‐based delineation (DL‐D), which employs the object containment information provided by MM to delineate each object. NI from (ii), AI from (i), (iii), and (v), and their combination from (iv) facilitate the HI system. Results The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers. Data sets from one separate independent institution involving 125 patients were employed in training/model building for each of the two body regions, whereas 104 and 110 data sets from the 4 RT centers were utilized for testing on neck and thorax, respectively. In the testing data sets, 83% of the images had limitations such as streak artifacts, poor contrast, shape distortion, pathology, or implants. The contours output by the HI system were compared to contours drawn in clinical practice at the four RT centers by utilizing an independently established ground‐truth set of contours as reference. Three sets of measures were employed: accuracy via Dice coefficient (DC) and Hausdorff boundary distance (HD), subjective clinical acceptability via a blinded reader study, and efficiency by measuring human time saved in contouring by the HI system. Overall, the HI system achieved a mean DC of 0.78 and 0.87 and a mean HD of 2.22 and 4.53 mm for neck and thorax, respectively. It significantly outperformed clinical contouring in accuracy and saved overall 70% of human time over clinical contouring time, whereas acceptability scores varied significantly from site to site for both auto‐contours and clinically drawn contours. Conclusions The HI system is observed to behave like an expert human in robustness in the contouring task but vast...

Given the diversity of sex, gender identity, race, ethnicity, and socioeconomic position (SEP) in children across the US, it is incumbent upon pediatric and epidemiologic researchers to conduct their work in ways that promote inclusivity, understanding and reduction in inequities. Current child health research often utilizes an approach of “convenience” in how data related to these constructs are collected, categorized and included in models; the field needs to be more systematic and thoughtful in its approach to understand how sociodemographics affect child health. We offer suggestions for improving the discourse around sex, gender identity, race, ethnicity and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses, without the automatic “adjusting for” all sociodemographic constructs. We propose to leverage newly available data from large multi-cohort consortia as unique opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs. Improving the characterization and interpretation of child health studies with regards to core sociodemographic constructs is critical for optimizing child health and reducing inequities in the health and well-being of all children across the US.

BACKGROUND: In utero exposure to heavy metals lead (Pb), mercury (Hg), and cadmium (Cd) may be associated with higher childhood blood pressure (BP), whereas trace elements selenium (Se) and manganese (Mn) may have protective antioxidant effects that modify metal-BP associations. OBJECTIVES: We examined the individual and joint effects of in utero exposure to Pb, Hg, Cd, Se, and Mn on childhood BP. METHODS: We used data from the Boston Birth Cohort (enrolled 2002Cohort (enrolled -2013. We measured heavy metals and trace elements in maternal red blood cells collected 24-72 h after delivery. We calculated child BP percentile per the 2017 American Academy of Pediatrics Clinical Practice Guideline. We used linear regression models to estimate the association of each metal, and Bayesian kernel machine regression (BKMR) to examine metal coexposures, with child BP between 3 to 15 years of age. RESULTS: Our analytic sample comprised 1,194 mother-infant pairs (61% non-Hispanic Black, 20% Hispanic). Hg and Pb were not associated with child systolic BP (SBP). Se and Mn were inversely associated with child SBP percentiles, which, on average, were 6.23 points lower with a doubling of Se (95% CI: −11:51, −0:96) and 2.62 points lower with a doubling of Mn (95% CI: −5:20, −0:04). BKMR models showed similar results. Although Cd was not associated with child SBP overall, the inverse association between Mn and child SBP was stronger at higher levels of Cd (p-interaction = 0:04). Consistent with this finding, in utero exposure to cigarette smoke modified the Mn-child SBP association. Among children whose mothers smoked during pregnancy, a doubling of Mn was associated with a 10.09-point reduction in SBP percentile (95% CI: −18:03, −2:15), compared with a 1.49-point reduction (95% CI: −4:21, 1.24) in children whose mothers did not smoke during pregnancy (p-interaction = 0:08). CONCLUSION: Se and Mn concentrations in maternal red blood cells collected 24-72 h after delivery were associated with lower child SBP at 3 to 15 years of age. There was an interaction between Mn and Cd on child SBP, whereby the protective association of Mn on child SBP was stronger among mothers who had higher Cd. The association of Mn and child SBP was also modified by maternal cigarette smoking-a source of Cd-during pregnancy. Optimizing in utero Se levels, as well as Mn levels in women who had high Cd or smoked during pregnancy, may protect offspring from developing high BP during childhood.