Risk assessment and environmental determinants of urinary phthalate metabolites in pregnant women in Southwest China.

Pregnant women are widely exposed to phthalic acid esters (PAEs) that are commonly used in most aspects of modern life. However, few studies have examined the cumulative exposure of pregnant women to a variety of PAEs derived from the living environmental conditions in China. Therefore, this study aimed to determine the urinary concentrations of nine PAE metabolites in pregnant women, examine the relationship between urinary concentrations and residential characteristics, and conduct a risk assessment analysis. We included 1,888 women who were in their third trimester of pregnancy, and we determined their urinary concentrations of nine PAE metabolites using high-performance gas chromatography-mass spectrometry. The risk assessment of exposure to PAEs was calculated based on the estimated daily intake. A linear regression model was used to analyze the relationship between creatinine-adjusted PAE metabolite concentrations and residential characteristics. The detection rate of five PAE metabolites in the study population was > 90%. Among the PAE metabolites adjusted by creatinine, the urinary metabolite concentration of monobutyl phthalate was found to be the highest. Residential factors, such as housing type, proximity to streets, recent decorations, lack of ventilation in the kitchen, less than equal to three rooms, and the use of coal/kerosene/wood/wheat straw fuels, were all significantly associated with high PAE metabolite concentrations. Due to PAE exposure, ~ 42% (n = 793) of the participants faced potential health risks, particularly attributed to dibutyl phthalate, diisobutyl phthalate, and di(2-ethyl)hexyl phthalate exposure. Living in buildings and using coal/kerosene/wood/wheat straw as domestic fuel can further increase the risks.

Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis.

OBJECTIVES: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. METHODS: A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. RESULTS: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. CONCLUSIONS: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs. KEY POINTS: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.

An automated feeding system for the African killifish reveals the impact of diet on lifespan and allows scalable assessment of associative learning.

The African turquoise killifish is an exciting new vertebrate model for aging studies. A significant challenge for any model organism is the control over its diet in space and time. To address this challenge, we created an automated and networked fish feeding system. Our automated feeder is designed to be open-source, easily transferable, and built from widely available components. Compared to manual feeding, our automated system is highly precise and flexible. As a proof of concept for the feeding flexibility of these automated feeders, we define a favorable regimen for growth and fertility for the African killifish and a dietary restriction regimen where both feeding time and quantity are reduced. We show that this dietary restriction regimen extends lifespan in males (but not in females) and impacts the transcriptomes of killifish livers in a sex-specific manner. Moreover, combining our automated feeding system with a video camera, we establish a quantitative associative learning assay to provide an integrative measure of cognitive performance for the killifish. The ability to precisely control food delivery in the killifish opens new areas to assess lifespan and cognitive behavior dynamics and to screen for dietary interventions and drugs in a scalable manner previously impossible with traditional vertebrate model organisms.