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The environmental obesogen hypothesis proposes that exposure to endocrine disruptors during developmental "window" contributes to adipogenesis and the development of obesity. Implication of environmental endocrine disruptor such as diethyl-hexyl-phthalate (DEHP) on adipose tissue development has been poorly investigated. Here, we evaluated the effects of DEHP on adipocyte differentiation in vitro and in vivo, and explored potential mechanism involved in its action. DEHP had no effect on adipocyte differentiation in the murine 3T3-L1 cell model, whereas DEHP induced the expression of transcriptional factors peroxisome proliferator-activated receptor (PPAR) gamma, CCAAT/enhancer-binding protein (C/EBP) alpha and sterol regulatory element binding factor 1 (Srebf1) as well as downstream target genes required for adipogenesis in vivo. Furthermore, perinatal exposure to DEHP had an impact on filial adipogenesis. Body weight, adipose tissue deposition, serum lipids and glucose levels were significantly elevated in offspring at postnatal day (PND) 60. Therefore, these results suggested that perinatal exposure to DEHP might be expected to increase the incidence of obesity in offspring and could act as a potential chemical stressor for obesity and obesity-related disorders.

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Background: The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver. Methods: A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Radscore) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness. Results: Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934-0.995) and the validation set (AUC, 0.865; 95% CI, 0.725-1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959-0.998) and the validation set (AUC, 0.917; 95% CI, 0.800-1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719-0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness. Conclusions: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the noncirrhotic liver, which might facilitate clinical decision-making process.

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