The variation in normal TSH levels is partially related to the lipid components and hypercholesterolemia in euthyroid subjects and includes both TH-dependent and TH-independent effects. Our study suggests the importance of controlling TSH in hypothyroid subjects.

Galangin (3,5,7‑trihydroxyflavone), a natural flavonoid present in plants, has been reported to possess anticancer properties in various types of cancers comprising glioma. The underlying mechanism, however, has not been fully elucidated. CD44, a hall marker in glioma, has been reported to be associated with epithelial-mesenchymal transition (EMT) and angiogenesis, which play important roles in glioma progression. In this study, we aimed to investigate whether galangin can inhibit EMT, angiogenesis and CD44 expression in glioma. We observed that galangin inhibited the proliferation, migration, invasion and angiogenesis of glioma cells in a dose-dependent manner, suppressed the expression of CD44 and inhibited angiogenesis of glioma cells through downregulating vascular endothelial growth factor (VEGF) in HUVECs. In addition, the overexpression of CD44 in U87 and U251 cells partly abolished the effects of galangin on glioma cells. Moreover, galangin suppressed tumor growth in an intracranial glioma mouse model. These results indicate that galangin is a potential novel drug for glioblastoma treatment due to its ability to suppress of CD44, EMT and angiogenesis.

Nonsteroidal antiinflammatory drugs (NSAIDs) are widely used for their antiinflammatory, antipyretic, and analgesic properties. The molecular basis for the therapeutic action of NSAIDs is believed to be in their ability to inhibit cyclooxygenase (COX) activity and thereby blocking the production of prostaglandins. Emerging evidence now suggests that NSAIDs can exert their pharmacological effects through other mechanisms. This study investigated the influence of a nonselective COX-inhibitor ketorolac on IL-1beta- and TNFalpha-induced expression of proinflammatory genes in the brain. Systemic injection of both cytokines caused a rapid and transient transcriptional activation of COX-2 gene within the cerebral microvasculature, which was significantly enhanced by ketorolac. Expression of genes encoding the index of nuclear factor kappaB activity and the chemokine monocyte chemoattractant protein-1 was also increased by the NSAID. We speculated here that such effect was indirectly mediated via an altered secretion of plasma glucocorticoids because ketorolac is a potent inhibitor of the hypothalamic-pituitary-adrenal axis during systemic inflammation. As expected, pretreatment with the glucocorticoid receptor antagonist RU-486 exacerbated the influence of systemic immune stimuli on proinflammatory signaling. In contrast, exogenous corticosterone abolished the effects of ketorolac on IL-1beta-induced COX-2 and monocyte chemoattractant protein-1 gene expression in the cerebral endothelium. This drug plays therefore a paradoxical role in its ability to inhibit the circulating levels of glucocorticoids that are essential inhibitory feedback on the proinflammatory signal transduction pathways and gene transcription. In altering the production of key prostaglandins that are involved in the control of hypothalamic-pituitary-adrenal axis, ketorolac may have proinflammatory properties in the central nervous system during systemic immune stimuli.

BackgroundThere is urgent need for an accurate preoperative prediction of metastatic status to optimize treatment for patients with ovarian cancer (OC). The feasibility of predicting the metastatic status based on radiomics features from preoperative computed tomography (CT) images alone or combined with clinical factors were investigated.MethodsA total of 101 OC patients who underwent primary debulking surgery were enrolled. Radiomics features were extracted from the tumor volumes contoured on CT images with LIFEx package. Mann-Whitney U tests, least absolute shrinkage selection operator (LASSO), and Ridge Regression were applied to select features and to build prediction models. Univariate and regression analysis were applied to select clinical factors for metastatic prediction. The performance of models generated with radiomics features alone, clinical factors, and combined factors were evaluated and compared.ResultsNine radiomics features were screened out from 184 extracted features to classify patients with and without metastasis. Age and cancer antigen 125 (CA125) were the two clinical factors that were associated with metastasis. The area under curves (AUCs) for the radiomics signature, clinical factors model, and combined model were 0.82 (95% CI, 0.66-0.98; sensitivity = 0.90, specificity = 0.70), 0.83 (95% CI, 0.67-0.95; sensitivity = 0.71, specificity = 0.8), and 0.86 (95% CI, 0.72-0.99, sensitivity = 0.81, specificity = 0.8), respectively.ConclusionsRadiomics features alone or radiomics features combined with clinical factors are feasible and accurate enough to predict the metastatic status for OC patients.