Despite enormous advances in the mass spectrometry and proteomics fields during the last two decades, the analysis of membrane proteins still remains a challenge for the proteomic community. Membrane proteins play a wide number of key roles in several cellular events, making them relevant target molecules to study in a significant variety of investigations (e.g., cellular signaling, immune surveillance, drug targets, vaccine candidates, etc.). Here, we critically review the several attempts that have been carried out on the different steps of the sample preparation procedure to improve and modify existing conventional proteomic strategies in order to make them suitable for the study of membrane proteins. We also revise novel techniques that have been designed to tackle the difficult but relevant task of identifying and characterizing membrane proteins.
Background: Thyroid cancer incidence is the most rapidly increasing malignancy; rates are three times higher in women than men. Thyroid hormone-disrupting flame-retardant chemicals, including polybrominated diphenyl ethers (PBDE) and polybrominated biphenyls (PBB), may contribute to this trend. Methods: We investigated the relationship between PBDE/ PBB exposure and papillary thyroid cancer (PTC) in 250 incident female papillary thyroid cancer cases and 250 female controls frequency-matched on age. Interviews and postdiagnostic serum samples were collected from 2010 to 2013. Serum samples were analyzed for 11 congeners. We calculated ORs and 95% confidence intervals (95% CI) using singlepollutant logistic regression models for continuous and categorical lipid-adjusted serum concentrations of PBDE/PBB, adjusted for age, alcohol consumption, and education. We applied three multi-pollutant approaches [standard multipollutant regression models, hierarchical Bayesian logistic regression modeling (HBLR), principal components analysis (PCA)] to investigate associations with PBDE/PBB mixtures. Results: In single-pollutant models, a decreased risk was observed at the highest (>90th percentile) versus lowest (
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