Polymorphism in CYP17, GSTM1 and the progesterone receptor genes and its relationship with mammographic density

Radiologic breast density is one of the predictive factors for breast cancer and the extent of the density is directly related to postmenopause. However, some patients have dense breasts even during postmenopause. This condition may be explained by the genes that codify for the proteins involved in the biosynthesis, as well as the activity and metabolism of steroid hormones. They are polymorphic, which could explain the variations of individual hormones and, consequently, breast density. The constant need to find markers that may assist in the primary prevention of breast cancer as well as in selecting high risk patients motived this study. We determined the influence of genetic polymorphism of CYP17 (cytochrome P450c17, the gene involved in steroid hormone biosynthesis), GSTM1 (glutathione S-transferase M1, an enzyme involved in estrogen metabolism) and PROGINS (progesterone receptor), for association with high breast density. One hundred and twenty-three postmenopausal patients who were not on hormone therapy and had no clinical or mammographic breast alterations were included in the present study. The results of this study reveal that there was no association between dense breasts and CYP17 or GSTM1. There was a trend, which was not statistically significant (P = 0.084), towards the association between PROGINS polymorphism and dense breasts. However, multivariate logistic regression showed that wild-type PROGINS and mutated CYP17, taken together, resulted in a 4.87 times higher chance of having dense breasts (P = 0.030). In conclusion, in the present study, we were able to identify an association among polymorphisms, involved in estradiol biosyntheses as well as progesterone response, and radiological mammary density.Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Disciplina de MastologiaUNIFESP, EPM, Disciplina de MastologiaSciEL

A standardisation framework for bio‐logging data to advance ecological research and conservation

Bio‐logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio‐logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio‐logging data into research and management recommendations. We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy‐of‐use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security. We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing. Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter‐governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio‐logging data formats across all fields in animal ecology