Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm.
View Article and Find Full Text PDFOvercoming PARPi resistance is a high clinical priority. We established and characterized comparative in vitro models of acquired PARPi resistance, derived from either a -proficient or -deficient isogenic background by long-term exposure to olaparib. While parental cell lines already exhibited a certain level of intrinsic activity of multidrug resistance (MDR) proteins, resulting PARPi-resistant cells from both models further converted toward MDR.
View Article and Find Full Text PDFJ Synchrotron Radiat
September 2021
FinEstBeAMS (Finnish-Estonian Beamline for Atmospheric and Materials Sciences) is a multidisciplinary beamline constructed at the 1.5 GeV storage ring of the MAX IV synchrotron facility in Lund, Sweden. The beamline covers an extremely wide photon energy range, 4.
View Article and Find Full Text PDFThe optimized design of multilayer-coated blazed gratings (MLBG) for high-flux tender X-ray monochromators was systematically studied by numerical simulations. The resulting correlation between the multilayer d-spacing and grating blaze angle significantly deviated from the one predicted by conventional equations. Three high line density gratings with different blaze angles were fabricated and coated by the same Cr/C multilayer.
View Article and Find Full Text PDFJ Biomed Semantics
September 2012
We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests.
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