Rabbit Anti-Human BRCA1 (Phospho-Ser1423) polyclonal antibody

Radiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting gBRCA1/2 status in women with healthy ovaries.

In this work we present for the first time the effect of carbon nanodots on the interaction of DNA layers deposited on gold electrodes and Thionine. Spectroscopic, electrochemical and operando spectroelec-trochemistry data demonstrate that Thionine interacts with ssDNA or dsDNA to a different extent, depending on the presence or not of carbon nanodots on the electrode surface. Carbon nanodots serve as nanosubstrate for direct immobilization of non-functionalized oligonucleotide, due to the strong inter-action of DNA bases with the nanomaterial. The acquired knowledge is applied to develop a new carbon nanodots based disposable DNA biosensor for the selective mutation detection in BRCA1 gene directly in clinical samples, using Thionine as redox indicator. (c) 2020 Elsevier Ltd. All rights reserved.