New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN). BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core with leucine-rich repeats. The objective of this study is to compare the protein expression of BGN in breast tissue with and without cancer, using immunohistochemical technique associated with digital histological score (D-HScore) and supervised deep learning neural networks (SDLNN). In this case-control study, 24 formalin–fixed, paraffin-embedded tissues were obtained for analysis. Normal (n = 9) and cancerous (n = 15) tissue sections were analyzed by immunohistochemistry using BGN monoclonal antibody (M01-Abnova) and 3,3’-Diaminobenzidine (DAB) as the chromogen. Photomicrographs of the slides were analysed with D-HScore, using arbitrary DAB units. Another set (n = 129) with higher magnification without ROI selection, was submitted to the inceptionV3 deep neural network image embedding recognition model. Next, supervised neural network analysis, using stratified 20 fold cross validation, with 200 hidden layers, ReLu activation, and regularization at α = 0.0001 were applied for SDLNN. The sample size was calculated for a minimum of 7 cases and 7 controls, having a power = 90%, an α error = 5%, and a standard deviation of 20, to identify a decrease from the average of 40 DAB units (control) to 4 DAB units in cancer. BGN expression in DAB units [median (range)] was 6.2 (0.8 to 12.4) and 27.31 (5.3 to 81.7) in cancer and normal breast tissue, respectively, using D-HScore (p = 0.0017, Mann-Whitney test). SDLNN classification accuracy was 85.3% (110 out of 129; 95%CI = 78.1% to 90.3%). BGN protein expression is reduced in breast cancer tissue, compared to normal tissue.

Objective: The aim of this study was to compare the protein expression of biglycan (BGN) in normal breast tissue and in breast cancer using deep learning and digital HScore techniques. Methods: In this case-control study, 24 formalin-fixed, paraffin-embedded tissues were obtained from pathological archives for analysis. Normal breast (n=9) and breast cancer (n=15) tissue sections were analyzed by immunohistochemistry using BGN monoclonal antibody (M01 – Abnova), clone 4E1-1G7 at dilution 1:300 at pH 6, and 3,3’-diaminobenzidine (DAB) as the chromogen. Photomicrographs of the slides were analyzed using the ImageJ software with “color deconvolution”. After selecting the regions of interest (ROI), deconvoluted panels with DAB only were quantified using arbitrary DAB units. Another set, with higher magnification without ROI selection, was submitted to the inception V3 deep neural network image embedding recognition model. Next, supervised neural network analysis, using stratified 20-fold cross-validation, with 200 hidden layers, ReLu activation, and regularization at α=0.0001 were applied for SDLNN. The sample size was calculated for a minimum of seven cases and seven controls, having a power of 90%, an α error=5%, and a standard deviation of 20, to identify a decrease from the average of 40 DAB units (control) to 4 DAB units in cancer. Ethical approval was obtained from the Hospital de Clínicas de Porto Alegre Ethical Review Board (2019/0337). CAAE 15329119.9.0000.5327. Results: BGN expression (mean±SD) was 6.1±3.9 in breast cancer tissue, while in normal breast tissue, it was 39.6±21.9, using D-HScore (p=0.0017, student t-test, Welch corrected). SDLNN was able to correctly classify 110 out of 129 photomicrographs of the dataset using DAB panels only, with a classification accuracy of 85.3% (95%CI 78.1–90.3%) and the area under the curve of 94.3%. Conclusion: D-HScore and SDLNN revealed that BGN protein expression is reduced in breast cancer tissue, compared to normal tissue. The use of SDLNN seems to be a potential tool for image analysis in histological samples.

A falta de capacitação profissional no atual cenário do mercado de trabalho é uma grande preocupação encontrada junto a todos os gestores que buscam crescimento junto a empresa e diante aos seus demais concorrentes e com isso a proposta de métodos de educação corporativa acabam sendo uma forma econômica e lucrativa de se gerar tais profissionais. Este estudo tem como objetivo elencar e mostrar de forma clara quais são os métodos de implementação das universidades corporativas, bem como os níveis de satisfação encontrado em algumas empresas onde os métodos já foram devidamente aplicados e estão em funcionamento