Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high-resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma.

Proteomics, oxidative stress and male infertility.

Oxidative stress has been established as one of the main causes of male infertility and has been implicated in many diseases associated with infertile men. It results from high concentrations of free radicals and suppressed antioxidant potential, which may alter protein expression in seminal plasma and/or spermatozoa. In recent years, proteomic analyses have been performed to characterize the protein profiles of seminal ejaculate from men with different clinical conditions, such as high oxidative stress.