Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.

Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.

Salts accelerate the switching kinetics of a cyclobis(paraquat-p-phenylene) [2]rotaxane.

The rate at which the macrocyclic cyclobis(paraquat-p-phenylene) ring of a bistable [2]rotaxane moves from a tetrathiafulvalene station to an oxyphenylene station upon oxidation of the tetrathiafulvalene station is found to be increased in the presence of added salts. Compared to the salt-free case, 0.1 M solutions of a series of tetraalkylammonium hexafluorophosphate salts (R4N·PF6, R = H, Me, Et or n-Bu) and of tetrabutylammonium perchlorate (n-Bu4N·ClO4) all afford an increased switching rate, which is largest in the case of n-Bu4N·ClO4 with smaller anions.