Abstract: A method for predicting a microsatellite instability (MSI) from a whole slide image based on a self-attention mechanism, in which an image of a tissue region obtained through cellular staining on a tissue sample of a patient serves as a target whole slide image to be predicted. The tissue region is segmented into tissue patches, and stain normalization is performed with staintools. N tumor patches are screened out from the tissue patches, and the N tumor patches are inputted to a preset feature extraction model. Each of the tumor patches are encoded as an M-dimensional feature vector, thereby obtaining a N×M feature matrix; and the N×M feature matrix is inputted to a pre-trained prediction model, such that the prediction model outputs an MSI score of the target whole slide image. The present disclosure predicts the MSI based on deep learning and the cellular staining.

Abstract: The invention is directed towards Apratyramide linear depsipeptide compounds, pharmaceutical compositions thereof, and methods of affecting wound healing, and methods of affecting the biological processes involved in wound healing (e.g., inflammation, cell proliferation, tissue granulation, remodeling of scar tissue, etc.).

Abstract: The invention is directed towards Apratyramide linear depsipeptide compounds, pharmaceutical compositions thereof, and methods of affecting wound healing, and methods of affecting the biological processes involved in wound healing (e.g., inflammation, cell proliferation, tissue granulation, remodeling of scar tissue, etc.).