RESUMO
Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky due to the tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using the AI method is useful and critical to assist pathologists to pre-screen proper paraffin block for IHC. It is a challenging task since only WSI-level labels of molecular subtypes from IHC can be obtained without detailed local region information. Gigapixel WSIs are divided into a huge amount of patches to be computationally feasible for deep learning, while with coarse slide-level labels, patch-based methods may suffer from abundant noise patches, such as folds, overstained regions, or non-tumor tissues. A weakly supervised learning framework based on discriminative patch selection and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs. Firstly, co-teaching strategy using two networks was adopted to learn molecular subtype representations and filter out some noise patches. Then, a balanced sampling strategy was used to handle the imbalance in subtypes in the dataset. In addition, a noise patch filtering algorithm that used local outlier factor based on cluster centers was proposed to further select discriminative patches. Finally, a loss function integrating local patch with global slide constraint information was used to fine-tune MIL framework on obtained discriminative patches and further improve the prediction performance of molecular subtyping. The experimental results confirmed the effectiveness of the proposed AI method and our models outperformed even senior pathologists, which has the potential to assist pathologists to pre-screen paraffin blocks for IHC in clinic.
RESUMO
Induced pluripotent stem (iPS) cells can differentiate into nearly all types of cells. In contrast to embryonic stem cells, iPS cells are not subject to immune rejection because they are derived from a patient's own cells without ethical concerns. These cells can be used in regenerative medical techniques, stem cell therapy, disease modelling and drug discovery investigations. However, this application faces many challenges, such as low efficiency, slow generation time, partially reprogrammed colonies and tumourigenicity. Numerous techniques have been formulated in the past decade to improve reprogramming efficiency and safety, including the use of different transcription factors, small molecule compounds and non-coding RNAs. Recently, microRNAs (miRNAs) were found to promote the generation and differentiation of iPS cells. The miRNAs can more effectively and safely generate iPS cells than transcription factors. This process ultimately leads to the development of iPSC-based therapeutics for future clinical applications. In this comprehensive review, we summarise advances in research and the application of iPS cells, as well as recent progress in the use of miRNAs for iPS cell generation and differentiation. We examine possible clinical applications, especially in cardiology.
