Mesenchymal Stem Cell Therapy for Doxorubicin-Induced Cardiomyopathy: Potential Mechanisms, Governing Factors, and Implications of the Heart Stem Cell Debate.

Over the past decades, researchers have reported several mechanisms for doxorubicin (DOX)-induced cardiomyopathy, including oxidative stress, inflammation, and apoptosis. Another mechanism that has been suggested is that DOX interferes with the cell cycle and induces oxidative stress in C-kit+ cells (commonly known as cardiac progenitor cells), reducing their regenerative capacity. Cardiac regeneration through enhancing the regenerative capacity of these cells or administration of other stem cells types has been the axis of several studies over the past 20 years. Several experiments revealed that local or systemic injections with mesenchymal stem cells (MSCs) were associated with significantly improved cardiac function, ameliorated inflammatory response, and reduced myocardial fibrosis. They also showed that several factors can affect the outcome of MSC treatment for DOX cardiomyopathy, including the MSC type, dose, route, and timing of administration. However, there is growing evidence that the C-kit+ cells do not have a cardiac regenerative potential in the adult mammalian heart. Similarly, the protective mechanisms of MSCs against DOX-induced cardiomyopathy are not likely to include direct differentiation into cardiomyocytes and probably occur through paracrine secretion, antioxidant and anti-inflammatory effects. Better understanding of the involved mechanisms and the factors governing the outcomes of MSCs therapy are essential before moving to clinical application in patients with DOX-induced cardiomyopathy.

Structured crowdsourcing enables convolutional segmentation of histology images.

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.