An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos.

In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, rates of live birth following clinical ART remain low worldwide. Grading is based on the embryo shape at a limited number of stages and does not consider the shape of embryos and intracellular structures, e.

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it.

Roles of microglia/macrophage and antibody in cell sheet transplantation in the central nervous system.

Background: We previously established a human mesenchymal stem cell (MSC) line that was modified to express trophic factors. Transplanting a cell sheet produced from this line in an amyotrophic lateral sclerosis mouse model showed a beneficial trend for mouse life spans. However, the sheet survived for less than 14 days, and numerous microglia and macrophages were observed within and adjacent to the sheet.

Predicting the future direction of cell movement with convolutional neural networks.

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time.