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SummaryDeep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology.

Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.

BackgroundPatient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.ObjectiveTo make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.MethodWe developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.ResultsOf the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84–0.93) for detecting patients who required urgent revascularization.ConclusionsOur artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.

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Connective tissue growth factor (CTGF) is induced by transforming growth factor-b (TGF-b) via Smad activation in mesangial cells. We recently reported that sphingosine 1-phosphate (S1P) induces CTGF expression in rat cultured mesangial cells. However, the mechanism by which S1P induces CTGF expression is unknown. The present study revealed that S1P-induced CTGF expression is mediated via pertussis toxin-insensitive pathways, which are involved in the activation of small GTPases of the Rho family and protein kinase C. We also showed by luciferase reporter assays and chromatin immunoprecipitation that S1P induces CTGF expression via Smad activation as TGF-b does.

Abstract. Inhibitors of poly(ADP-ribose) polymerase (PARP) are new types of personalized treatment of relapsed platinum-sensitive ovarian cancer harboring BRCA1/2 mutations. Ovarian clear cell cancer (CCC), a subset of ovarian cancer, often appears as low-stage disease with a higher incidence among Japanese. Advanced CCC is highly aggressive with poor patient outcome. The aim of the present study was to determine the potential synthetic lethality gene pairs for PARP inhibitions in patients with CCC through virtual and biological screenings as well as clinical studies. We conducted a literature review for putative PARP sensitivity genes that are associated with the CCC pathophysiology. Previous studies identified a variety of putative target genes from several pathways associated with DNA damage repair, chromatin remodeling complex, PI3K-AKT-mTOR signaling, Notch signaling, cell cycle checkpoint signaling, BRCA-associated complex and Fanconi's anemia susceptibility genes that could be used as biomarkers or therapeutic targets for PARP inhibition. BRCA1/2, ATM, ATR, BARD1, CCNE1, CHEK1, CKS1B, DNMT1, ERBB2, FGFR2, MRE11A, MYC, NOTCH1 and PTEN were considered as candidate genes for synthetic lethality gene partners for PARP interactions. When considering the biological background underlying PARP inhibition, we hypothesized that PARP inhibitors would be a novel synthetic lethal therapeutic approach for CCC tumors harboring homologous recombination deficiency and activating oncogene mutations. The results showed that the majority of CCC tumors appear to have indicators of DNA repair dysfunction similar to those in BRCA-mutation carriers, suggesting the possible utility of PARP inhibitors in a subset of CCC.