Induced Pluripotent Stem Cells for Clinical Use

Induced pluripotent stem cells (iPSCs) are expected to be a novel cell source for regenerative medicine. Although iPSCs represented a significant breakthrough, there were many initial obstacles for their clinical use such as exogenous sequence insertions, inefficient cell reprogramming, tumorigenic properties, and animal-derived culture components. However, much progress has been made in iPSC generation since their development. The first human trial of iPSC-derived cell transplantation was conducted in September 2014, in which iPSC-derived retinal pigment epithelial cells were transplanted to a patient with macular degeneration. Because multiple clinical trials using iPSCs are expected in the near future, preparation of guidelines for generating and selecting iPSC lines suitable for clinical application is a pressing issue

Glacial fjord environment and ecosystem reconstructed from sediments deposited in Bowdoin Fjord, northwestern Greenland.

The Tenth Symposium on Polar Science/Ordinary sessions: [OG] Polar Geosciences, Wed. 4 Dec. / 3F Seminar room, National Institute of Polar Researc

Multiple episodes of ice loss from the Wilkes Subglacial Basin during the Last Interglacial

The Last Interglacial (LIG: 130,000–115,000 years ago) was a period of warmer global mean temperatures and higher and more variable sea levels than the Holocene (11,700–0 years ago). Therefore, a better understanding of Antarctic ice-sheet dynamics during this interval would provide valuable insights for projecting sea-level change in future warming scenarios. Here we present a high-resolution record constraining ice-sheet changes in the Wilkes Subglacial Basin (WSB) of East Antarctica during the LIG, based on analysis of sediment provenance and an ice melt proxy in a marine sediment core retrieved from the Wilkes Land margin. Our sedimentary records, together with existing ice-core records, reveal dynamic fluctuations of the ice sheet in the WSB, with thinning, melting, and potentially retreat leading to ice loss during both early and late stages of the LIG. We suggest that such changes along the East Antarctic Ice Sheet margin may have contributed to fluctuating global sea levels during the LIG

Emerin plays a crucial role in nuclear invagination and in the nuclear calcium transient.

Alteration of the nuclear Ca2+ transient is an early event in cardiac remodeling. Regulation of the nuclear Ca2+ transient is partly independent of the cytosolic Ca2+ transient in cardiomyocytes. One nuclear membrane protein, emerin, is encoded by EMD, and an EMD mutation causes Emery-Dreifuss muscular dystrophy (EDMD). It remains unclear whether emerin is involved in nuclear Ca2+ homeostasis. The aim of this study is to elucidate the role of emerin in rat cardiomyocytes by means of hypertrophic stimuli and in EDMD induced pluripotent stem (iPS) cell-derived cardiomyocytes in terms of nuclear structure and the Ca2+ transient. The cardiac hypertrophic stimuli increased the nuclear area, decreased nuclear invagination, and increased the half-decay time of the nuclear Ca2+ transient in cardiomyocytes. Emd knockdown cardiomyocytes showed similar properties after hypertrophic stimuli. The EDMD-iPS cell-derived cardiomyocytes showed increased nuclear area, decreased nuclear invagination, and increased half-decay time of the nuclear Ca2+ transient. An autopsied heart from a patient with EDMD also showed increased nuclear area and decreased nuclear invagination. These data suggest that Emerin plays a crucial role in nuclear structure and in the nuclear Ca2+ transient. Thus, emerin and the nuclear Ca2+ transient are possible therapeutic targets in heart failure and EDMD. © The Author(s) 2017

Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data.

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization