Biology subjects

Sato, A.

Publications and source records attributed to Sato, A..

4 recordsLinked to original sources

bioRxiv2018

Dipeptidyl dipeptidase-4 inhibitor, MK-0626, promotes bone marrow-derived endothelial progenitor cell bioactivities for vascular regeneration in diet-induced obese mice

Metabolic syndrome (MS), overlapping type 2 diabetes, hyperlipidemia, and/or hypertension, based on high-fat diet, poses risk for cardiovascular disease. A critical feature associated with such risk is the functional impairment of endothelial progenitor cells (EPCs). Dipeptidyl dipeptidase-4 inhibitors are known not only to inhibit degradation of incretins to control blood glucose levels, but also to improve EPC bioactivity and induce anti-inflammatory effects in tissues. In the present study, we investigated the effects of such an inhibitor, MK-06266, in ischemia model of MS using diet-induced obese (DIO) mice. EPC bioactivity was examined in MK-0626-administered DIO mice and non-treated control group, using an EPC colony-forming assay and bone marrow cKit+ Sca-1+ lineage-cells, and peripheral blood-mononuclear cells. Our results showed that, in vitro, the effect of MK-0626 treatment on EPC bioactivities and differentiation was superior in comparison with non-treatment. Further, in vivo hindlimb ischemia model experiment indicated that microvascular density and pericyte-recruited arteriole number were increased in MK-0626-administered group, but not control group. Lineage profiling of isolated cells from ischemic tissues disclosed that MK-0626 administration has an inhibitory effect on unproductive inflammation. This occurred via a decrease in the influx of total blood cells and pro-inflammatory cells such as neutrophils, total macrophages, M1, total T-cells, cytotoxic T-cells, and B-cells, with a concomitant increase in number of regeneration-associated cells, such as M2/M ratio and Treg/T-helper. Laser Doppler analysis revealed that at day 14 after ischemic injury, blood perfusion in hindlimb was grater in DIO mice treated with MK-0626, but not in control. In conclusion, the dipeptidyl dipeptidase-4 inhibitor has a positive effect on EPC differentiation in MS model of DIO mice. Following ischemic injury, DPP-4 i sharply reduces recruitment of pro-inflammatory cells into ischemic tissue, and triggers regeneration and reparation process. Thus, DPP-4 i is a promising therapeutic agent for MS treatment.

cell biology↗

bioRxiv2018

Parallelized analysis of spatial gene expression patterns by database integration

We developed a computational framework for automated integration of a large number of two-dimensional (2D) images with three-dimensional (3D) image datasets located in the standard 3D coordinate. We applied the framework to 2,810 para-sagittal sectioned mouse brain 2D images of in situ hybridization (ISH), archived in the BrainTx database (http://www.cdtdb.neuroinf.jp). We registered the ISH images into the mouse standard coordinate space for MR images, Waxholm space (WHS, https://www.nitrc.org/projects/incfwhsmouse) by linearly transforming them into each of a series of para-sagittal MR image slices, and identifying the best-fit slice by calculating the similarity metric value ({delta}). Transformed 2D images were compared with 3D gene expression image datasets, which were made using a microtomy-based microarray assay system, Transcriptome Tomography, and archived in the ViBrism DB (http://vibrism.neuroinf.jp): the 3D images are located in the WHS.\n\nWe first transformed ISH images of 10 regionally expressed genes and compared them to signals of corresponding 3D expression images in ViBrism DB for evaluating the integration schema: two types of data, produced with different modalities and originally located in different dimensions, were successfully compared after enhancing ISH signals against background noise. Then, for the massive transformation of BrainTx database images, we parallelized our framework, using the IPython cluster package, and implemented it on the PC cluster provided for the Brain Atlasing Hackathon activity hosted by Neuroinformatics Japan Center in Japan. We could identify the best-fit positions for all of the ISH images. All programs were made available through the GitHub repository, at the web site of neuroinformatics/bah2016_registration (https://github.com/neuroinformatics/bah2016_registration).

neuroscience↗

bioRxiv2018

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis.

A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large omics or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high \"startup\" costs and poor adherence to procedures when they deviate substantially from an analysts usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individuals existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically Rs package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

bioinformatics↗

bioRxiv2018

The recognition and expectations of ex-inpatients of mental health services: A web-based questionnaire survey in Japan.

Concern about mental health issues and the treatment of mentally disordered offenders attracts a lot of public attention. This study aimed to gather the experiences and opinions of people who have experienced admission to a psychiatric ward in order to understand their reaction to, and understanding of, the legislation behind the involuntary admission of psychiatric patients. A web-based questionnaire survey was conducted with a total of 379 participants, using a cross-sectional, exploratory design. The data were analyzed using a chi-square test, Fishers exact test, and a logistic regression analysis. According to the results, many patients were satisfied with their psychiatric admission, however, few participants said that they had been given adequate explanation for their involuntary treatment. Most participants expected qualified aids after discharge, although the prospect of a regular visit from an official was not entirely supported by the participants. Patient satisfaction was relevant to the discussion of their needs after discharge and in developing a crisis plan during admission. These findings suggests that psychiatric patients accept inpatient treatment as long as they receive an adequate explanation. More qualified care such as relapse prevention would be expected to lead to better satisfaction. In order for them to welcome regular visits from an official, patients may need more information and discussion.

neuroscience↗