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Akademický článek

Recurrence prediction in clear cell renal cell carcinoma using machine learning of quantitative nuclear features.

Autor: Matsubara S; Department of Urology, Tokyo Medical University Hospital, 6-7-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan., Saito A; Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, 6-1-1, Shinjuku, Shinjuku-Ku, Tokyo, 160-8402, Japan.; Department of Molecular Pathology, Tokyo Medical University, 6-1-1, Shinjuku, Shinjuku-Ku, Tokyo, 160-8402, Japan., Tokuyama N; Department of Urology, Tokyo Medical University Hospital, 6-7-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan., Muraoka R; Department of Urology, Tokyo Medical University Hospital, 6-7-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan., Hashimoto T; Department of Urology, Tokyo Medical University Hospital, 6-7-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan., Satake N; Department of Urology, Tokyo Medical University Hospital, 6-7-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan., Nagao T; Department of Anatomic Pathology, Tokyo Medical University, 6-1-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-8402, Japan., Kuroda M; Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, 6-1-1, Shinjuku, Shinjuku-Ku, Tokyo, 160-8402, Japan. kuroda@tokyo-med.ac.jp.; Department of Molecular Pathology, Tokyo Medical University, 6-1-1, Shinjuku, Shinjuku-Ku, Tokyo, 160-8402, Japan. kuroda@tokyo-med.ac.jp., Ohno Y; Department of Urology, Tokyo Medical University Hospital, 6-7-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan. yoshio-o@tokyo-med.ac.jp.

Publikováno v: Scientific reports [Sci Rep] 2023 Jul 07; Vol. 13 (1), pp. 11035. Date of Electronic Publication: 2023 Jul 07.

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Akademický článek

Anisotropic superconductivity in [formula omitted]-[formula omitted][formula omitted]: STM spectroscopy

Autor: Nomura, K., Muraoka, R., Matsunaga, N., Ichimura, K., Yamada, J.

Publikováno v: In Physica B: Physics of Condensed Matter 1 March 2009 404(3-4):562-564

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Akademický článek

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Akademický článek

Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features.

Autor: Tokuyama N; Department of Urology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Saito A; Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan.; Department of Molecular Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan., Muraoka R; Department of Urology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Matsubara S; Department of Urology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Hashimoto T; Department of Urology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Satake N; Department of Urology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Matsubayashi J; Department of Anatomic Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Nagao T; Department of Anatomic Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan., Mirza AH; Department of Molecular Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan.; Department of Pharmacology, Weill Cornell Medicine, New York, NY, 10065, USA., Graf HP; Department of Machine Learning, NEC Labs America Inc., Princeton, NJ, 08540, USA., Cosatto E; Department of Machine Learning, NEC Labs America Inc., Princeton, NJ, 08540, USA., Wu CL; Department of Pathology, Massachusetts General Hospital, Boston, MA, 02114, USA., Kuroda M; Department of AI Applied Quantitative Clinical Science, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan. kuroda@tokyo-med.ac.jp.; Department of Molecular Pathology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-8402, Japan. kuroda@tokyo-med.ac.jp., Ohno Y; Department of Urology, Tokyo Medical University, Shinjuku-ku, Tokyo, 160-0023, Japan.

Publikováno v: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2022 Apr; Vol. 35 (4), pp. 533-538. Date of Electronic Publication: 2021 Oct 29.

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Akademický článek

Immunohistochemical expression of heat shock proteins in the mouse periodontal tissues due to orthodontic mechanical stress

Autor: Muraoka R, Nakano K, Kurihara S, Yamada K, Kawakami T

Publikováno v: European Journal of Medical Research, Vol 15, Iss 11, p 475 (2010)

Abstract The histopathology of periodontal ligament of the mouse subjected to mechanical stress was studied. Immunohistochemical expressions of HSP27 and pHSP27 were examined. Experimental animals using the maxillary molars of ddY mouse by Waldo meth

Externí odkaz: https://doaj.org/article/0fd91b82078c41f1a4cc23e29c75b7c2

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Akademický článek

Heat Shock Protein Overexpression-Mediated Periodontal Ligament Regeneration: A Fundamental Approach to Generate a Potential Biomaterial.

Autor: Muraoka R; Department of Orthodontics, Matsumoto Dental University School of Dentistry and Hospital, 1780 Hirooka-Gobara, Shiojiri 399-0781, Japan., Nakano K; Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Okayama 700-8525, Japan., Kawakami T; Department of Oral Pathology, School of Dentistry, Aichi Gakuin University, 1-100 Kusumoto-cho, Chikusa-ku, Nagoya 464-8650, Japan.

Publikováno v: Materials (Basel, Switzerland) [Materials (Basel)] 2022 Jan 21; Vol. 15 (3). Date of Electronic Publication: 2022 Jan 21.

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HSP47 as a Possible Molecular Chaperone for the Collagen Synthesis in the Mouse Periodontal Ligament Cells due to Orthodontic Force

Autor: Muraoka, R, Nakano, K, Yamada, K, Kawakami, T

Publikováno v: 雑誌掲載論文(電子版)

Orthodontic mechanical stress induces various molecules, which cause structural changes in the proteins in periodontal tissue. The expression kinetics of HSP47 in mouse periodontal ligament (PDL) tissue after application of corrective mechanical stre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::985908e0c386ac4b393a4d39856e192c
https://zenodo.org/record/581943

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