Early detection of genotoxic hepatocarcinogens in rats using Î³H2AX and Ki-67: prediction by machine learning.

Michiba, Ayano; Gi, Min; Yokohira, Masanao; Sakurai, Eiko; Teramoto, Atsushi; Kiriyama, Yuka; Yamada, Seiji; Wanibuchi, Hideki; Tsukamoto, Tetsuya

Michiba, Ayano; Gi, Min; Yokohira, Masanao; Sakurai, Eiko; Teramoto, Atsushi; Kiriyama, Yuka; Yamada, Seiji; Wanibuchi, Hideki; Tsukamoto, Tetsuya.

Afiliação

Michiba A; Department of Diagnostic Pathology, Graduate School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan.
Gi M; Department of Environmental Risk Assessment, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka 545-8585, Japan.
Yokohira M; Departments of Medical Education and Pathology and Host-Defense, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa 761-0793, Japan.
Sakurai E; Department of Diagnostic Pathology, Graduate School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan.
Teramoto A; Faculty of Information Engineering, Meijo University, Nagoya, Aichi 468-8502, Japan.
Kiriyama Y; Department of Diagnostic Pathology, Graduate School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan.
Yamada S; Department of Pathology, Narita Memorial Hospital, Toyohashi, Aichi 441-8029, Japan.
Wanibuchi H; Department of Diagnostic Pathology, Graduate School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan.
Tsukamoto T; Department of Molecular Pathology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka 545-8585, Japan.

Toxicol Sci ; 195(2): 202-212, 2023 Sep 28.

Article em En | MEDLINE | ID: mdl-37527026

Palavras-chave

Ki-67; Random Forest; carcinogen; liver; machine learning; Î³H2AX

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Toxicol Sci Assunto da revista: TOXICOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Japão

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