Author

T. Ariizumi

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2 Plant Biology· 2

Signature Assays, Organisms & Methods

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Approach:experimental assay· 4 Research Focus:gene regulatory network inference· 2 Research Focus:host-pathogen & immune response· 2 Organism:mouse· 2 Organism:plant (other)· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

A. Sato· 2 shared H. Kobayashi· 2 shared H. Nobori· 2 shared H. Sawa· 2 shared K. Tabata· 2 shared M. Kishimoto· 2 shared M. Sasaki· 2 shared S. Iida· 2 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMicrobiology

A. Sato

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Combination therapy with oral antiviral and anti-inflammatory drugs improves the efficacy of delayed treatment in severe COVID-19

M. Sasaki, S. Iida, Y. Hirata, S. Kusakabe, K. Konishi, Y. Itakura, K. Tabata, M. Kishimoto, H. Kobayashi, T. Ariizumi, K. Intaruck, H. Nobori, S. Toba, A. Sato, K. Matsuno, , H. Sawa

This study shows that combining an oral antiviral with a corticosteroid improves treatment outcomes in delayed SARS-CoV-2 infection in hamsters compared to monotherapy.

bioRxiv · Jun 20, 2023Microbiology

Oral administration of S-217622, a SARS-CoV-2 main protease inhibitor, decreases viral load and accelerates recovery from clinical aspects of COVID-19

M. Sasaki, K. Tabata, M. Kishimoto, Y. Itakura, H. Kobayashi, T. Ariizumi, K. Uemura, S. Toba, S. Kusakabe, Y. Maruyama, S. Iida, N. Nakajima, T. Suzuki, S. Yoshida, H. Nobori, , H. Sawa

This study shows that oral S-217622 reduces SARS-CoV-2 viral load and improves recovery in hamsters, with activity against multiple variants.

bioRxiv · Feb 14, 2022Microbiology

Genome-wide cis-decoding for expression designing in tomato using cistrome data and explainable deep learning

T. Akagi, K. Masuda, E. Kuwada, K. Takeshita, T. Kawakatsu, T. Ariizumi, Y. Kubo, K. Ushijima, S. Uchida

This study uses cistrome data and explainable deep learning to predict tomato fruit expression patterns from DNA sequences, identifying key regulatory elements involved in ripening.

bioRxiv · Jun 1, 2021 · PreprintPlant Biology

Organelle genome assembly uncovers the dynamic genome reorganization and cytoplasmic male sterility associated genes in tomato.

K. Kuwabara, I. Harada, Y. Matsuzawa, T. Ariizumi, K. Shirasawa

This study identifies CMS-associated genes in tomato by comparing mitochondrial and chloroplast genomes across CMS lines and their donors, finding three uniquely expressed genes in CMS lines.

bioRxiv · Mar 3, 2021Plant Biology