Author

K. Kido

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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Physiology· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Approach:experimental assay· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Assay:electrophysiology· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

D. Motooka· 1 shared D. Okuzaki· 1 shared E. Morii· 1 shared E. Takeuchi· 1 shared F. Nielsen· 1 shared H. Mochizuki· 1 shared H. Murata· 1 shared J. B. Birk· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperImmunology

D. Motooka

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Metformin improves glycemia independently of skeletal muscle AMPK via enhanced intestinal glucose clearance

R. Kjobsted, J. M. Kristensen, J. B. Birk, N. O. Jorgensen, K. Kido, N. R. Andersen, J. K. Larsen, M. Foretz, V. Benoit, F. Nielsen, K. Brosen, N. Jessen, Y. Hellsten, K. Hojlund, J. F. P. Wojtaszewski

This study shows metformin reduces blood glucose through improved intestinal glucose clearance, not via skeletal muscle AMPK activation, challenging current understanding of its mechanism.

bioRxiv · May 22, 2022 · PreprintPhysiology

Myasthenia gravis-specific aberrant neuromuscular gene expression by medullary thymic epithelial cells in thymoma

Y. Yasumizu, N. Ohkura, H. Murata, M. Kinoshita, S. Funaki, S. Nojima, K. Kido, M. Kohara, D. Motooka, D. Okuzaki, S. Suganami, E. Takeuchi, Y. Nakamura, Y. Takeshima, M. Arai, , H. Mochizuki

This study identifies neuromuscular gene expression in thymoma-associated myasthenia gravis by analyzing bulk and single-cell RNA-seq data, linking it to specific thymic epithelial cell subpopulations.

bioRxiv · Dec 19, 2021Immunology

The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals

M. Huang, K. Kido, N. Ono, M. Altaf-UI-Amin, T. Tamura, S. Kanaya

This paper examines the feasibility of detecting atrial and ventricular arrhythmias using complexity metrics of heartbeat intervals, achieving 100% recall and precision with a 600-heartbeat-length signal.

bioRxiv · Apr 17, 2019 · PreprintPhysiology