Author

T. T. Mai

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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Immunology· 1 Microbiology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:bacteria (other)· 1 Research Focus:host-pathogen & immune response· 1 Assay:imaging· 1 Organism:non-human primate· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

H. T. Dong· 2 shared S. Senapin· 2 shared C. Rodkhum· 1 shared D. T. Le· 1 shared J. A. Lees· 1 shared J. Corander· 1 shared J. d. Pozo· 1 shared J. Jorge del-Pozo· 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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2 shared papersPathology

H. T. Dong

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Usefulness of the pancreas as a prime target for histopathological diagnosis of Tilapia parvovirus (TiPV) infection in Nile tilapia, Oreochromis niloticus

H. T. Dong, P. Sangpo, D. T. Le, T. T. Mai, L. Nguyen Vu, J. Jorge del-Pozo, K. R. Salin, S. Senapin

This study identifies pancreatic Cowdry type A inclusion bodies as a specific histopathological marker for TiPV infection in Nile tilapia.

bioRxiv · Mar 21, 2022Pathology

Efficacy of heat-killed and formalin-killed vaccines against Tilapia tilapinevirus in juvenile Nile tilapia (Oreochromis niloticus)

T. T. Mai, P. Kayansamruaj, S. Taengphu, S. Senapin, J. Z. Costa, J. d. Pozo, K. D. Thompson, C. Rodkhum, H. T. Dong

This study evaluates heat-killed and formalin-killed vaccines against Tilapia tilapinevirus in Nile tilapia, showing both vaccines provide significant protection with varying immune responses.

bioRxiv · Jun 3, 2021Immunology

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

J. A. Lees, T. T. Mai, M. Galardini, N. E. Wheeler, J. Corander

This paper introduces a joint modeling framework for bacterial GWAS that improves statistical power and phenotype prediction while maintaining interpretability and computational efficiency.

bioRxiv · Nov 23, 2019 · PreprintMicrobiology