Author

I. A. Rosales

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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Bioengineering· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:clinical cohort / trial· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Assay:imaging· 1

Frequent Co-Authors

B. Murphy· 1 shared C. Wei· 1 shared C. Xi· 1 shared F. Salem· 1 shared F. Su· 1 shared G. T. Ribas· 1 shared G. Wong· 1 shared J. A. Lewis· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperPathology

B. Murphy

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Immune Response of Transplanted Kidney Tissues Assembled from Organoid Building Blocks

T. J. Borges, Y. Ganchiku, J. O. Aceves, R. van Gaal, S. G. M. Uzel, J. E. Rubins, K. Kobayashi, K. Hiratsuka, M. Tekguc, I. A. Rosales, G. T. Ribas, K. Lima, R. B. Gassen, R. Morizane, J. A. Lewis, L. V. Riella

This study transplants engineered kidney tissues made from organoid building blocks into mice with human immune cells to assess immune response and tissue integration, finding immune infiltration causes tissue injury similar to transplant rejection.

bioRxiv · Aug 26, 2023 · PreprintBioengineering

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

Z. Yi, F. Salem, M. C. Menon, K. Keung, C. Xi, S. Hultin, M. R. H. A. Rasheed, L. Li, F. Su, Z. Sun, C. Wei, W. Huang, S. Fredericks, Q. Lin, K. Banu, , W. Zhang

This paper develops a deep learning model to quantify pathological features in kidney transplant biopsies, showing improved prediction of graft loss compared to clinical and Banff scores.

bioRxiv · Apr 18, 2021Pathology