Author

S. Hultin

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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Cell Biology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

B. Murphy· 1 shared C. Wei· 1 shared C. Xi· 1 shared D. F. J. Ketelhuth· 1 shared E. Hutterer· 1 shared F. Salem· 1 shared F. Su· 1 shared G. Wong· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperPathology

B. Murphy

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2 papers

Recent·Most cited

The VE-cadherin/AmotL2 mechanosensory pathway suppresses aortic inflammation and the formation of abdominal aortic aneurysms

Y. Zhang, E. Hutterer, S. Hultin, O. Bergman, M. J. Forteza, Z. Andonovic, D. F. J. Ketelhuth, J. Roy, P. Eriksson, L. Holmgren

This study identifies the VE-cadherin/AmotL2 mechanosensory pathway as a suppressor of aortic inflammation and abdominal aortic aneurysms through mechanotransduction in endothelial cells.

bioRxiv · May 7, 2021Cell Biology

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