A deep-learning based biomarker of systemic cellular senescence burden to predict mortality and health outcomes

Authors

• Shangshu Zhao, University of Connecticut
• Chia-Ling Kuo, University of Connecticut
• Eric J Lenze, Washington University School of Medicine in St. Louis
• Julie L Wetherell, University of California - San Diego
• Laura Haynes, University of Connecticut
• Perla El-Ahmad, University of Connecticut
• Richard Fortinsky, University of Connecticut
• George Kuchel, University of Connecticut
• Trevor Harris, University of Connecticut
• Breno S Diniz, Washington University School of Medicine in St. Louis

Journal

Aging cell

Publication Date

10-1-2026

Volume

25

Issue

10

First Page

e70737

Document Type

Open Access Publication

DOI

10.1111/acel.70737

Rights and Permissions

Zhao, S., C.-L. Kuo, E. J. Lenze, et al. 2026. “ A Deep-Learning Based Biomarker of Systemic Cellular Senescence Burden to Predict Mortality and Health Outcomes.” Aging Cell 25, no. 10: e70737. https://doi.org/10.1111/acel.70737. © 2026 The Author(s). Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Recommended Citation

Zhao, Shangshu; Kuo, Chia-Ling; Lenze, Eric J; Wetherell, Julie L; Haynes, Laura; El-Ahmad, Perla; Fortinsky, Richard; Kuchel, George; Harris, Trevor; and Diniz, Breno S, "A deep-learning based biomarker of systemic cellular senescence burden to predict mortality and health outcomes." Aging cell. 25, 10. e70737 (2026).
https://digitalcommons.wustl.edu/oa_4/7638

Department

ICTS (Institute of Clinical and Translational Sciences)

Additional Links

Supplemental material is available for this article at publisher site.