AI discovery of TLR agonist-driven phenotypes reveals unique features of peripheral cells from healthy donors and ART-suppressed people living with HIV
These data suggest that machine learning, combined with cell imaging and cytokine quantification, can be used to better understand the cytological and soluble immune responses following treatments with immunomodulatory agents <i>in vitro.</i> In addition, comparisons of these responses between disease states are possible with the appropriate patient samples.
Authors
- Robert W. Omange (ORCID: https://orcid.org/0000-0002-0421-110X)
- Hope O’Donnell (ORCID: https://orcid.org/0000-0001-6460-9016)
- Yanhui Cai (ORCID: https://orcid.org/0009-0001-9936-8184)
- Tempest Plott
- Nikita Kolhatkar
- Jeffrey J. Wallin (ORCID: https://orcid.org/0000-0002-5952-3813)
- Pamela M. Odorizzi (ORCID: https://orcid.org/0000-0003-2598-4766)
- Meron Mengistu (ORCID: https://orcid.org/0000-0002-2247-0914)
- Ahmed Hosny (ORCID: https://orcid.org/0000-0002-1844-481X)
- Rachel Jacobson (ORCID: https://orcid.org/0000-0002-8667-5804)
- Kenneth Zhang (ORCID: https://orcid.org/0000-0002-4090-6005)
- Will Van Trump
- Daniel Chen (ORCID: https://orcid.org/0000-0002-4716-549X)
- Samuel Kim (ORCID: https://orcid.org/0000-0001-5237-6358)
- Michael Wiest (ORCID: https://orcid.org/0000-0001-8063-6321)
- Zach Barry
- Elisa Cambronero Addiego
Institutions
- Gilead Sciences (United States) (US)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-03-25
- DOI
- https://doi.org/10.3389/fimmu.2025.1541152
- Citations
- 2
- Primary Topic
- Immune Response and Inflammation
- Type
- article
- Field-Weighted Citation Impact
- 1.59
Funders
- Gilead Sciences