Deep-DPC: Deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology
The deep-DPC offers a promising strategy for fibrosis assessment by combining deep learning with dynamic cell morphology analysis based on time-series DPC images. Additionally, the platform holds potential as a novel therapeutic approach for anti-myocardial fibrosis by regulating cell morphology.
Authors
- Xuefeng Yan (ORCID: https://orcid.org/0000-0001-6030-0855)
- Hua Yang (ORCID: https://orcid.org/0000-0002-4876-4072)
- Y. Cui
- Xudong Xing (ORCID: https://orcid.org/0000-0002-3081-1694)
- Wen Yuan Gao (ORCID: https://orcid.org/0000-0002-5087-8351)
- Chenchen Feng (ORCID: https://orcid.org/0000-0002-1854-356X)
- Yang Liu (ORCID: https://orcid.org/0000-0002-1109-0756)
- Ping Li (ORCID: https://orcid.org/0000-0003-4150-3758)
- Huiying Wang (ORCID: https://orcid.org/0009-0001-3229-2365)
- Ping Zhou (ORCID: https://orcid.org/0000-0002-6224-054X)
- Y. Stanley Tan
- Yu-Ru Cai
- Hao Dai (ORCID: https://orcid.org/0000-0003-0226-9292)
Institutions
- China Pharmaceutical University (CN)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2025-02-24
- DOI
- https://doi.org/10.1016/j.jare.2025.02.028
- Citations
- 3
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.90
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Fundamental Research Funds for the Central Universities