Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model
Our SCSD-Net model offers a rapid protocol for detecting reproductive defects from H&E-stained testicular sections in as few as 3 h, providing both quantitative and qualitative assessments of spermatogenic defects. Our analysis uncovered evidence supporting the synergistic interaction of Sertoli PUM1 and PUM2 in maintaining average testis size, and redundant roles of DAZ family proteins DAZL and BOULE in meiosis.
Authors
- Xiangxue Wang
- Nianfei Ao
- Min Zang
- Chengfei Cai (ORCID: https://orcid.org/0000-0002-9582-0348)
- Jun Xu (ORCID: https://orcid.org/0000-0001-5315-8811)
- Yiping Jiao (ORCID: https://orcid.org/0000-0002-0787-3424)
- Eugene Yujun Xu (ORCID: https://orcid.org/0000-0002-4344-6606)
- Xin Li (ORCID: https://orcid.org/0000-0001-5632-5427)
- Yue Lü
- Tingting Zhao (ORCID: https://orcid.org/0000-0002-1099-4286)
- Minge Xie (ORCID: https://orcid.org/0000-0002-9946-4715)
- Haoda Lu (ORCID: https://orcid.org/0000-0003-4434-7869)
Institutions
- Rutgers, The State University of New Jersey (US)
- Northwestern University (US)
- Agency for Science, Technology and Research (SG)
- Nanjing University of Information Science and Technology (CN)
- University of Chicago (US)
- Bioinformatics Institute (SG)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Andrology
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1111/andr.13773
- Citations
- 1
- Primary Topic
- Sperm and Testicular Function
- Type
- article
- Field-Weighted Citation Impact
- 0.54
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China