ArticleClinical and translational science2025
Application of Machine Learning to Discriminate Photoreceptor Cell Species in Xenotransplanted Chimeric Retinas.
Article in Clinical and translational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Application of Machine Learning to Discriminate Photoreceptor Cell Species in Xenotransplanted Chimeric Retinas.Clinical and translational science · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Photoreceptor transplantation is being studied to restore visual function in retinal diseases causing blindness, including age-related macular degeneration, hereditary eye diseases, and traumatic retinopathy. Preclinical studies often involve delivering exogenous human photoreceptor cells into animal models' retinas. A key readout in such experiments is distinguishing donor cell integration from artificial labeling secondary to material transfer of cytosolic or nuclear labels. Recognizing donor (human) versus animal photoreceptor nuclei is key, but purely immunohistology discrimination is challenging due to antigenic species overlap or intercellular antigen transfer. To address this, we sought to develop and validate a computational technique to discriminate between photoreceptor cells of different animal species based on machine learning of nuclear morphology. We aim to evaluate the feasibility of computer-assisted nuclear detection combined with random forest classification to automate species differentiation in DAPI-stained photoreceptors after xenotransplantation into mouse and pig retinas. Our models were trained on single-species samples and validated with mixed-species samples. We then transplanted human embryonic stem cell-derived retinal organoid cells into rodent and pig retinal degeneration models. The random forest model accurately determined cell identity post-xenotransplantation, validated by histological assessment using an antihuman nuclear antibody. Our results support the potential efficacy of employing machine learning image analysis and classification techniques that may promote experimental rigor, minimize observer bias, and enable high throughput semiautomated workflows for transplantation outcomes analysis. The methodological framework reported here may enable a more nuanced and precise analysis of the behavior of transplanted photoreceptors for the purposes of human retinal regeneration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.