Organoids and organ chips in ophthalmology
Bibliographic record
Abstract
Recent advances have driven the development of stem cell-derived, self-organizing, three-dimensional miniature organs, termed organoids, which mimic different eye tissues including the retina, cornea, and lens. Organoids and engineered microfluidic organ-on-chips (organ chips) are transformative technologies that show promise in simulating the architectural and functional complexity of native organs. Accordingly, they enable exploration of facets of human disease and development not accurately recapitulated by animal models. Together, these technologies will increase our understanding of the basic physiology of different eye structures, enable us to interrogate unknown aspects of ophthalmic disease pathogenesis, and serve as clinically-relevant surrogates for the evaluation of ocular therapeutics. Both the burden and prevalence of monogenic and multifactorial ophthalmic diseases, which can cause visual impairment or blindness, in the human population warrants a paradigm shift towards organoids and organ chips that can provide sensitive, quantitative, and scalable phenotypic assays. In this article, we review the current situation of organoids and organ chips in ophthalmology and discuss how they can be leveraged for translational applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".