Expansion of retinal stem cells and their progeny using cell microcarriers in a bioreactor
Bibliographic record
Abstract
Abstract Blindness as a consequence of degenerative eye diseases (e.g., age‐related macular degeneration and retinitis pigmentosa) is a major health problem and numbers are expected to increase by up to 50% by 2020. Unfortunately, adult mouse and human retinal stem cells (RSCs), unlike fish and amphibians , are quiescent in vivo and do not regenerate following disease or injury. To replace lost cells, we used microcarriers (MCs) in a suspension stirring bioreactor to help achieve numbers suitable for differentiation and transplantation. We achieved a significant 10‐fold enrichment of RSC yield compared to conventional static culture techniques using a combination of FACTIII MCs and relative hypoxia (5%) inside the bioreactor. We found that hypoxia (5% O 2 ) was associated with better RSC expansion across all platforms; and this can be attributed to hypoxia‐induced increases in survival and/or symmetric division of stem cells. In the future, we will target the differentiation of RSCs and their progeny toward rod and cone photoreceptor phenotypes using FACTIII MCs inside bioreactors to expand their populations in order to produce the large numbers of cells needed for transplantation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".