Bibliographic record
Abstract
Currently, there is no cure for blindness. Pharmacology can only slow down the progression of a blinding disease, aiming at minimizing the symptoms. Stem cell regenerative therapy is a novel and very promising approach to overcome blindness. It has shown some potential in the eye but there remain major obstacles to be overcome before it can turn into a clinical reality, namely, the integration and survival of transplanted cells. In this work, we follow a two-pronged approach to address these challenges. Studies of photoreceptor development and degeneration could previously only be performed in vivo, due to the extremely poor survival of isolated photoreceptors in culture. Here, we describe a three-dimensional culture platform that enables isolated mouse postnatal day 11 rod photoreceptor survival and maturation in vitro and investigate its mechanism of action. We show that material stiffness does not affect photoreceptor survival and instead identify hyaluronic acid (HA) as its bioactive component. We investigate the molecular pathways activated by HA on photoreceptors and find that the Wnt, RhoA and mTOR pathways are its downstream effectors. The photoreceptors and the retinal pigmented epithelium (RPE) of the retina have a well-established mutual relationship in development, homeostasis and degeneration. In the second part of this work, we assess whether co-transplanting human embryonic stem cell-derived RPE and mouse postnatal day 11 rod photoreceptors in an HA hydrogel can improve behavioral recovery and cell survival compared to transplanting each cell type alone. We first validate the NaIO3 mouse model, which recapitulates advanced retinal degeneration. We next demonstrate that visual recovery in this model can be achieved only when RPE and photoreceptors are co-transplanted, as evidenced by three different outcome measures. This is accompanied by superior cell survival for both cell types in the co-transplant group. Our findings will guide future research and translation for retinal degenerative diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".