The new paradigm: retinal pigment epithelium cells generated from embryonic or induced pluripotent stem cells
Bibliographic record
Abstract
Compared with neural crest-derived melanocytes, retinal pigment epithelium (RPE) cells in the back of the eye are pigment cells of a different kind. They are a part of the brain, form an epithelial monolayer, respond to distinct extracellular signals, and provide functions that far exceed those of a light-absorbing screen. For instance, they control nutrient and metabolite flow to and from the retina, replenish 11-cis-retinal by re-isomerizing all-trans-retinal generated during photoconversion, phagocytose daily a portion of the photoreceptors' outer segments, and secrete cytokines that locally control the innate and adaptive immune systems. Not surprisingly, RPE cell damage is a major cause of human blindness worldwide, with age-related macular degeneration a prevalent example. RPE replacement therapies using RPE cells generated from embryonic or induced pluripotent stem cells provide a novel approach to a rational treatment of such forms of blindness. In fact, RPE-like cells can be obtained relatively easily when stem cells are subjected to a two-step induction protocol, a first step that leads to a neuroectodermal fate and a second to RPE differentiation. Here, we discuss the characteristics of such cells, propose criteria they should fulfill in order to be considered authentic RPE cells, and point out the challenges one faces when using such cells in attempts to restore vision.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".