Label-free enrichment of human pluripotent stem cell-derived early retinal progenitor cells for cell-based regenerative therapies
Bibliographic record
Abstract
Pluripotent stem cell-based therapy for retinal degenerative diseases is a promising approach to restoring visual function. A clinical study using retinal organoid (RO) sheets was recently conducted in patients with retinitis pigmentosa. However, the graft preparation currently requires advanced skills to identify and excise suitable segments from the transplantable area of the limited number of suitable ROs. This remains a challenge for consistent clinical implementations. Herein, we enabled the enrichment of wild-type (non-reporter) retinal progenitor cells (RPCs) from dissociated ROs using a label-free ghost cytometry (LF-GC)-based sorting system, where a machine-based classifier was trained in advance with another RPC reporter line. The sorted cells reproducibly formed retinal spheroids large enough for transplantation and developed mature photoreceptors in the retinal degeneration rats. This method of enriching early RPCs with no specific surface antigens and without any reporters or chemical labeling is promising for robust preparation of graft tissues during cell-based therapy.
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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 | 0.001 |
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".