Anti-ageing glycoprotein promotes long-term survival of transplanted neurosensory precursor cells
Bibliographic record
Abstract
Cell therapy, to replace lost tissue, is a promising approach for the treatment of various neurodegenerative diseases. Many studies suggest, however, that the percentage of transplanted cells that survive and undergo functional integration remains low as a result of immune rejection, suboptimal precursor cell type, trauma during cell transplantation, toxic compounds released by dying tissues or nutritional deficiencies. We recently developed an ex vivo system to facilitate identification of factors contributing to the death of transplanted neuronal (photoreceptor) cells and compounds that block these toxic effects. In this system, photoreceptor precursor cells (PPCs) are sandwiched between a neurosensory retinal explant and retinal pigment epithelium derived from human embryonic stem cells. Explant medium was collected to identify toxic components and PPC survival was assessed by flow cytometry. We also assessed the potential for AAGP™, a cryopreservative molecule, to improve PPC survival. We identified elevated prostaglandin E2 (PGE2) in the explant medium and demonstrated that AAGP™ reduced PGE2 levels by 2.6-fold. A pro-inflammatory stress assay suggested that this may result from AAGP™ inhibition of cyclo-oxygenase-2 (COX-2) expression. We confirmed that PGE2 reduced the viability of cultured PPCs by 44% and found that the survival rate of PPCs pretreated with AAGP™ was 2.8-fold higher than in untreated PPCs. These data suggest that PGE2 release from necrotic tissue may be one factor that reduces the survival of transplanted precursor cells and that the pro-survival molecule AAGP™ may improve long-term transplanted cell viability. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".