Photoreceptor precursor cell integration into rodent retina after treatment with novel glycopeptide PKX-001
Bibliographic record
Abstract
SUMMARY Cell replacement therapy is emerging as an important approach in novel treatments for neurodegenerative diseases. Many problems remain, in particular improvements are needed in the survival of transplanted cells and increasing functional integration into host tissue. These problems arise because of immune rejection, suboptimal precursor cell type, trauma during cell transplantation, toxic compounds released by dying tissues and nutritional deficiencies. We recently developed an ex vivo system to facilitate identification of factors contributing to the death of transplanted neuronal (photoreceptor) and showed 2.8-fold improvement in transplant cell survival after pre-treatment with a novel glycopeptide (PKC-100). In this study we extended these studies to look at cell survival, maturation and functional integration in an in vivo rat model of rhodopsin-mutant retinitis pigmentosa causing blindness. We found that only when human photoreceptor precursor cells (PPCs) were pre-incubated with PKX-100 prior to transplantation, did the cells integrate and mature into cone photoreceptors expressing S-opsin or L/M opsin. In addition, ribbon synapses were observed in the transplanted cells suggesting they were making synaptic connections with the host tissue. Furthermore, optokinetic tracking and electroretinography responses in vivo were significantly improved compared to cell transplants without PKX-100 pre-treatment. These data demonstrate that PKX-100 promotes significant long-term stem cell survival in vivo , providing a platform for further investigation towards the clinical application to repair damaged or diseased retina.
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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".