P.235 Differences in Human, Pig, and Rat Spinal Cord Stem Cells in Response to Inflammatory and Regenerative Factors In Vitro
Bibliographic record
Abstract
Background: While the use of neural stem/progenitor cells has been reported as a promising therapeutic approach for spinal cord injury, direct comparison of adult primary animal spinal cord NSPCs have not been compared to human NSPCs under the same conditions to characterize intrinsic differences between human/animal NSPC response to inflammatory/regenerative factors. Methods: To mimic post-injury inflammation, primary-derived NSPCs from adult humans, pigs, and rats were treated with pro-inflammatory factors.To direct regeneration, NSPCs were treated with retinoic acid, platelet-derived growth factor or bone morphogenic protein-(BMP4) to induce neurons, oligodendrocytes or astrocytes, respectively.Cultures were treated for 7 or 14 days and characterized by immunocytochemistry. Results: Pro-inflammatory factors promoted more astrogenesis in rat and pig NSPCs compared to human NSPCs and induced neuronal differentiation in human NSPCs. RA increased neurogenesis of human and rat NSPCs, PDGFα increased oligodendrocyte differentiation of rat NSPCs, and BMP4 increased astrogenesis of human and rat NSPCs Conclusions: For the first time, differences in response of human, pig and rat primary NSPCs to inflammatory and regenerative factors have been identified. Better understanding of these differences is essential to improving the successful translation of regenerative therapies to humans.
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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) | 0.006 | 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".