SKIN DERIVED SCHWANN CELL TRANSPLANT AND IL6 NEUTRALIZATION FOLLOWING NERVE INJURY
Bibliographic record
Abstract
Schwann cells (SCs), the glial cells of the peripheral nervous system, are highly plastic and are thought to be the key players in successful regeneration following nerve injury. Recently, the use of SC therapy, including therapies using SCs derived from skin (SKPSCs), has shown much potential for improving clinical outcomes after nerve injury. One of the primary mechanisms by which SKPSCs appear to enhance recovery is via the secretion of factors (ie. cytokine) known to play vital roles in regulating inflammation following injury. In particular, interleukin-6 (IL6), a pleiotropic cytokine upregulated after nerve injury, is thought to play an important role in the macrophage recruitment and phenotype regulation important for successful regeneration. Still, others have demonstrated that IL6 can play deletrious roles in the nerve as well. The dichotomous nature of IL6 in the nerve is highly context specific, highly complex and yet to be fully understood. Since we have recently identified IL6 as being expressed by SKPSCs at levels 20x higher than most other cytokines, and this cytokine is notorious for being a potent regulator of the immune system, here, we set out to determine the complex role of IL6 following SKPSCs transplant in the injured nerve. To answer this question, we administered IL6 neutralizing antiody or an IgG control during the first week following sciatic nerve crush injury and immediate SKPSC tranplant. We found that SKPSC tranplants alone increased macrophage densities in the distal stump, however this effect remained unaffected by anti-IL6 or IgG treatment. We also tested for difference in functional recovery due to our treaments and found that peak CMAP amplitudes were greatest when SKPSCs therapy was augmented with anti-IL6 treatment, compared to control treatments. Our results suggest that IL6 ultimately has a comprimising effect on SKPSCs therapy, but this effect is not due to early regulation of macrophage densities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".