The immunomodulatory properties of adult skin‐derived precursor <scp>S</scp>chwann cells: implications for peripheral nerve injury therapy
Bibliographic record
Abstract
Skin-derived precursor Schwann cell (SKPSC) therapy has been identified as a potentially beneficial treatment for peripheral nerve injuries. One hypothesised mechanism by which SKPSCs enhance recovery is via the modulation of macrophages. In the present study, we investigated the immunomodulatory properties of adult rat SKPSCs, and demonstrated that these cells expressed a battery of cytokines, including interferon-γ (IFN-γ), interleukin (IL)-1β, and, most abundantly, IL-6. Whereas macrophages exposed to depleted or fibroblast-conditioned medium secreted minimal amounts of tumor necrosis factor-α (TNF-α), in the presence of SKPSC-conditioned medium, macrophages secreted > 500 pg/mL TNF-α. Following the transplantation of SKPSCs into injured rat sciatic nerves, we observed an SKPSC density-dependent increase in the number of macrophages (Pearson's r = 0.66) and an SKPSC density-dependent decrease in myelin debris (Pearson's r = -0.68). To determine the effect of IL-6 in a proinflammatory context, macrophage cultures were primed with either lipopolysaccharide (LPS)/IFN-γ/IL-1β or LPS/IFN-γ/IL-1β + IL-6, and this showed a 212% and 301% increase in the number of inducible nitric oxide synthase (iNOS)-positive proinflammatory macrophages respectively. In contrast to neurons exposed to conditioned medium from unprimed macrophages, neurons treated with conditioned medium from proinflammatory-primed macrophages showed a 13-26% reduction in neurite outgrowth. Anti-IL-6 antibody combined with SKPSC transplant therapy following nerve injury did not alter macrophage numbers or debris clearance, but instead reduced iNOS expression as compared with SKPSC + IgG-treated rats. SKPSC + anti-IL-6 treatment also resulted in a two-fold increase in gastrocnemius compound muscle action potential amplitudes as compared with SKPSC + IgG treatment. Understanding the mechanisms underlying immunomodulatory aspects of SKPSC therapy and developing approaches to manipulate these responses are important for advancing Schwann cell-based therapies.
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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 | 0.000 |
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".