Practical considerations concerning the use of stem cells for peripheral nerve repair
Bibliographic record
Abstract
In this review the authors intend to demonstrate the need for supplementing conventional repair of the injured nerve with alternative therapies, namely transplantation of stem or progenitor cells. Although peripheral nerves do exhibit the potential to regenerate axons and reinnervate the end organ, outcome following severe nerve injury, even after repair, remains relatively poor. This is likely because of the extensive injury zone that prevents axon outgrowth. Even if outgrowth does occur, a relatively slow growth rate of regeneration results in prolonged denervation of the distal nerve. Whereas denervated Schwann cells (SCs) are key players in the early regenerative success of peripheral nerves, protracted loss of axonal contact renders Schwann cells unreceptive for axonal regeneration. Given that denervated Schwann cells appear to become effete, one logical approach is to support the distal denervated nerve environment by replacing host cells with those derived exogenously. A number of different sources of stem/precursor cells are being explored for their potential application in the scenario of peripheral nerve injury. The most promising candidate, transplant cells are derived from easily accessible sources such as the skin, bone marrow, or adipose tissue, all of which have demonstrated the capacity to differentiate into Schwann cell-like cells. Although recent studies have shown that stem cells can act as promising and beneficial adjuncts to nerve repair, considerable optimization of these therapies will be required for their potential to be realized in a clinical setting. The authors investigate the relevance of the delivery method (both the number and differentiation state of cells) on experimental outcomes, and seek to clarify whether stem cells must survive and differentiate in the injured nerve to convey a therapeutic effect. As our laboratory uses skin-derived precursor cells (SKPCs) in various nerve injury paradigms, we relate our findings on cell fate to other published studies to demonstrate the need to quantify stem cell survival and differentiation for future studies.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".