Bibliographic record
Abstract
OBJECTIVE: The purpose of this review is to summarize the basic science literature related to chronic nerve injuries, and to then use this as the background to provide emerging insights into the promising role of cellular therapy for nerve injury repair. METHODS: The literature pertinent to the experimental and clinical aspects of chronic nerve injury was reviewed, as was emerging literature and our own recent experience in using cellular therapy to repair injured nerves. RESULTS: Peripheral nerves have the potential to regenerate axons and reinnervate end organs. Yet, outcome after peripheral nerve injury, even after nerve repair, remains relatively poor. The single most important quantitative contributor to poor motor recovery is chronic denervation of the distal nerve. Chronic denervation is common because of the often extensive injury zone that prevents any axonal outgrowth or (even if outgrowth occurs) the relatively slow rate of regeneration. As a consequence, the distal nerve remains chronically devoid of regrowing axons. In turn, prolonged denervation of Schwann cells (SCs) seems to be the critical factor that makes them unreceptive for axonal regeneration. Regenerative success was demonstrated when denervated SCs were replaced with healthy SCs cultured from a secondary nerve. This cell-replacement strategy is, however, limited in the clinical setting by the inability to obtain sufficient numbers of cells and the requirement for sacrifice of additional nerve tissue. We, along with several other groups, have therefore begun investigating stem cell therapies to improve the regenerative environment. CONCLUSION: There are several avenues of stem cell-based approaches to peripheral nerve repair. One of these, skin-derived precursor cells, are easily accessible, autologous adult stem cells that can survive and myelinate in the peripheral nerve environment and become SC-like in their apparent differentiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".