Fate of stem cell transplants in peripheral nerves
Bibliographic record
Abstract
While damaged peripheral nerves demonstrate some potential to regenerate, complete functional recovery remains infrequent, owing to a functional loss of supportive Schwann cells distal to the injury. An emerging solution to improve upon this intrinsic regenerative capacity is to supplement injured nerves with stem cells derived from various tissues. While many of these strategies have proven successful in animal models, few studies have examined the behavior of transplanted stem cells in vivo, including whether they survive and differentiate. In previous work, we demonstrated that cells derived from neonatal rodent dermis (skin-derived precursor cells, or SKPs) could improve regenerative parameters when transplanted distal to both acute and chronic nerve injuries in Lewis rats. The aim of this work was to track the fate of these cells in various nerve injury paradigms and determine the response of these cells to a known glial growth factor. Here, we report that SKPs survive, respond to local cues, differentiate into myelinating Schwann cells, and avoid complete clearance by the host's immune defenses for a minimum of 10weeks. Moreover, the ultimate fate of SKPs in vivo depends on the nerve environment into which they are injected and can be modified by inclusion of heregulin-1β.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".