Mesenchymal stem cells and local tacrolimus delivery synergistically enhance neurite extension
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the combined effect of mesenchymal stem cells (MSC) and local delivery of tacrolimus (FK506) on nerve regeneration when applied to nerve autografts and decellularized allografts. METHODS: A three-dimensional in vitro compartmented cell culture system consisting of a neonatal dorsal root ganglion adjacent to a nerve graft was used to evaluate the regenerating neurites into the peripheral nerve scaffold. Nerve autografts and allografts were treated with (i) undifferentiated MSCs, (ii) FK506 (100 ng/mL) or (iii) both (N = 9/group). After 48 hours, neurite extension was measured to quantify nerve regeneration and stem cell viability was evaluated. RESULTS: Stem cell viability was confirmed in all MSC-treated grafts. Neurite extension was superior in autografts treated with FK506, and MSCs and FK506 combined (p < 0.001 and p = 0.0001, respectively), and autografts treated with MSCs (p = 0.12) were comparable to untreated autografts. In allografts, FK506 treatment and combined treatment were superior to controls (p < 0.001 and p = 0.0001, respectively), and treatment with MSCs (p = 0.09) was comparable to controls. All autograft groups were superior compared to their respective allograft treatment group (p < 0.05) in neurite extension. CONCLUSIONS: Alone, either MSC or FK506 treatment improved neurite outgrowth, and combined they further enhanced neurite extension in both autografts and allografts.
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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".