Exosomes combined with biosynthesized cellulose conduits improve peripheral nerve regeneration
Bibliographic record
Abstract
Peripheral nerve injury is one of the more common forms of peripheral nerve disorders, and the most severe type of peripheral nerve injury is a defect with a gap. Biosynthetic cellulose membrane (BCM) is a commonly used material for repair and ligation of nerve defects with gaps. Meanwhile, exosomes from mesenchymal stem cells can promote cell growth and proliferation. We envision combining exosomes with BCMs to leverage the advantages of both to promote repair of peripheral nerve injury. Prepared exosomes were added to BCMs to form exosome-loaded BCMs (EXO-BCM) that were used for nerve repair in a rat model of sciatic nerve defects with gaps. We evaluated the repair activity using a pawprint experiment, measurement and statistical analyses of sciatica function index and thermal latency of paw withdrawal, and quantitation of the number and diameter of regenerated nerve fibers. Results indicated that EXO-BCM produced comprehensive and durable repair of peripheral nerve defects that were similar to those for autologous nerve transplantation, the gold standard for nerve defect repair. EXO-BCM is not predicted to cause donor site morbidity to the patient, in contrast to autologous nerve transplantation. Together these results indicate that an approach using EXO-BCM represents a promising alternative to autologous nerve transplantation, and could have broad applications for repair of nerve defects.
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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 | 0.001 |
| 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".