Nerve growth factor improves ligament healing
Bibliographic record
Abstract
Previous work has shown that innervation participates in normal ligament healing. The present study was performed to determine if exogenous nerve growth factor (NGF) would improve the healing of injured ligament by promoting reinnervation, blood flow, and angiogenesis. Two groups of 30 Sprague-Dawley rats underwent unilateral medial collateral ligament transection (MCL). One group was given 10 microg NGF and the other was given PBS via osmotic pump over 7 days after injury. After 7, 14, and 42 days, in vivo blood flow was measured using laser speckle perfusion imaging (LSPI). Morphologic assessments of nerve density, vascularity, and angiogenesis inhibitor production were done in three animals at each time point by immunohistochemical staining for the pan-neuronal marker PGP9.5, the endothelial marker vWF, and the angiogenesis inhibitor thrombospondin-2 (TSP-2). Ligament scar material and structural mechanical properties were assessed in seven rats at each time point. Increased nerve density was promoted by NGF at both 14 and 42 days. Exposure to NGF also led to increased ligament vascularity, as measured by histologic assessment of vWF immunohistochemistry, although LSPI-measured blood flow was not significantly different from controls. NGF treatment also led to decreased expression of TSP-2 at 14 days. Mechanical testing revealed that exposure to NGF increased failure load by 40%, ultimate tensile strength by 55%, and stiffness by 30% at 42 days. There were no detectable differences between groups in creep properties. The results suggest that local application of NGF can improve ligament healing by promoting both reinnervation and angiogenesis, and results in scars with enhanced mechanical properties.
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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) | 0.002 | 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".