Growth factor and protease expression during different phases of healing after rabbit deep flexor tendon repair
Bibliographic record
Abstract
The purpose of the study was to contribute to the mapping of molecular events during flexor tendon healing, in particular the growth factors insulin-like growth factor-1 (IGF-1), vascular endothelial growth factor (VEGF) and nerve growth factor (NGF), matrix metalloproteinases (MMP-3 and MMP-13) and their inhibitors (tissue inhibitors of metalloproteinases, TIMP-1 and TIMP-3, and the protease cathepsin K. In a rabbit model of flexor tendon injury, the mRNA expression for the growth factors, MMPs and TIMPs were measured in tendon and tendon sheath tissue at several time points (3, 6, 21, and 42 days) representing different phases of the healing process. We found that MMP-13 remained increased during the study period, whereas MMP-3 returned to normal levels within the first week after injury. TIMP-3 was down-regulated in the tendon sheaths. Cathepsin K was up-regulated in tendons and sheaths after injury. NGF was present in both tendons and sheaths, but unaltered. IGF-1 exhibited a late increase in the tendons, while VEGF was down-regulated at the later time points. In conclusion, we have demonstrated the presence of NGF in flexor tendons. MMP-13 expression appears to play a more protracted role in flexor tendon healing than MMP-3. The relatively low levels of endogenous IGF-1 and VEGF mRNA following injury support their potential beneficial role as exogenous modulators to optimize tendon healing and strength without increasing adhesion formation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".