Decorin antisense gene therapy improves functional healing of early rabbit ligament scar with enhanced collagen fibrillogenesis <i>in vivo</i>
Bibliographic record
Abstract
Injured ligaments heal with scar tissue, which has mechanical properties inferior to those of normal ligament, potentially resulting in re-injury, joint instability, and subsequent degenerative arthritis. In ligament scars, normal large-diameter collagen fibrils have been shown to be replaced by a homogenous population of small collagen fibrils. Because collagen is a major tensile load-bearing matrix element and because the proteoglycan decorin is known to inhibit collagen fibrillogenesis, we hypothesized that the restoration of larger collagen fibrils in a rabbit ligament scar, by down-regulating the proteoglycan decorin, would improve the mechanical properties of scar. In contrast to sense and injection-treated controls, in vivo treatment of injured ligament by antisense decorin oligodeoxynucleotides led to an increased development of larger collagen fibrils in early scar and a significant improvement in both scar failure strength (83-85% improvement at 6 weeks; p < 0.01) and scar creep elongation (33-48% less irrecoverable creep; p < 0.03) under loading. This is the first report that in vivo manipulation of collagen fibrillogenesis improves tissue function during repair processes with gene therapy. These findings not only suggest the potential use of this type of approach to improve the healing of various soft tissues (skin, ligament, tendon, and so on) but also support the use of such methods to better understand specific structure-function relationships in scars.
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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) | 0.001 | 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 | 0.001 |
| 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".