Surgical menopause initiates molecular changes that do not result in mechanical changes in normal and healing ligaments
Bibliographic record
Abstract
OBJECTIVES: Ligaments which heal spontaneously have a healing process that is similar to skin wound healing. Menopause impairs skin wound healing and may likewise impair ligament healing. Our purpose in this study was to investigate the effect of surgical menopause on ligament healing in a rabbit medial collateral ligament model. METHODS: Surgical menopause was induced with ovariohysterectomy surgery in adult female rabbits. Ligament injury was created by making a surgical gap in the midsubstance of the medial collateral ligament. Ligaments were allowed to heal for six or 14 weeks in the presence or absence of oestrogen before being compared with uninjured ligaments. Molecular assessment examined the messenger ribonucleic acid levels for collagens, proteoglycans, proteinases, hormone receptors, growth factors and inflammatory mediators. Mechanical assessments examined ligament laxity, total creep strain and failure stress. RESULTS: Surgical menopause in normal medial collateral ligaments initiated molecular changes in all the categories evaluated. In early healing medial collateral ligaments, surgical menopause resulted in downregulation of specific collagens, proteinases and inflammatory mediators at 6 weeks of healing, and proteoglycans, growth factors and hormone receptors at 14 weeks of healing. Surgical menopause did not produce mechanical changes in normal or early healing medial collateral ligaments. With or without surgical menopause, healing ligaments exhibited increased total creep strain and decreased failure stress compared with uninjured ligaments. CONCLUSIONS: Surgical menopause did not affect the mechanical properties of normal or early healing medial collateral ligaments in a rabbit model. The results in this preclinical model suggest that menopause may result in no further impairment to the ligament healing process. Cite this article: Bone Joint Res 2015;4:38-44.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".