Correlation of synovial tissue protein abundance with menopause in osteoarthritis.
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is a common articular disorder. Epidemiologic surveys show a higher prevalence of OA in women than men and that morbidity is higher during menopause. We aimed to explore whether menopause influences the clinical recovery of a knee joint following OA and injury, and identify associated mechanisms by analyzing the proteomic profile of synovial tissue (ST) samples. METHODS: Routine blood examination and hormone level tests were conducted before surgery. ST samples from eight participants were collected intraoperatively for proteomic analysis. One day before and one month after the surgery, we assessed various aspects of function in the affected knee including the with Visual Analog Score (VAS), Lysholm, The Western Ontario, and McMaster Universities Osteoarthritis Index (WOMAC) scores. The relationships between proteomic data, estrogen levels, and affected knee function were compared and analyzed. This was a retrospective study. RESULT: Menopause was associated with the clinical outcomes of knee OA and knee injuries. ST proteomic data identified that 80 proteins in premenopausal OA females were significantly different from menopausal OA females. In addition, 100 proteins were significantly different between premenopausal OA females and premenopausal injured females. CONCLUSIONS: Age and menopause showed a positive correlation with the protein profile of ST from OA or knee injury female patients, indicating that the protein components might be affected by menopause. Postoperative clinical outcomes were affected by menopause. We conclude that menopause may, in part, regulate knee joint function by altering ST protein expression.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".