Tenocyte apoptosis in the torn rotator cuff: a primary or secondary pathological event?
Bibliographic record
Abstract
Little information exists on the contribution of apoptosis to pathological tendon changes in rotator cuff tendinopathy. The purpose of this study was to quantitate the rate of tenocyte apoptosis in torn supraspinatus tendons and in the matched intact subscapularis and to examine the potential relation between apoptotic index (AI) and tendon pathology. In addition, the authors examined tenocyte density, proliferation rate and p53 gene expression patterns to gain further insight into relevant pathological mechanisms in the torn suprapinatus. 15 torn supraspinatus tendons with matched intact subscapularis tendon samples and 10 reference subscapularis samples were collected. Immunohistochemistry was used to define the AI (F7-26), proliferation rate (Ki67) and presence of p53 (M7001). Tendon degeneration was evaluated according to the Bonar scale. Expression of p53 and relevant genes (n=84) was examined on a subset of samples using microfluidic arrays. The AI was significantly increased in torn supraspinatus tendon and matched subscapularis tendon (R² =0.5742; p=0.0005). Cell density and proliferation rate were also elevated in torn supraspinatus compared with reference subscapularis tendons (p<0.05). A significant increase in p53 occurred specifically in torn supraspinatus tendon (p<0.05), and several genes encoding p53-inhibiting proteins were downregulated in association, including HDAC1 (p<0.05), MDM4 (p<0.001) and PPM1D (p<0.05). Our results suggest that tenocyte apoptosis results from more than one mechanism in the injured rotator cuff, including both intrinsic factors related specifically to the torn supraspinatus tendon, as well as a more generalised effect on the adjacent subscapularis tendon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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 teacher head, 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".