Do different tendons exhibit the same response following chronic exposure to statins?
Bibliographic record
Abstract
Over the past few years, a number of cases of tendon injuries associated with statin therapy have been reported. In this study, we assessed whether statins can affect the extracellular matrix (ECM) of the deep digital flexor tendon (DDFT) and patellar tendon (PT). Wistar rats were assigned to groups treated with atorvastatin (A20, A80), treated with simvastatin (S20, S80), and control. Zymography, Western blotting for collagen I, non-collagenous proteins (NCP), glycosaminoglycans (GAGs), and hydroxyproline quantifications were performed. DDFT findings: NCP were increased in A20 and A80; higher concentration of hydroxyproline was found in S80; levels of GAGs was increased in all statin-treated groups; collagen I was increased in S80 and pro-MMP-2 activity was reduced in A80, S20, and S80. PT findings: NCP were reduced in A20, A80, and S80; GAGs was reduced in A80 and S20; collagen I was increased in A20 and pro-MMP-2 activity was reduced in the S20. Both the statins provoked marked changes in both tendons. All these changes may make the tendons more prone to microdamage and ruptures. Therefore, a better understanding of the behavior of the tendon ECM components under statin therapy may provide important insights into the mechanisms behind statin-induced tendon injuries.
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) | 0.001 | 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 | 0.001 | 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".