Pifithrin-α, an inhibitor of p53 transactivation, alters the inflammatory process and delays tendon healing following acute injury
Bibliographic record
Abstract
Transcription factor p53, which was initially associated with cancer, has now emerged as an important regulator of inflammation and extracellular matrix homeostasis, two processes highly relevant to tendon repair. The goal of this study was to evaluate the effect of a p53 transactivation inhibitor, namely, pifithrin-alpha, on the pathophysiological sequence following collagenase-induced tendon injury. Administration of pifithrin-alpha during the inflammatory phase reduced the accumulation of neutrophils and macrophages by 30 and 40%, respectively, on day 3 postinjury. Pifithrin-alpha failed to reduce the percentage of apoptotic cells following collagenase injection but delayed functional recovery. In uninjured Achilles tendons, pifithrin-alpha increased metalloprotease activity 2.4-fold. Accordingly, pifithrin-alpha reduced the collagen content in intact tendons as well as in injured tendons 7 days posttrauma compared with placebo. The effect of pifithrin-alpha on load to failure and stiffness was also evaluated. The administration of pifithrin-alpha during the inflammatory phase did not significantly decrease the functional deficit 3 days posttrauma. More importantly, load to failure and stiffness were significantly decreased in the pifithrin-alpha group from day 7 to day 28 compared with placebo. Overall, our results suggest that administration of pifithrin-alpha alters the inflammatory process and delays tendon healing. The present findings also support the concept that p53 can regulate extracellular matrix homeostasis in vivo.
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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) | 0.000 | 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".