<i>Scleraxis</i>expression is coordinately regulated in a murine model of patellar tendon injury
Bibliographic record
Abstract
This study investigated the expression of Scleraxis in a murine model of patellar tendon injury in which the central third of the patellar tendon was unilaterally injured. The presence of tendon pathology was assessed using dual photon microscopy, conventional histology and microCT. Tendon pathology was also quantified noninvasively over a 12-week period using high-frequency ultrasound and laser Doppler flowmetry. Gene expression (Scx, Tnmd, and Col1a1) was determined at defined end-points (1, 4, 8, and 12 weeks) using qPCR on RNA from individual patellar tendons on injured and uninjured sides. There was significant development of tendon pathology as gauged by ultrasound and laser Doppler over 12 weeks. Injured tendons demonstrated significant histological and microCT evidence of pathological change, and disorganized collagen with reduced density. The expression of Scx and Col1a1 was unchanged at 1 week, significantly upregulated at 4 and 8 weeks, and had returned to baseline by 12 weeks. Tnmd expression was unchanged at 1 week, and significantly increased at 4, 8, and 12 weeks. Patellar tendon injury was associated with marked increases in the expression of Scx, Tnmd, and Col1a1. Our data suggest new roles for Scleraxis in coordinating the response to injury in the pathogenesis of tendon disorders.
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 | 0.001 | 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 | 0.001 |
| 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".