INFLAMMATORY MECHANISMS INVOLVED IN TENDON OVERUSE: RECENT INSIGHTS
Bibliographic record
Abstract
This presentation will synthesise and review recent findings from our laboratory and others' on inflammatory mechanisms involved in tendon overuse injuries. Tendinopathy is a state of chronic tendon thickening and pain, usually as a result of overuse. Recent work has shown that repetitive loading of human tendon-derived cells leads to upregulation of TAC1, the mRNA encoding for Substance P1—a pro-inflammatory neuropeptide which is known to contribute to tendon pain.2 In addition, previous work has found that mast cells are present in greater numbers in chronically overused tendon,3 and that they degranulate in response to SP, releasing substances that directly influence human tenocyte collagen remodelling. Thus, both SP and one of its main targets—the mast cell—are over-represented in tendinopathic tissue. Mast cells have a high affinity for human tenocytes, forming elaborate inter-cellular junctions, and releasing substances which substantially influence tenocyte behaviour that could have relevance for improving outcomes following injury or overuse. For example, inhibiting mast cells results in a reduction of TGF-beta-related genes (related to scarring) and reduces the amount of tendon thickening following injury.4 Additionally, mast cells induce a large increase in COX-2 expression by human tenocytes. It is possible that a clinical strategy which incorporates inhibitors of mast cells could improve on outcomes with standard treatment.5
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".