Mast cells exert pro-inflammatory effects of relevance to the pathophyisology of tendinopathy
Bibliographic record
Abstract
INTRODUCTION: We have previously found an increased mast cell density in tendon biopsies from patients with patellar tendinopathy compared to controls. This study examined the influence of mast cells on basic tenocyte functions, including production of the inflammatory mediator prostaglandin E2 (PGE2), extracellular matrix remodeling and matrix metalloproteinase (MMP) gene transcription, and collagen synthesis. METHODS: Primary human tenocytes were stimulated with an established human mast cell line (HMC-1). Extracellular matrix remodeling was studied by culturing tenocytes in a three-dimensional collagen lattice. Survival/proliferation was assessed with the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium salt (MTS) assay. Levels of mRNA for COX-2, COL1A1, MMP1, and MMP7 were determined by quantitative real-time polymerase chain reaction (qPCR). Cox-2 protein level was assessed by Western blot analysis and type I procollagen was detected by immunofluorescent staining. PGE2 levels were determined using an enzyme-linked immunosorbent assay (ELISA). RESULTS: Mast cells stimulated tenocytes to produce increased levels of COX-2 and the pro-inflammatory mediator PGE2, which in turn decreased COL1A1 mRNA expression. Additionally, mast cells reduced the type I procollagen protein levels produced by tenocytes. Transforming growth factor beta 1 (TGF-β1) was responsible for the induction of Cox-2 and PGE2 by tenocytes. Mast cells increased MMP1 and MMP7 transcription and increased the contraction of a three-dimensional collagen lattice by tenocytes, a phenomenon which was blocked by a pan-MMP inhibitor (Batimastat). CONCLUSION: Our data demonstrate that mast cell-derived PGE2 reduces collagen synthesis and enhances expression and activities of MMPs in human tenocytes.
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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) | 0.001 | 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 | 0.000 |
| 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 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".