Inflammation activation and resolution in human tendon disease
Bibliographic record
Abstract
Improved understanding of the role of inflammation in tendon disease is required to facilitate therapeutic target discovery. We studied supraspinatus tendons from patients experiencing pain before and after surgical subacromial decompression treatment. Tendons were classified as having early, intermediate, or advanced disease, and inflammation was characterized through activation of pathways mediated by interferon (IFN), nuclear factor κB (NF-κB), glucocorticoid receptor, and signal transducer and activator of transcription 6 (STAT-6). Inflammation signatures revealed expression of genes and proteins induced by IFN and NF-κB in early-stage disease and genes and proteins induced by STAT-6 and glucocorticoid receptor activation in advanced-stage disease. The proresolving proteins FPR2/ALX and ChemR23 were increased in early-stage disease compared to intermediate- to advanced-stage disease. Patients who were pain-free after treatment had tendons with increased expression of CD206 and ALOX15 mRNA compared to tendons from patients who continued to experience pain after treatment, suggesting that these genes and their pathways may moderate tendon pain. Stromal cells from diseased tendons cultured in vitro showed increased expression of NF-κB and IFN target genes after treatment with lipopolysaccharide or IFNγ compared to stromal cells derived from healthy tendons. We identified 15-epi lipoxin A4, a stable lipoxin isoform derived from aspirin treatment, as potentially beneficial in the resolution of tendon inflammation.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".