Circulating biomarkers in acute myofascial pain
Bibliographic record
Abstract
The aims of the present study were to compare levels of circulating inflammatory biomarkers and growth factors between patients with myofascial pain syndrome (MPS) and healthy control participants, and to assess the relationship among inflammatory markers and growth factors in the two groups.Biomarkers levels were assessed in patients (n = 37) with myofascial pain complaints recruited from the hospital emergency department and non-MPS controls (n = 21), recruited via advertisements in the hospital and community.Blood levels of the cytokines, namely, interleukin-6 (IL-6), tumor necrosis factor (TNF), and interleukin-12 (IL-12), and the chemokine, namely, monocyte chemoattractant protein-1 (MCP-1), macrophage-derived chemokine (MDC), eotaxin, granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-8 (IL-8), and macrophage inflammatory proteins-1β (MIP-1β) were significantly higher in patients with MPS than controls. The results of the growth factor analyses revealed significantly higher levels of fibroblast growth factor-2 (FGF-2), platelet-derived growth factor (PDGF), and vascular endothelial growth factor (VEGF) in MPS patients versus controls. The pattern of correlation coefficients between cytokines and growth factors differed considerably for MPS patients and controls with far fewer significant positive coefficients observed in the controls. Serum inflammatory and growth factor biomarkers were elevated in MPS patients.Inflammatory biomarkers and growth factor levels may play an important role in the onset and maintenance of MPS and therefore may be useful in the diagnosis and treatment of MPS. Understanding the mechanisms of inflammation in MPS necessitates future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".