Correlation of Biomarkers of Endothelium Dysfunction and Matrix Remodeling in Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a multisystem disease characterized by microvascular dysfunction and excessive fibrosis. However, the relationship between these 2 features remains unclear. Endothelial dysfunction can be assessed by quantifying plasma asymmetric dimethylarginine (ADMA), an endogenous inhibitor of endothelial nitric oxide synthase. Matrix remodeling can be assessed by quantifying serum tissue inhibitor of matrix metalloproteinases-1 (TIMP-1). Both biomarkers are elevated in patients with SSc. Our objective was to test whether plasma ADMA is correlated with serum TIMP-1. METHODS: We enrolled 91 subjects, 39 patients with SSc, 28 patients with primary Raynaud's phenomenon (RP), and 24 healthy volunteers. Plasma ADMA concentrations were measured by liquid chromatography-tandem mass spectrometry. Serum TIMP-1 concentrations were determined by ELISA. RESULTS: Mean ADMA concentrations were higher in patients with SSc (0.68 microM +/- 0.12) than in patients with primary RP or healthy volunteers (respectively, 0.56 microM +/- 0.14 and 0.62 microM +/- 0.12; p = 0.002). Median serum TIMP-1 concentrations were increased in patients with SSc compared to primary RP and healthy volunteers [12 (9-15), 11 (8-13), and 10 (7-13) nM, respectively; p = 0.05]. In the SSc group, we observed a statistically significant correlation between plasma ADMA and serum TIMP-1 (r = 0.34, p = 0.035). CONCLUSION: These data are consistent with our hypothesis of an association of endothelial dysfunction and matrix remodeling in scleroderma spectrum disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".