Abnormalities in the Regulators of Angiogenesis in Patients with Scleroderma
Bibliographic record
Abstract
OBJECTIVE: To determine plasma levels of regulators of angiogenesis in patients with scleroderma and to correlate those levels with manifestations of scleroderma-related vascular disease. METHODS: Plasma levels of vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), fibroblast growth factor-2 (FGF-2), matrix metalloproteinase-9 (MMP-9), endostatin, pro-MMP-1, hepatocyte growth factor (HGF), placental growth factor (PlGF), and FGF-4 were examined by ELISA in a cross-sectional study of 113 patients with scleroderma and 27 healthy controls. Simple and multivariate regression models were used to look for associations between factor levels and clinical disease characteristics. RESULTS: There were marked differences in the levels of pro-angiogenic growth factors between patients with scleroderma and controls, with significant elevations of VEGF, PDGF, FGF-2, and PlGF among patients with scleroderma (p < 0.0001). Levels of MMP were also higher in scleroderma patients compared to controls (MMP-9 and pro-MMP-1) (p < 0.0001). Levels of the pro-angiogenic and anti-fibrotic factor, HGF, were noted to be lower in patients with scleroderma, but had a positive correlation with right ventricular systolic pressure (RVSP) as measured by echocardiogram (p < 0.0001) and the Raynaud Severity Score (p = 0.05). Endostatin (an anti-angiogenic factor) was notably higher in patients with scleroderma (p < 0.0001) and also correlated positively with RVSP (p = 0.023). CONCLUSION: These results demonstrate striking abnormalities in the circulating regulators of angiogenesis in patients with scleroderma. The levels of some factors correlate with measures of vascular disease among patients with scleroderma. Dysregulated angiogenesis may play a role in the development of scleroderma vascular disease.
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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 | 0.001 |
| 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".