Association of Type III and Type VI Collagen Neoepitopes With Disease Severity in Systemic Sclerosis
Bibliographic record
Abstract
Objective Dysregulated collagen turnover is implicated in systemic sclerosis (SSc) pathogenesis. We evaluated collagen turnover biomarkers in relation to the severity of fibrotic manifestations, key cytokines, and progression in SSc. Methods Baseline and 6-month serum samples of patients with early SSc in the Collaborative National Quality and Efficacy Registry (CONQUER) cohort were analyzed for type III (pro-collagen III [PRO-C3] and collagen III M [C3M]) and type VI (pro-collagen VI [PRO-C6] and collagen VI M [C6M]) collagen turnover biomarkers, as well as C-reactive protein (CRP), interleukin 6 (IL-6), and interferon (IFN)-inducible proteins. The modified Rodnan skin score and % predicted forced vital capacity (FVC%) served as surrogate markers of disease severity. Results A total of 222 patients were included. PRO-C3 ( P < 0.001) and PRO-C6 ( P < 0.001) concentrations were higher in patients with diffuse disease, whereas C6M ( P = 0.04) was higher in those with interstitial lung disease. Baseline PRO-C3 ( P < 0.001) and PRO-C6 ( P < 0.001) positively correlated with mRSS, whereas C3M ( P = 0.03) and C6M ( P = 0.01) negatively correlated with FVC%, although the magnitude of the observed correlations was in the weak range ( r s < 0.4). Collagen biomarker concentrations positively correlated with CRP, IL-6, and IFN-inducible proteins at baseline. Although changes in CRP positively correlated with changes in collagen degradation protein levels (C3M and C6M), they did not correlate with changes in collagen formation protein levels (PRO-C3 and PRO-C6). In contrast, changes in IFN score showed the highest correlation with changes in PRO-C6. Conclusion PRO-C3 and PRO-C6 correlated with skin disease severity, whereas C3M and C6M correlated with lung disease severity. Collagen turnover biomarkers correlated with CRP, IL-6, and IFN-inducible proteins, providing support for the link between inflammation and fibrosis in SSc.
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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) | 0.000 | 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 | 0.000 | 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".