Association Between Centromere- and Topoisomerase-specific Immune Responses and the Degree of Microangiopathy in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Autoreactive antibody responses, including the use of several isotypes of autoantibodies, have been shown to be associated with clinical outcome in several rheumatic autoimmune diseases. The goals of this study were to evaluate whether (1) anticentromere antibody (ACA)- and antitopoisomerase antibody (ATA)-specific isotype expression, and (2) organ involvement are associated with the degree of microangiopathy in systemic sclerosis (SSc). METHODS: ACA and ATA IgG, IgM, and IgA levels were measured in baseline serum samples of ACA IgG-positive (+) and ATA IgG+ patients with SSc. The degree of microangiopathy was determined based on nailfold videocapillaroscopy (NVC) images collected at the same point in time. Logistic regression analyses with autoantibodies, clinical characteristics, isotype expression, and ACA and ATA IgG, IgM, and IgA levels as independent variables, and NVC pattern as the dependent variable were performed. RESULTS: In 164 patients, isotype levels and degree of microangiopathy were evaluated. Logistic regression confirmed the association of the degree of microangiopathy with the presence of digital ulcers (OR 3.07, 95% CI 1.43-6.60), interstitial lung disease (OR 3.41, 95% CI 1.11-10.61), and pulmonary arterial hypertension (OR 5.58, 95% CI 2.05-17.81). ATA positivity was associated with more severe microangiopathy (OR 2.09, 95% CI 1.05-4.13). Patients who expressed solely ACA IgG showed a trend towards less severe microangiopathy compared to patients also expressing ACA IgM and/or IgA. Levels of ACA IgG and ATA IgM were found to be associated with microangiopathy severity. CONCLUSION: We observed an association between ACA and ATA responses and the degree of microangiopathy in SSc. These findings might indicate that the breadth of the autoimmune response, as reflected by autoantibody production and microvascular damage, interacts in the pathophysiology of 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 | 0.000 |
| 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) | 0.002 | 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".