Immunoglobulin G Subclass Profile of Anticitrullinated Peptide Antibodies Specific for Epstein Barr Virus-derived and Histone-derived Citrullinated Peptides
Bibliographic record
Abstract
To the Editor: Studies have shown that the anticitrullinated peptide antibodies (ACPA) response is highly polyclonal, in terms of epitope specificity, V genes, and isotype usage1,2. Longitudinal studies of patients with rheumatoid arthritis (RA) have documented epitope spreading, and ACPA, specific for distinct citrullinated epitopes, have been described. By using different citrullinated antigens, ACPA from immunoglobulin (Ig)G, IgA, and IgM isotype have been detected3. ACPA are polyclonal in the usage of different IgG subclasses, but in this case the pattern is more heterogeneous. So far the studies conducted indicate the dominance of IgG1 and IgG4, while IgG3 have been detected with cyclic citrullinated peptide (CCP) and vimentin, but not with fibrinogen4,5. The production of specific IgG subclasses might help in deciphering the mechanisms eliciting B cell expansion in response to different antigens. Thus, it is of interest to explore the profile of IgG subclasses of antibodies reactive with novel citrullinated substrates, already known to be tools for ACPA detection. Ninety-three patients with RA, 25 with psoriatic arthritis, 15 with ankylosing spondylitis, and … Address correspondence to Professor P. Migliorini, Department of Clinical and Experimental Medicine, University of Pisa, Via Roma 67, 56126 – Pisa, Italy. E-mail: paola.migliorini{at}med.unipi.it
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".