Elevated Fab glycosylation of autoantibodies maintained during B cell depletion therapy
Bibliographic record
Abstract
Several chronic autoimmune diseases are characterized by elevated autoantibody Fab glycosylation. Whether Fab glycans link to disease state or development remains unclear, yet may serve as a marker thereof. Many autoimmune diseases are treated with B cell depletion therapies that particularly result in a decline of autoantibodies. The question arises whether B cell depletion therapy may have an impact on Fab glycosylation. Here, we investigated the longitudinal effects of B cell depletion therapy on Fab glycosylation of total IgG and IgG autoantibodies in rheumatoid arthritis (RA), pemphigus vulgaris (PV), ANCA-associated vasculitis (AAV), and multiple sclerosis (MS). Baseline Fab glycosylation was compared to 6-12 months into therapy by lectin affinity chromatography, determining Fab sialylation as an estimate of Fab glycosylation. We observed a modest decrease in Fab glycosylation of total IgG for RA (median 13.8%[IQR 11.7-16.3] - 9.1%[IQR8-11]) and PV (16.4%[IQR14.9-17.5] - 13.01%[IQR10.8-15.5]) after 6 months, whereas for AAV Fab glycosylation slightly increased (11.6%[IQR7.4-15] - 14.9%[IQR11.4-19.3]), and no changes were found for MS. Autoantibody titers (anti-CCP, anti-PR3, anti-Dsg3) had declined following B cell depletion therapy, yet their elevated Fab glycosylation levels were maintained. Taken together, Fab glycosylation levels of autoantibodies do not decrease upon B cell depletion therapy, thereby retaining their predictive potential as biomarker.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".