MAGE-B2 Autoantibody: A New Biomarker for Pediatric Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Melanoma-associated antigen gene B2 (MAGE-B2) encodes an embryonic antigen normally silenced after birth except in testis and placenta. We identified the MAGE-B2 gene and autoantibodies in pediatric patients with systemic lupus erythematosus (SLE) glomerulonephritis. We investigated the prevalence of MAGE-B2 autoantibodies in association with active SLE, to determine a pathogenetic role of MAGE-B2 protein through its distribution in cells and tissues. METHODS: A cross-sectional study analyzed the frequency of MAGE-B2 autoantibodies in 40 patients with pediatric SLE, 23 adult controls, and 16 patients with pediatric juvenile rheumatoid arthritis (JRA) using Western blots containing recombinant MAGE-B2. SLE Disease Activity Index 2000 (SLEDAI-2K) and British Isles Lupus Assessment Group (BILAG) index measured SLE disease activity. Tissue distribution of MAGE-B2 protein was assessed by immunohistochemistry, immunofluorescence, and Western blots. RESULTS: Seventeen (43%) of 40 pediatric SLE patients had MAGE-B2 autoantibodies as compared to 0 of 16 JRA patients and 2 of 23 adult controls. SLE disease activity was significantly higher in MAGE-B2 autoantibody-positive versus autoantibody-negative patients (SLEDAI-2K, mean 10.9 vs 5.2, p = 0.013; BILAG, mean 15.3 vs 6.3, p = 0.023). Active nephritis was more prevalent (60% vs 24%) in MAGE-B2 autoantibody-positive than autoantibody-negative SLE patients. MAGE-B2 protein was visualized in SLE kidney proximal convoluted tubules and in tumor epithelial cells, but not in lymphoblastoid cells. CONCLUSION: MAGE-B2 autoantibody appears to be a clinically relevant biomarker for pediatric SLE disease activity and nephritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".