NOVEL DIAGNOSTIC BIOMARKER OF SYSTEMIC LUPUS ERYTHEMATOSUS: ANTICHAPERONIN CONTAINING T-COMPLEX POLYPEPTIDE 1 ANTIBODY
Bibliographic record
Abstract
PV037 / #22 Poster Topic: AS04 - Biomarkers Background/Purpose Systemic lupus erythematosus (SLE) is diagnosed with several clinical and immunological criteria. To find a diagnostic biomarker of SLE, microarray technique was used to find SLE-specific autoantibodies and to provide a clear diagnosis. Methods Autoantibodies were discovered by analyzing sera of SLE patients and normal controls (NCs) using a human proteome microarray containing 21,000 purified proteins. This analysis revealed the presence of 63 SLE-specific autoantibodies. Notably, the antichaperonin containing t-complex polypeptide 1 (TCP1) antibody exhibited higher expression in patients with SLE. To validate the specificity of anti-TCP1 antibody expression in SLE, Dot blot analysis and enzyme-linked immunosorbent assay (ELISA) were conducted using sera from patients with SLE, NCs, and patients with rheumatoid arthritis, Behçet’s disease, and systemic sclerosis. Results Dot blot analysis was conducted using sera from patients with SLE and NCs, as well as patients with rheumatoid arthritis, Behçet’s disease, and systemic sclerosis. The results confirmed the detection of anti-TCP1 antibody in 79 out of 100 patients with SLE, with significantly elevated expression compared to both NCs and patients with other autoimmune diseases. We performed enzyme-linked immunosorbent assay (ELISA) to determine the relative amounts of anti-TCP1 antibody. ELISA analysis revealed markedly elevated anti-TCP1 antibody levels in the sera of patients with SLE (50.1 ± 17.3 AU, n=251) compared to those of NCs (33.9 ± 9.3 AU, n=50), RA (35 ± 8.7 AU, n=25), BD (37.5 ± 11.6 AU, n=28), and SSc (43 ± 11.9 AU, n=30). Conclusions These data suggest that anti-TCP1 antibody is a potential diagnostic biomarker of SLE.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".