Autoantibody Against Survivin in Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: In patients with systemic sclerosis (SSc), to determine concentrations of antibodies against survivin and their clinical association with SSc, and to evaluate serum survivin concentrations. METHODS: Anti-survivin antibody was examined by ELISA and immunoblotting using human recombinant survivin. Serum survivin levels were assessed by ELISA. RESULTS: IgG but not IgM anti-survivin antibody levels in patients with SSc were significantly higher than those in healthy controls and patients with systemic lupus erythematosus (SLE). When cutoff values were set as mean + 2 SD of control, IgG anti-survivin antibodies were positive in 41% (25/61) of patients with SSc, while they were detected in only 1 healthy individual (3%, 1/29) and 1 patient with SLE (5%, 1/20). Regarding the clinical correlation, patients with SSc who were positive for IgG anti-survivin antibody exhibited significantly longer disease duration than those who were negative. Immunoblotting analysis confirmed the presence of anti-survivin antibody in sera from patients with SSc. Serum survivin levels in patients with SSc were also significantly higher than in controls and patients with SLE. CONCLUSION: Our results suggest that autoantibody against survivin is generated in patients with SSc, especially those with long disease duration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".