Establishment of international autoantibody reference standards for the detection of autoantibodies directed against PML bodies, GW bodies, and NuMA protein
Bibliographic record
Abstract
Objectives: Reference materials are important in the standardization of autoantibody testing and only a few are freely available for many known autoantibodies. Our goal was to develop three reference materials for antibodies to PML bodies/multiple nuclear dots (MND), antibodies to GW bodies (GWB), and antibodies to the nuclear mitotic apparatus (NuMA). Methods: Reference materials for identifying autoantibodies to MND (MND-REF), GWB (GWB-REF), and NuMA (NuMA-REF) were obtained from three donors and validated independently by seven laboratories. The sera were characterized using indirect immunofluorescence assay (IFA) on HEp-2 cell substrates including two-color immunofluorescence using antigen-specific markers, western blot (WB), immunoprecipitation (IP), line immunoassay (LIA), addressable laser bead immunoassay (ALBIA), enzyme-linked immunosorbent assay (ELISA), and immunoprecipitation-mass spectrometry (IP-MS). Results: MND-REF stained 6-20 discrete nuclear dots that colocalized with PML bodies. Antibodies to Sp100 and PML were detected by LIA and antibodies to Sp100 were also detected by ELISA. GWB-REF stained discrete cytoplasmic dots in interphase cells, which were confirmed to be GWB using two-color immunofluorescence. Anti-Ge-1 antibodies were identified in GWB-REF by ALBIA, IP, and IP-MS. All reference materials produced patterns at dilutions of 1:160 or greater. NuMA-REF produced fine speckled nuclear staining in interphase cells and staining of spindle fibers and spindle poles. The presence of antibodies to NuMA was verified by IP, WB, ALBIA, and IP-MS. Conclusions: MND-REF, GWB-REF, and NuMA-REF are suitable reference materials for the corresponding antinuclear antibodies staining patterns and will be accessible to qualified laboratories.
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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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".