Establishment of an international autoantibody reference standard for human anti-DFS70 antibodies: proof-of-concept study for a novel Megapool strategy by pooling individual specific sera
Bibliographic record
Abstract
Background International autoantibody standards, traditionally based on material obtained from plasmapheresis of single subjects, represent individual immune response and may not comprehend the heterogeneity of the general population. The anti-DFS70 autoantibody yields a characteristic dense fine speckled (DFS) nuclear pattern on indirect immunofluorescence assay on HEp-2 cells (HEp-2 IFA) and speaks against autoimmunity. We propose a novel strategy for developing autoantibody reference standards, based on stepwise pooling of serum samples from hundreds of individuals with anti-DFS70 antibodies. Methods Within a 2-year period, serum samples were selected from routine HEp-2 IFA according to the following criteria: DFS HEp-2 IFA pattern at titer ≥1:640; anti-DFS70 reactivity in three analyte-specific tests (Western blot [WB], enzyme-linked immunosorbent assay [ELISA] and chemiluminescent immunoassay [CLIA]). Aliquots of individual samples were combined into progressively larger pools with stepwise validation of intermediary pools as for individual samples. Validated intermediary pools were merged into a final pool for lyophilization. Results A total of 741 validated samples yielded a 750 mL final pool that was lyophilized into thousands of 200 μL-aliquots. Reconstituted aliquots yielded the expected anti-DFS70 reactivity in ELISA, CLIA and WB, as well as high-titer DFS HEp-2 IFA pattern. The appropriate anti-DFS70 reactivity of the lyophilized pool was confirmed by seven international expert centers, using HEp-2 IFA, ELISA, WB and immunoprecipitation. Conclusions This proof-of-concept study provides an innovative and efficient strategy to build serum reference standards for autoantibody testing. The anti-DFS70 standard will integrate the panel of standards of Autoantibody Standardization Committee (ASC, www.autoab.org), contributing to education for proper assay validation and interpretation of the DFS pattern and other HEp-2 IFA patterns.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".