Quantitation of anti–factor VIII antibodies in human plasma
Bibliographic record
Abstract
The presence of antibodies (Abs) in hemophilia A patients can potentially influence the therapeutic qualities of factor VIII (fVIII) administration. Much work has been focused on the presence of inhibitory antibodies, whereas the quantitation of noninhibitory anti-fVIII antibodies has been largely undetermined. Our objective was to develop a sensitive and specific fluorescence-based immunoassay (FLI) for the quantitation of anti-fVIIIAbs in human plasma. Affinity-purified human anti-fVIIIAb, isolated from a hemophilia A subject, was used as a calibrator with a detectability limit of 40 (+/-1.5) pM. The calibrator and the human plasma anti-fVIIIAb were captured on recombinant fVIII (rfVIII)- coupled microspheres and probed with mouse anti-human Ig-R-phycoerythrin. Plasma samples from 150 healthy donors and 39 inhibitor-negative hemophilia A subjects were compared with 4 inhibitor-positive hemophilia A plasma samples with inhibitor titers of 1 BU/mL (94.6 +/- 0.8 nM), 11 BU/mL (214.3 +/- 7.1 nM), 106 BU/mL (2209.4 +/- 84.9 nM), 140 BU/mL (2417.7 +/- 3.8 nM) as measured by the Nijmegen method. We also describe the validation of a mouse anti-human fVIIIAb as a surrogate calibrator. Four healthy individuals (3%) showed detectable anti-fVIIIAb in the range of 0.6 to 6.2 nM, whereas 13 (33%) of the 39 inhibitor-free hemophilia A subjects were positive for anti-fVIIIAb in the range of 0.5 to 20 nM. The method may be useful for therapeutic management of hemophilia A patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".