The measurement of antibodies binding to IFNβ in MS patients treated with IFNβ
Bibliographic record
Abstract
The intensity of the antibody response to injected interferons (IFNs) depends on many factors: route of injection, dose injected, frequency of injections, and duration of treatment. The optimal assay or approach for testing for anti-IFN antibodies has not been determined. Neutralizing antibody (NAb) determinations are most commonly used but have intrinsic problems when used as clinical assays. They are time consuming, expensive, and only indirectly measure antibodies. Thus, false-positive results can occur because of other serum factors. In contrast, binding assays measure antibodies directly; they are mainly used as an initial screen to detect NAbs. ELISA methodologies for binding antibodies have been used, but direct adhesion of the antigen (i.e., IFNβ) to the plate has resulted in false-negative and false-positive results, presumably because of changes in antigenicity.1 This limitation is circumvented by using the capture ELISA in which a first antibody is used to capture the antibody and hold it in an antigenic position, mimicking that of IFNβ in its natural state. Radioimmunoprecipitation assays (RIPAs) are used routinely to measure pathogenic autoantibodies, including antibodies to the acetylcholine receptor and voltage-gated calcium and potassium channels, with good sensitivity and specificity. The RIPA also has been used to measure antibodies to another biologic substance, botulinum toxin, in patients treated with this toxin. The experience of groups in the United States (University of Medicine and Dentistry of New Jersey) and Canada (University of British Columbia) with improved ELISAs and in the United Kingdom (Oxford University) with RIPA is summarized below. ### Direct ELISA. The assay has been previously described.2,3⇓ In brief, IFNβ was directly coated onto an ELISA plate, followed by serial incubations with serum, conjugate, and substrate, with washes in between. ### Capture ELISA. #### United States. The assay has been previously described.1 Experience with this assay in a broad spectrum of patients with MS has recently …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".