A sensitive radioimmunoprecipitation assay for assessing the clinical relevance of antibodies to IFN
Bibliographic record
Abstract
BACKGROUND: Some multiple sclerosis (MS) patients treated with interferon beta (IFN beta) develop antibodies to the drug. Neutralising antibody (NAB) assays for IFN beta are expensive and the clinical relevance of the results has been debated. OBJECTIVE: To establish a cheap, sensitive, and reliable assay for antibodies to (125)I-IFN beta, and to correlate levels of antibodies with clinical response to IFN beta treatment. METHODS: We established a radioimmunoprecipitation assay (RIPA) using (125)I-IFN beta. We tested NAB positive sera, healthy control sera, and serial samples of 33 IFN beta-1b treated MS patients from the Vancouver cohort of the Berlex pivotal trial who had a high incidence of NABs. RESULTS: We found that the RIPA was highly sensitive for the detection of antibodies to IFN beta-1a and -1b, and that there was a strong correlation between reactivity of NAB positive sera for (125)I-IFN beta-1b and for (125)I-IFN beta-1a. The RIPA was more sensitive and consistent than the NAB. Moreover, there was a trend towards poorer MRI outcomes in RIPA positive patients, but not in NAB-positive patients. CONCLUSIONS: The RIPA assay is sensitive and easy to perform. It should be of value in assessing the clinical impact of IFN beta antibodies, and its use could help target expensive INF beta treatments to those who will respond best.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".