A Biosensor-Based Characterization of the Affinity Maturation of the Immune Response Against Interferon-β and Correlations with Neutralizing Antibodies in Treated Multiple Sclerosis Patients
Bibliographic record
Abstract
The study of antibodies against Interferon-beta (IFN-beta) in treated multiple sclerosis (MS) patients has focused primarily on detection and quantification, with no single method of detection providing a comprehensive characterization. We assessed serial samples of 18 MS patients, treated with IFN-beta for between 66 and 198 months, to characterize the affinity maturation of these antibodies using a biosensor-based approach (Biacore). Biacore utilizes the principles of surface plasmon resonance (SPR) with data from the dissociation phase of the antigen-antibody reaction being inversely proportional to relative antibody affinity. In patients with neutralizing antibodies (NAbs+), mean antibody dissociation rates decreased from 0.00118 +/- 0.00030 s(-1) at month 6 to 0.00021 +/- 0.00008 s(-1) at month 36, followed by a slight increase to 0.00027 +/- 0.00003 s(-1) at month 60. In NAb- patients, mean antibody dissociation rates decreased only very slightly from 0.00130 +/- 0.00025 s(-1) to 0.00105 +/- 0.00020 s(-1) at month 18, followed by a gradual increase to 0.00243 +/- 0.00099 s(-1) at month 60. Our study shows little improvement in antibody affinity in NAb- patients, in contrast to a marked increase in antibody affinity over time in NAb+ patients with a significant correlation between NAb titers and relative antibody dissociation rates (Spearman's correlation, R(2) = -0.374, p < 0.001). The evaluation of relative antibody affinities will contribute to a thorough understanding of the anti-IFN-beta antibody response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".