Potential for Interferon Beta–Induced Serum Antibodies in Multiple Sclerosis to Inhibit Endogenous Interferon-Regulated Chemokine/Cytokine Responses Within the Central Nervous System
Bibliographic record
Abstract
BACKGROUND: A proportion of patients with multiple sclerosis (MS) receiving systemic interferon beta therapy will develop serum neutralizing antibodies (NAbs) that can reduce the activity of the drug. Interferon-beta (IFN-beta) is produced by glial cells within the central nervous system. Although systemic interferon beta does not access the central nervous system, titers of serum NAbs may be sufficient that some will access the central nervous system. OBJECTIVE: To address whether serum samples that contain high titers of NAbs could inhibit glial cell production of chemokines and cytokines that are regulated by endogenous IFN-beta. DESIGN: We used an in vitro assay involving toll-like receptor 3 ligand (polyinosinic-polycytidylic acid) signaling to assess the effect of serum samples containing high titers of NAbs (1800-20 000 U) on production of the chemokine CXCL10 and the cytokine interleukin 6 by human astrocytes. RESULTS: Serum samples positive for NAbs significantly inhibited polyinosinic-polycytidylic acid-induced CXCL10 and IL-6 production by astrocytes. CONCLUSION: High-titer NAbs to interferon beta may block endogenous IFN-beta function and alter the chemokine/cytokine microenvironment within the central nervous system, thereby modulating the profile and course of the local inflammatory 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".