Interferon Beta Promotes Nerve Growth Factor Secretion Early in the Course of Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Interferon beta therapy has been shown to reduce the rate of clinical relapse and the frequency of magnetic resonance imaging-defined T2- weighted lesions in patients with multiple sclerosis (MS). When given early, interferon beta also reduces the rate of development of brain atrophy and improves axonal integrity. Nerve growth factor (NGF) can retard the severity and course of experimental allergic encephalomyelitis. OBJECTIVE: To determine whether interferon beta effects on patients with MS could be related to modulation of neurotrophin production within the central nervous system. DESIGN: We studied neurotrophin production by human glial and brain endothelial cells in response to coculture with MS patient-derived lymphocytes, and correlated levels of NGF secretion with clinical and magnetic resonance imaging-defined markers of disease. RESULTS: We demonstrate that production of NGF by human brain microvascular endothelial cells is triggered by interaction with T lymphocytes derived from MS patients. No such response was observed using human adult microglia or human fetal astrocytes. Nerve growth factor production by endothelial cells was potentiated by pretreating lymphocytes with interferon beta in vitro, and by using lymphocytes derived from MS patients treated with interferon beta in vivo. By using this assay, we show that levels of NGF induced by lymphocytes from MS patients inversely correlate with magnetic resonance imaging measures of brain atrophy and axonal injury. CONCLUSION: These findings suggest that interferon beta-mediated production of NGF at the level of the blood-brain barrier, whether acting as an immunomodulator or directly on neural cells, is another potential mechanism contributing to the magnetic resonance imaging-defined effect of interferon beta on brain atrophy when given early in the course of MS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".