Natalizumab effects on immune cell responses in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Our objective was to study in vivo biological effects of natalizumab on immune cell phenotype and function in multiple sclerosis (MS) patients. METHODS: Blood was obtained before and after serial monthly natalizumab infusions to track functional expression of VLA-4 and migratory capacity of immune cells. The impact of infusion on activation thresholds of immune cells was evaluated. RESULTS: Preinfusion VLA-4 expression differed across immune cell subsets. Natalizumab significantly, albeit partially, diminished VLA-4 expression on circulating immune cells. Cell subsets were differentially affected. Treatment significantly decreased migratory capacity of immune cells, correlating well with changes in VLA-4 expression. Effects of a single dose were not saturating and did not persist through the monthly dose interval. Infusion effect varied across patients but was remarkably stable in individual patients, over multiple infusions. Treatment significantly modulated proliferative responses of immune cells. INTERPRETATION: To our knowledge, we provide first proof of concept that natalizumab diminishes migratory capacity of immune cells. Our prospective study further shows that effects of therapy likely (1) differ for distinct immune cell subsets, (2) are not sustained over current dose interval, (3) have unique profiles in individual patients, and (4) include modulation of activation threshold of immune cells. Monitoring these parameters could be relevant to ongoing safety and efficacy considerations.
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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".