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Record W2057903396 · doi:10.1002/ana.20859

Natalizumab effects on immune cell responses in multiple sclerosis

2006· article· en· W2057903396 on OpenAlexaff
Masaaki Niino, Caroline Bodner, Marie‐Lune Simard, Sudabeh Alatab, Dawn Gano, Ho Jin Kim, Manuela Trigueiro, Denise Racicot, Christine Guérette, Jack P. Antel, Alyson E. Fournier, François Grand’Maison, Amit Bar‐Or

Bibliographic record

VenueAnnals of Neurology · 2006
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHôpital Charles-Le MoyneMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsNatalizumabImmune systemMultiple sclerosisImmunologyCellMedicineBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.116
GPT teacher head0.337
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations210
Published2006
Admission routes1
Has abstractyes

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