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Record W1887786573

Neutralising antibodies to interferon beta in multiple sclerosis

2010· dissertation· en· W1887786573 on OpenAlexaboutno aff
Rachel Farrell

Bibliographic record

VenueUCL Discovery (University College London) · 2010
Typedissertation
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsnot available
Fundersnot available
KeywordsAntibodyMedicineMultiple sclerosisIn vivoImmunologyBiomarkerTiterBiologyGenetics
DOInot available

Abstract

fetched live from OpenAlex

Multiple Sclerosis is the most common non-traumatic cause of neurological disability in \nyoung people. Until recently few treatments existed for Multiple Sclerosis and Interferon \nbeta was the first and remains the most commonly prescribed. As a biological product it \ninduces antibodies to the protein which may abrogate the efficacy of the drug (neutralising \nantibodies - NAbs). Testing for these antibodies has been problematic as biological assays \nare difficult to standardise, time consuming and expensive. In the experiments described \nhere we sought to develop a novel cell-based reporter-gene assay to reliably test for Nabs, \nto explore the relationship between NAbs, treatment efficacy, biological activity and \ncorrelate NAb titres with in vivo biomarker induction to establish guidelines to interpret \nresults. The work presented in this thesis describes the development and validation of the \nluciferase assay and has shown that subjects who develop NAbs experienced increased \nrelapse rates, (which lag behind the appearance of NAbs). Evaluating the in vivo biological \nresponse to IFNβ injection it was established that in subjects with NAbs there was titre \ndependent loss of bioactivity with reduced myxovirus resistance protein A (MxA) level in \nthose with titres 100 – 600 NU and absent response in those with titres > 600 NU. Opinion \namongst neurologists (UK, USA, Canada and Austria) regarding NAbs was evaluated and \nrevealed uncertainty of their significance in the clinical setting, and the reluctance of some \nneurologists to incorporate NAb testing into routine practice. Results were similar in the \ncountries surveyed in that 90 – 100% were aware of NAbs and thought they abrogate \nclinical efficacy, yet few routinely tested for them, particularly in the UK. The validated \nluciferase assay has since been dissemintated to 9 countries. Since it’s launch in the UK in \n2006 over 4,000 patients have been tested for NAbs. This work directly translates into \nclinical practice and provides a useful service to aid management of subjects with MS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.268
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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