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Record W2065507002 · doi:10.1159/000070855

Decreased Frequency of the Tumor Necrosis Factor α –308 Allele in Serbian Patients with Multiple Sclerosis

2003· article· en· W2065507002 on OpenAlexaff
Jelena Drulović, Dušan Popadić, Šarlota Mesaroš, Irena Dujmović, Ivana Cvetković, Djordje Miljković, Nebojša Stojsavljević, Vera Pravica, Tatjana Pekmezović, Gradimir Bogdanović, M Jarebinski, Marija Mostarica Stojković

Bibliographic record

VenueEuropean Neurology · 2003
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsMultiple sclerosisAlleleAllele frequencyMedicineSerbianTumor necrosis factor alphaImmunologyPathologyBiologyGeneticsGenePhilosophy

Abstract

fetched live from OpenAlex

Tumor necrosis factor (TNF) alpha has been considered the prototypic cytopathogenic cytokine in multiple sclerosis (MS), but recently this cytokine has been shown to possess significant anti-inflammatory and neuroprotective effects in demyelinating diseases. It has been reported that the TNFalpha -308 polymorphism influences levels of TNFalpha production, and that the rare allele, TNF2, is associated with high TNFalpha production. We investigated the TNFalpha -308 polymorphism in 143 unrelated Serbian patients with MS and 123 ethnically matched, healthy individuals using the allele-specific restriction fragment length polymorphism polymerase chain reaction technique. The frequency of the TNF2 allele was significantly decreased in MS patients (14%) in comparison with controls (24%; p = 0.044). The TNF2 allele had no influence on disease behavior, since it was not associated with the course and severity of MS in this group of patients. The result suggests that in the Serbian population polymorphism at position -308 of TNFalpha or at an adjacent locus might have a role in MS susceptibility.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.239
Teacher spread0.202 · 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

Citations28
Published2003
Admission routes1
Has abstractyes

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