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Record W2329125823 · doi:10.1017/s0317167100015973

IL2RA Allele Increases Risk of Neuromyelitis Optica in Southern Han Chinese

2013· article· en· W2329125823 on OpenAlexvenueno aff
Yongqiang Dai, Jin Li, Xiaonan Zhong, Yuge Wang, Wei Qiu, Zhengqi Lu, Aimin Wu, Jian Bao, Fuhua Peng, Xueqiang Hu

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2013
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSingle-nucleotide polymorphismNeuromyelitis opticaAlleleSNPHan chineseMultiple sclerosisGeneticsGeneBiologyAllele frequencyMedicineGenotypeImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Neuromyelitis optica (NMO) and multiple sclerosis (MS) are chronic neuro-inflammatory diseases believed to arise from complex interactions between environmental and genetic factors. Recently, single nucleotide polymorphisms (SNPs) in interleukin (IL)-2 and -7 receptor alpha genes have been identified as novel susceptibility alleles for MS in genome-wide association studies. However, similar research on NMO is limited. We aimed to investigate the association of IL2RA SNPs rs2104286 and rs12722489 and IL7RA SNP rs6897932 with Southern Han Chinese NMO and MS patients. METHODS: Frequencies of the three SNPs were examined in Southern Han Chinese mS cases (n=78), NMS cases (n=67) and controls (n=133) using sequencing-based typing. RESULTS: The rs2104286(G) frequency in the IL2RA gene was significantly higher in NMO patients than in controls (p(uncorr)=0.013, p(corr)=0.026, OR:1.942, 95%CI:1.146-3.291). CONCLUSION: The rs2104286 G allele in IL2RA is present at higher frequencies in NMO patients than in healthy controls within a Southern Han Chinese population. Les allèles IL2RA augmentent le risque de neuromyélite optique chez les Chinois Han du sud.

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.289
Teacher spread0.253 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicMultiple Sclerosis Research Studies→French-language works237,207→