MétaCan
Menu
Back to cohort

NFκB1 Polymorphisms Are Associated with Severe Influenza A (H1N1) Virus Infection in a Canadian Population

2022· article· en· W4296616425 on OpenAlexafffundabout
Suhrobjon Mullo Mirzo, Anand Kumar, Naresh Kumar Sharma, Lin Li, Robert Balshaw, Francis A. Plummer, Ma Luo, Binhua Liang

Bibliographic record

VenueMicroorganisms · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsGeorge & Fay Yee Centre for Healthcare InnovationUniversity of Manitoba
FundersMax Rady College of Medicine, University of ManitobaUniversity of ManitobaManitoba Medical Service Foundation
KeywordsSingle-nucleotide polymorphismGenotypingSNP genotypingGeneInfluenza A virusGenotypeBiologySNPH1N1 influenzaMedicineVirologyGeneticsVirusImmunologyInternal medicineDiseaseCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: We examined associations between NFκB1 polymorphisms and influenza A (H1N1) clinical outcomes in Canadian. Methods: A total of thirty-six Caucasian patients admitted to the intensive care unit (ICU) in hospitals in Canada were recruited during the 2009 H1N1 pandemic. Genomic DNA was extracted from the whole blood samples. The NFkB1 gene was targeted for genotyping using next-generation sequencing technology—Roche 454. Results: A total of 136 single nucleotide polymorphisms (SNPs) were discovered within the NFκB1 gene. Among them, 63 SNPs were significantly enriched in patients admitted in the ICU (p < 0.05) compared with the British Caucasian population in the 1000 Genomes study. These enriched SNPs are mainly intron variants, and only two are exon SNPs from the non-transcribing portion of the NFκB1 gene. Conclusions: Genetic variations in the NFκB1 gene could influence clinical outcomes of pandemic H1N1 infections. Our findings showed that sequence variations of the NFκB1 gene might influence patient response to influenza infection.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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.

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

Citations1
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueMicroorganismsSame topicinterferon and immune responsesFrench-language works237,207