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Record W2910722063 · doi:10.3390/jpm9010002

Association of TLR4 Polymorphisms, Expression, and Vitamin D with Helicobacter pylori Infection

2019· article· en· W2910722063 on OpenAlexaff
Shafika Assaad, Christy Costanian, Lama Jaffal, Fida Tannous, Maria G. Stathopoulou, Saïd El Shamieh

Bibliographic record

VenueJournal of Personalized Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsUniversity of Ottawa
FundersUniversité Libanaise
KeywordsHelicobacter pylori infectionTLR4MedicineHelicobacter pyloriHelicobacterBiologyImmunologyInternal medicineGastroenterologyInflammation

Abstract

fetched live from OpenAlex

Helicobacter pylori (H. pylori) infection is the strongest recognized risk factor for gastric adenocarcinoma. Since previous observations have shown that polymorphisms in innate immune system genes, as well as vitamin D (VitD) levels, could modify the risk of infection with Helicobacter pylori (H. pylori), we analyzed the relation between single nucleotide polymorphisms (SNPs) in TLRs (TLR1, TLR2, TLR4) CD14, RUNX3 and VitD levels with H. pylori infection. A case-control study on four hundred sixty Lebanese individuals was conducted. Eleven SNPs in total were genotyped and gene expression analysis using real-time PCR was performed in white blood cells of a subsample of eight individuals. A total of 49% of the participants were affected. Although no direct association was found between the SNPs and H. pylori infection, rs4986790G>A and rs4986791T>C in TLR4 were negatively associated with VitD levels (β = −0.371, p = 5 × 10−3 and β = −0.4, p = 2 × 10−3, respectively), which was negatively associated with H. pylori infection (OR = 0.01, p < 1 × 10−3). TLR4 expression was 3× lower in individuals with H. pylori compared with non-infected (p = 0.01). TLR4 polymorphisms, expression, and VitD could be implicated in H. pylori infection and further development of gastric adenocarcinoma.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.158
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 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

Citations25
Published2019
Admission routes1
Has abstractyes

Explore more

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