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Record W2981183376 · doi:10.15171/mejdd.2019.149

Vitamin D Increases CTLA-4 Gene Expression in Patients with Mild to Moderate Ulcerative Colitis

2019· article· en· W2981183376 on OpenAlexaff
Amrollah Sharifi, Homayoon Vahedi, Mohammad Reza Honarvar, Behnam Alipoor, Zeinab Nikniaz, Hossein Rafiei, Mohammad Javad Hosseinzadeh‐Attar

Bibliographic record

VenueMiddle East Journal of Digestive Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersGolestan University of Medical Sciences
KeywordsUlcerative colitisCTLA-4MedicineVitaminCytotoxic T cellInternal medicineInflammatory bowel diseaseVitamin D and neurologyAntibodyGene expressionImmunologyGastroenterologyT cellEndocrinologyImmune systemGeneChemistryIn vitro

Abstract

fetched live from OpenAlex

BACKGROUND Ulcerative colitis (UC) is a chronic inflammatory disorder of the large intestine. Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is a member of the immunoglobulin superfamily, which binds B7-1 and B7-2 on APCs (antigen-presenting cells), and induces APCs to produce an inhibitory signal to T cells. The aim of this study was to investigate the effect of vitamin D on CTLA-4 gene expression in whole blood samples of patients with UC. METHODS 90 patients with mild to moderate UC were randomized to receive either a single injection of 7.5 mg vitamin D3 or 1 mL normal saline. 90 days following the intervention fold changes in CTLA-4 mRNA expression were determined and statistical comparisons between the two groups were performed. RESULTS Serum vitamin D increased significantly only in the vitamin D group. CTLA-4 fold changes were significantly higher in the vitamin D group compared with the placebo group (median ± IQR: 1.21 ± 2.3 vs. 1.00 ± 1.5, respectively; p = 0.007). CONCLUSION The results of this study revealed that vitamin D administration in patients with UC enhances the CTLA-4 gene expression.

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)0.001
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 score0.003
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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