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Record W2021286238 · doi:10.4021/jocmr1505w

The Role of Vitamin D in Autoimmune Hepatitis

2013· review· en· W2021286238 on OpenAlexvenueno aff
Khanh vinh quô′c Lương

Bibliographic record

VenueJournal of Clinical Medicine Research · 2013
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAutoimmune hepatitisMedicineCalcitriol receptorVitamin D and neurologyEndocrinologyCalcitriolImmunologyHepatitisInternal medicinevitamin D deficiencyCytotoxic T cellBiologyBiochemistry

Abstract

fetched live from OpenAlex

Autoimmune hepatitis is an inflammation of the liver characterized by the presence of peri-portal hepatitis, hypergammaglobulinemia, and the serum autoantibodies. The disease is classified into 2 distinct types according to the nature of auto-antibodies. Disturbances of the calcium-parathyroid hormone-vitamin D axis are frequently associated with chronic liver disease. Patients with AIH have a high prevalence of vitamin D deficiency. Genetic studies have provided the opportunity to determine which proteins link vitamin D to AIH pathology, namely, the major histocompatibility complex class II molecules, vitamin D receptors, toll-like receptors, cytotoxic T lymphocyte antigen-4, cytochrome P450 CYP2D6, regulatory T cells (Tregs) and the forkhead/winged helix transcription factor 3. Vitamin D also exerts its effect on AIH through non-genomic factors, namely, mitogen-activated protein kinase signaling pathways, γδT cells, interferon-gamma nitric oxide synthase, and reactive oxygen stress. In conclusion, vitamin D may have a beneficial role in AIH and improves liver function in concanavalin A-induced mouse AIH. Calcitriol is best used for AIH because it is the active form of a vitamin D3 metabolite and its receptors are present in sinusoidal endothelial cells, Kupffer cells, stellate cells of normal livers, and the biliary cell line.

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)0.045
metaresearch head score (Gemma)0.096
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.096
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0020.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.009
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 head0.310
GPT teacher head0.600
Teacher spread0.290 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations37
Published2013
Admission routes1
Has abstractyes

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