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Record W3202573798 · doi:10.3389/fcell.2021.719235

Critical Role of Gut Microbiota and Epigenetic Factors in the Pathogenesis of Behçet’s Disease

2021· review· en· W3202573798 on OpenAlexfundno aff
Xiaomin Ma, Xin Wang, Guangbing Zheng, Guiqin Tan, Fangyu Zhou, Wenwen Wei, Dan Tian, Hongsong Yu

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2021
Typereview
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaPetroleum Technology Research CentreGuizhou Science and Technology DepartmentDepartment of Education of Guizhou ProvinceInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsPathogenesisEpigeneticsBehcet's diseaseDiseaseImmunologyVasculitisMedicineBiologyGeneticsPathologyGene

Abstract

fetched live from OpenAlex

Behçet's disease (BD) is a chronic refractory multisystem autoinflammatory disease, characterized by typical clinical features of non-specific vasculitis, oral and genital ulcers, uveitis, as well as skin lesions. The exact etiopathogenesis of BD remains unknown, existing studies have indicated that genetics and environmental factors contribute to the increased development of BD. Recently, several studies have shown that external environmental factors can affect the process of epigenetic modification, and abnormalities of epigenetic factors have been confirmed to be involved in the occurrence of BD. At the same time, abnormalities of gut microbiota (GM) in the body, have also been confirmed to participate in the pathogenesis of BD by regulating the balance of Th17/Tregs. This article reviews the pathogenesis of BD and summarizes numerous clinical studies, focusing on the mechanism of GM and epigenetic factors impacting on BD, and providing new ideas for further elucidating the pathogenesis of BD.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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 designNot applicable
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

Citations19
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Cell and Developmental BiologySame topicOcular Diseases and Behçet’s SyndromeFrench-language works237,207