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Record W4220934739 · doi:10.3389/fimmu.2022.859400

Epigenetic Regulation in the Pathogenesis of Rheumatoid Arthritis

2022· review· en· W4220934739 on OpenAlexaff
Chao Yang, Dan Li, Dehong Teng, Yueru Zhou, Lei Zhang, Zhangfeng Zhong, Guan‐Jun Yang

Bibliographic record

VenueFrontiers in Immunology · 2022
Typereview
Languageen
FieldChemistry
TopicQuinazolinone synthesis and applications
Canadian institutionsUniversity of Waterloo
FundersJiangxi University of Traditional Chinese MedicineChengdu University of Traditional Chinese MedicineChengdu UniversityUniversidade de MacauFundo para o Desenvolvimento das Ciências e da TecnologiaChina Postdoctoral Science FoundationNingbo UniversityNational Natural Science Foundation of China
KeywordsEpigeneticsPathogenesisDNA methylationRheumatoid arthritisHistoneMedicineMechanism (biology)ImmunologyEpigenesisBioinformaticsDiseaseBiologyGeneticsGeneGene expressionPathology

Abstract

fetched live from OpenAlex

Rheumatoid arthritis (RA) is an autoimmune disease. The etiology of RA remains undetermined and the pathogenesis is complex. There remains a paucity of ideal therapeutic drugs and treatment strategies. The epigenetic modifications affect and regulate the function and characteristics of genes through mechanisms, including DNA methylation, histone modification, chromosome remodeling, and RNAi, thereby exerting a significant impact on the living state of the body. Recently, the phenomenon of epigenetic modification in RA has garnered growing research interest. The application of epigenetically modified methods is the frontier field in the research of RA pathogenesis. This review highlights the research on the pathogenesis of RA based on epigenetic modification in the recent five years, thereby suggesting new methods and strategies for the diagnosis and treatment of RA.

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)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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

Citations56
Published2022
Admission routes1
Has abstractyes

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