Emerging Insights Into the Role of Epigenetics and Gut Microbiome in the Pathogenesis of Graves’ Ophthalmopathy
Bibliographic record
Abstract
Graves' Ophthalmopathy (GO) is an organ-specific autoimmune disease that is often characterized by infiltration of orbital tissues and is considered as the most common extra-thyroid manifestation of Graves' disease (GD). Although genetic susceptibility has been found to be critical for the phenotype of GO, the associated risk alleles in a single gene are generally insufficient to cause the disease. Accruing evidence has shown that epigenetic disorders can act as the potentially missing link between genetic risk and clinically significant disease development. Abnormal epigenetic modifications can lead to pro-inflammatory cascades and activation of orbital fibroblasts (OFs) by promoting the various inflammatory response pathways and regulating the diverse signaling molecules that are involved in the fibrogenesis and adipogenesis, thereby leading to the significant expansion of orbital tissues, fibrosis and inflammation infiltration. Additionally, emerging evidence has shown that the gut microbiome can possibly drive the pathogenesis of GO by influencing the secretion of Thyrotropin receptor antibody (TRAb) and T-helper 17 (Th17)/regulatory T cells (Treg) imbalance. This paper describes the latest epigenetic research evidence and progress made in comprehending the mechanisms of GO development, such as DNA methylation, histone modification, non-coding RNAs, and the gut microbiome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".