Dysbiosis of oral microbiota and its association with salivary immunological biomarkers in autoimmune liver disease
Why this work is in the frame
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Bibliographic record
Abstract
The gut microbiota has recently been recognized to play a role in the pathogenesis of autoimmune liver disease (AILD), mainly primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH). This study aimed to analyze and compare the composition of the oral microbiota of 56 patients with AILD and 15 healthy controls (HCs) and to evaluate its association with salivary immunological biomarkers and gut microbiota. The subjects included 39 patients with PBC and 17 patients with AIH diagnosed at our hospital. The control population comprised 15 matched HCs. Salivary and fecal samples were collected for analysis of the microbiome by terminal restriction fragment length polymorphism of 16S rDNA. Correlations between immunological biomarkers measured by Bio-Plex assay (Bio-Rad) and the oral microbiomes of patients with PBC and AIH were assessed. Patients with AIH showed a significant increase in Veillonella with a concurrent decrease in Streptococcus in the oral microbiota compared with the HCs. Patients with PBC showed significant increases in Eubacterium and Veillonella and a significant decrease in Fusobacterium in the oral microbiota compared with the HCs. Immunological biomarker analysis showed elevated levels of inflammatory cytokines (IL-1β, IFN-γ, TNF-α, IL-8) and immunoglobulin A in the saliva of patients with AILD. The relative abundance of Veillonella was positively correlated with the levels of IL-1β, IL-8 and immunoglobulin A in saliva and the relative abundance of Lactobacillales in feces. Dysbiosis of the oral microbiota is associated with inflammatory responses and reflects changes in the gut microbiota of patients with AILD. Dysbiosis may play an important role in the pathogenesis of AILD.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it