Association of circulating microRNA-122 and microRNA-29a with stage of fibrosis and progression of chronic hepatitis in Labrador Retrievers
Bibliographic record
Abstract
Abstract Background Chronic hepatitis (CH) in dogs is common and has the tendency to progress to liver cirrhosis (LC). Circulating microRNAs might have the potential as markers for disease progression. Objectives To investigate whether concentration of specific microRNAs in serum correlate with the stage and grade of CH in Labrador Retrievers. Animals Twenty-two Labrador Retrievers with histological CH (n = 8), LC (n = 7), and normal liver (NL, n = 7). Methods In this retrospective study, serum concentrations of miR-122, miR-29a, miR-133a, miR-181b, and miR-17-5p were measured by quantitative real-time PCR and evaluated using univariate linear regression in dogs. A multivariate model was fit including the grade of hepatitis and the stage of fibrosis. Results Of the 5 microRNAs, only circulating miR-122 and miR-29a were significantly associated with the grade of hepatitis and the stage of fibrosis. A positive correlation was identified between the grade of hepatitis with miR-122 (rs = 0.79, P < .001) and miR-29a (rs = 0.78, P < .001). Both miR-122 (rs = 0.81, P < .001) and miR-29a (rs = 0.67, P < .001) showed a significant positive correlation with the stage of fibrosis. MiR-122 concentrations were significantly higher in the CH (P < .01) and LC groups (P < .001) compared to the NL group. MiR-29a concentrations were significantly higher in the CH (P < .001) and LC (P < .001) groups compared to the NL group. Conclusions and Clinical Importance Circulating miR-122 and miR-29a concentrations might be useful for monitoring the response to treatment and progression of canine CH.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".