Use of Serum MicroRNAs as Biomarker for Hepatobiliary Diseases in Dogs
Bibliographic record
Abstract
BACKGROUND: Current biochemical indicators cannot discriminate between parenchymal, biliary, vascular, and neoplastic hepatobiliary diseases. MicroRNAs are promising new biomarkers for hepatobiliary disease in humans and dogs. OBJECTIVE: To measure serum concentrations of an established group of microRNAs in dogs and to investigate their concentrations in various types of hepatobiliary diseases. ANIMALS: Forty-six client-owned dogs with an established diagnosis of hepatobiliary disease and stored serum samples and eleven client-owned healthy control Labrador Retrievers. METHODS: Retrospective study. Medical records of dogs with parenchymal, biliary, vascular, or neoplastic hepatobiliary diseases and control dogs were reviewed. Concentrations of miR-21, miR-122, miR-126, miR-148a, miR-200c, and miR-222 were quantified in serum by real-time polymerase chain reaction. RESULTS: No different microRNA concentrations were found in the adenoma and congenital portosystemic shunt groups. In all other diseases, miR-122 concentrations were elevated with the highest concentration in the mucocele group (267-fold, CI: 40-1,768, P < .001). In dogs with biliary diseases, miR-21 and miR-222 were only increased in dogs with mucoceles (26-fold, CI: 5-141, P = .005 and 13-fold, CI: 2-70, P = .025, respectively). Uniquely increased microRNAs were found in the hepatocellular carcinoma group (miR-200c, 35-fold increase, CI: 3-382, P = .035) and the chronic hepatitis group (miR-126, 22-fold increase, CI: 5-91, P = .002). CONCLUSIONS AND CLINICAL IMPORTANCE: A microRNA panel consisting of miR-21, miR-122, miR-126, miR-200c, and miR-222 can distinguish between parenchymal, biliary, and neoplastic hepatobiliary diseases. Serum microRNA profiling is a promising new tool that might be a valuable addition to conventional diagnostics to help diagnose various hepatobiliary diseases in dogs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".