Cutaneous microRNA expression in healthy Labrador and Golden retrievers and retrievers with allergic and inflammatory skin diseases
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNA) are short, single-stranded RNA molecules that regulate gene expression in a post-transcriptional manner. Their expression is proposed to be tissue-specific and alterations in miRNA expression have been detected in many diseases. OBJECTIVE: To compare miRNA expression in the skin of healthy Labrador and golden retrievers, and those with allergic and nonallergic dermatitis. METHODS AND MATERIALS: Formalin-fixed and paraffin-embedded (FFPE) skin specimens from seven healthy Labrador and golden retrievers, and seven dogs with allergic skin disease were collected. A further mixed nonallergic inflammation group consisted of samples from five dogs with fungal infection, demodicosis and mast cell tumours. Total RNA was extracted and miRNA primer assays for 18 target miRNAs (miR-142, miR-363, miR-18b, miR-451, miR-146a, miR-124, miR-409, miR-193b, miR-223, miR-215, miR-155, miR-423a, miR-143, miR-1839, miR-21, miR-34b, miR-146b and miR-202) were performed, with RNU6-2 and SNORD95 as miRNAs for normalisation. The selection of miRNAs for investigation was based on reported data and a pilot study evaluating miRNA extraction from FFPE tissue specimens. RESULTS: In the two dogs with mast cell tumours, miRNA expression was undetermined for most miRNAs, so both were excluded from analysis. Although there were differences in the miRNA expression between healthy and inflamed skin, allergic and nonallergic inflammation showed similar expression patterns. CONCLUSION AND CLINICAL RELEVANCE: Although the number of included dogs was small, based on this study, none of the evaluated miRNAs allowed differentiation of allergic dermatitis from other inflammatory skin diseases in retriever dogs.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".