MicroRNA profiling of the feline left heart identifies chamber-specific expression signatures in health and in advanced hypertrophic cardiomyopathy
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM) is a common heart disease in humans and cats, nonetheless, the disease pathogenesis is still poorly understood. MicroRNAs are suspected to be involved in the disease process but the myocardial microRNA expression pattern in cats has not been identified. We hypothesized that microRNA profiles differ between healthy cats and cats with HCM. Small RNA sequencing on left ventricle (LV) and left atria (LA) samples from healthy cats (8 LV, 8 LA) and cats with HCM (7 LV, 5 LA) was performed. We identified 1039 differentially expressed microRNAs (False Discovery Rate <0.01, fold change >2). Cats with HCM were found to have a distinct microRNA expression profile with apparent regional heterogeneity. Comparing the HCM and control hearts, we detected 80 differentially expressed microRNAs for the HCM LV, and 37 for the LA. These included LV and LA enriched miR-21, miR-146b, and reduced miR-122-5p, which were recently suggested as key microRNAs for the HCM pathogenesis, and miR-132, which might be of therapeutic interest. Several top enriched microRNAs: miR-3958, miR-382-5p, miR-487a-5p (HCM LV); miR-chrD4_30107-3p (HCM LA); miR-3548 (HCM LV and LA) have either not been reported in the heart or only little is known. We identified potentially relevant microRNAs and further investigations into their role are required. Genes known to be targeted by the differentially expressed microRNAs were associated with inflammation and growth pathways in the HCM LV and LA, cardioprotective pathways in the LV, and fibrosis and structural changes in the LA when compared to healthy hearts.
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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 | 0.000 |
| 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".