Evaluation of potential novel biomarkers for feline hypertrophic cardiomyopathy
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM) is the most common cardiomyopathy in cats. The diagnosis can be difficult, requiring advanced echocardiographic skills. Additionally, circulating biomarkers (N-terminal pro-B type natriuretic peptide and cardiac troponin I) have several limitations when used for HCM screening. In previous work, we identified interleukin 18 (IL-18), insulin-like growth factor binding protein 2 (IGFBP-2), brain-type glycogen phosphorylase B (PYGB), and WNT Family Member 5 A (WNT5A) as myocardial genes that show significant differential expression between cats with HCM and healthy cats. The products of these genes are released into the circulation, and we hypothesized that IL-18, IGFBP-2, PYGB, and WNT5A serum RNA and protein concentrations differ between healthy cats, cats with subclinical HCM, and those with HCM and congestive heart failure (HCM + CHF). Reverse transcriptase quantitative polymerase chain reaction (RTqPCR) and enzyme-linked immunosorbent assay (ELISA) were applied to evaluate gene and protein expression, respectively, in the serum of eight healthy controls, eight cats with subclinical HCM, and six cats with HCM + CHF. Serum IGFBP-2 RNA concentrations were significantly different among groups and were highest in cats with subclinical HCM. Compared to healthy controls, serum IL-18 and WNT5A gene expression were significantly higher in cats with HCM + CHF, and WNT5A was higher in cats with subclinical HCM. No differences were observed for PYGB. These results indicate that further investigation via large scale clinical studies for IGFBP-2, WNT5A, and IL-18 may be valuable in diagnosing and staging feline HCM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".