QUANTIFICATION OF CARDIAC FIBROSIS BY COLOUR‐SUBTRACTIVE COMPUTER‐ASSISTED IMAGE ANALYSIS
Bibliographic record
Abstract
1. Quantification of fibrosis is a key parameter in the assessment of the severity of cardiovascular disease and efficacy of future candidate therapies. Computer-assisted methods are frequently used to assess cardiac fibrosis in several experimental models. A brief survey indicated that there is a clear dearth of literature outlining detailed methodologies for computer-based assessment of cardiac fibrosis. The purpose of the present study was to provide a reliable method for a systematic assessment of cardiac fibrosis. 2. We induced cardiac fibrosis by isoproterenol (ISO) infusion in adult CD1 male mice and quantified fibrosis using a recently developed colour-subtractive computer-assisted image analysis (CS-CAIA) technique. Here, we provided a detailed description of our methodology to facilitate its wider use by other researchers. 3. We showed that the severity of ISO-induced cardiac fibrosis was similar in the apex, mid-ventricular ring and base of the adult CD1 mouse heart. In contrast with other species, such as rats and dogs, we found that uniform expression of beta(1)-adrenoceptors between different regions in CD1 mouse hearts correlated well with uniform induction of cardiac fibrosis. 4. A previous study found a negative correlation between levels of myocardial fibrosis and the degree of cardiac hypertrophy in ISO-treated Wistar rats. In contrast, we found a similar degree of cardiac fibrosis in our ISO-treated CD1 mice. 5. Our results suggest that CD1 mice are an ideal model system to study catecholamine-induced cardiac remodelling, as well as to screen candidate antifibrotic agents for future therapies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".