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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".