Multimodality imaging to predict calcific aortic valve disease progression in animal models
Bibliographic record
Abstract
27 Objectives: Calcific aortic valve disease (CAVD) is the most common valvular disease, accounting for 50% of all valve disorders and is the third most common cardiovascular disease following coronary disease and hypertension.[1,2] Currently, there is no pharmacological agent capable of reversing or slowing down the progression of CAVD and treatment of severe cases consists of surgical repair or valve replacement[2]. Hence, there is a crucial need for earlier detection using predictive biomarkers that will allow for preventative intervention as opposed to post-symptomatic disease treatment or management. The primary objective of this study is to assess the feasibility of detecting CAVD biomarkers by various in vivo imaging modalities, such as PET and echocardiography. In addition, this study assesses disease progression in various mouse strains to qualify an appropriate CAVD animal model. Methods: In vivo and ex vivo imaging of C57Bl/6, Gata6+/-, and ApoE-/- (n = 8 per strain cohort) mouse models are used to link unique features of matrix remodelling with CAVD progression. At baseline and longitudinal follow-up (4, 8, and 12 months), in vivo hemodynamic impairment is assessed through echocardiography, and calcification and MMP activity are measured using PET with a series of radiotracers: [18F]NaF, [18F]BR351, and [18F]FMBP. Following imaging, aortic valve (AV) tissue is harvested, sectioned, and analyzed for calcification, inflammatory markers, collagen types, and MMP activity in AV leaflets. Tracer autoradiography, immunofluorescence, and in situ zymography are used to confirm in vivo imaging results with improved resolution and quantification in valves. Histological sample preparation, experimentation, and analyses are then repeated in human AV tissue samples for relative comparison of biomarker expression in animal models. Results: Echocardiography suggests positive signs of disease progression in experimental animal models. In comparison to WT, ApoE-/- mice show; significantly decreased leaflet separation (p 1600 mm/s, double the value measured in WT) indicating stenosis (p<0.0001), increased aortic valve area (p<0.001), and irregular valve dynamics. Gata 6+/- animals show expected bicuspid valve morphology, with confirmation via echocardiography, in expected incidences as documented in the literature (approx. 75%) but do not develop stenosis (peak velocity <1600 mm/s after 12 months). [18F]NaF PET imaging shows expected bone uptake and low calcium-burden in young and WT animals. [18F]FMBP shows increased uptake in the valve area of diseased models at later time points, 1.530 compared to <0.001%ID/g (p<0.05), in disease vs control animals respectively. Furthermore, confirmation of sought-after biomarkers has also been assessed by analysis of various histological sample preparations including the presence of leaflet calcification, upregulation of MMP-2, -9, and -13, matrix remodelling, lipids, inflammatory markers, and activated MMP expression. Conclusions: This translational project will provide a better understanding of extracellular matrix remodeling in valvular heart disease pathophysiology. Findings from this study suggest that molecular imaging techniques using target-specific radiotracers, as well as echocardiography for assessment of hemodynamic impairment, are feasible solutions in predicting disease onset in CAVD specific animal models. Support CIHR Project 366633, uOttawa Translational Research Grants. References: [1] Scatena, M., Jackson, M .F., Speer, M. Y., et al. Cardiovasc Pathol. 2018. 34; 28-37. [2] Nguyen, V., Michel, M., Eltchaninoff, H., et al. J. Am. Coll. Cardiol. 2018. 71(15):1614-27.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".