Multimodality imaging to predict calcific aortic valve disease progression in animal models
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».