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Record W3107736266 · doi:10.1093/ehjci/ehaa946.3722

Modified New Zealand rabbit model produces severe aortic valve calcification and stenosis via extracellular membranous particles

2020· article· en· W3107736266 on OpenAlexaffabout
Nikolaos Anousakis‐Vlachochristou, Aimilia Varela, Maria Kyriakidou, Subhathirai Subramaniyan Parimalam, Simona Bǎdilescu, Anna Agapaki, Dimitra Lali, Alkistis Kapelouzou, Ioanna Anastassopoulou, Muthukumaran Packirisamy, Constantinos Anagnostopoulos, Dennis V. Cokkinos, Dimitris Tousoulis, Kostas Toutouzas

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsConcordia University
Fundersnot available
KeywordsMedicineCalcificationStenosisAortic valveTricuspid valveAortic valve stenosisInternal medicineHeart valveCardiologyPathology

Abstract

fetched live from OpenAlex

Abstract Background/Purpose In aortic valve stenosis calcification begins with nucleation on extracellular vesicles. In order to study early-stage disease, validated animal models are needed. The Drolet rabbit model is relevant due to tricuspid valve, but failed to consistently produce stenosis probably due to regimen administration. We compared a modified rabbit model and investigated the mechanisms and patterns of calcification. Methods New Zealand rabbits introduced to normal chaw+1% cholesterol+8750 IUs Vitamin D2/kg (Sigma) daily, in olive oil given in a bisquit vs control animals, for 8 weeks. Aortic valve area (AVA) and mean gradient (meanGr) was assessed with echocardiography (Vivid 7, M3S transducer, GE). At 8 weeks animals were sacrificed and valves were snap-frozen to −80°C. From each animal, one cusp was analyzed with Fourier-Transformed Infrared Spectroscopy (FT-IR, Nicolet 6700 spectrometer, OMNIC 7.3 software), another cusp was processed in alcoholic solution and the third was fixed 0.5 μm thin on 4% PFA; supernatant and tissue respectively examined with multispectral optical imaging. Valves from patients with severe stenosis were used for qualitative comparisons. Results At 8 weeks versus baseline, AVA reduced (0.5 cm2 to 0.3 cm2) and meanGr increased (1.1 to 2.95 mmHg, p<0.05), in control was unchanged. FT-IR vibrations in the region of 1800–800 cm–1 demonstrated changes in the protein structure and deposition of CaCO3 and non-hydroxyapatite Ca3(PO4)2 identical to patients' lesions. Multispectral optical imaging of supernatants revealed numerous membranous particles and conductivity analysis indicated calcium cations accumulation on the phospholipids of membrane. The tissue images confirmed the degradations and dendrimer-like depositions of calcium cations most likely on carbonates of amino acids. Conclusions The modified high-fat-vitamin D2 rabbit model produces aortic valve stenosis, with chemically identical mineralization to human lesion. Multispectral photonics demonstrate the presence of calcified membranous extracellular particles, a hallmark of cardiovascular calcification. Dendrimer-like depositions correspond to growing deposits. The model is suitable as a research platform purposed for aortic valve stenosis. Figure 1. A: Image from alcoholic solution supernatant. The bright spots have high conductivity due to Ca 2+ deposition. B: ImageJ surface plot of circulated region confirms calcification. C: 3D-plot illustrates mineralization of membranes. D: 3D-plot of human aortic valve. E: Hypermicroscopic image of rabbit valve tissue: dendrimer-like and mineral cation deposits. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): National and Kapodistrian University of Athens, Greece; Concordia University, Montreal, Canada

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.058
GPT teacher head0.313
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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