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Infrared study and computational simulations of coronary arteries atherosclerotic lesions for early diagnosis of disease progression

2021· article· en· W3205925872 on OpenAlexaff
Ioannis Mamarelis, Christina Mamareli, E Mylonas, V. Mamareli, K Spilopoulos, Sotirios Kotoulas, P. Kolovou, Prakash Subramaniyan, P. Muthukumaran, Simona Bǎdilescu, Maria Koui, T. Théophanides, Christophoros Kotoulas, J. Anastassopoulou

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsConcordia University
Fundersnot available
KeywordsMedicineCoronary arteriesArteryEx vivoGlycationCoronary artery diseaseStenosisIntravascular ultrasoundCardiologyBiomedical engineeringPathologyInternal medicineIn vivo

Abstract

fetched live from OpenAlex

Abstract Introduction Coronary artery atherosclerotic disease is the most common cardiovascular disease and bypass grafting surgery (CABG) is an effective treatment. However, the pathogenic mechanism of coronary arteries atherosclerosis and disease progression is not yet clear. Purpose The use of FT-IR spectroscopy, hyperspectral microscope and mathematical simulation models are some of the tools to predict the morphological and elasticity disorders in the vessel wall due to molecular structure changes. Methods Biopsies of atherosclerotic native coronary arteries from 54 patients (44–85 years), who underwent coronary endarterectomy during bypass grafting surgery (CABG), were examined ex vivo. The FT-IR-spectra were recorded with a Nicolet-6700 spectrometer. Morphological changes of atheromatic plaques were performed with SEM-EDX, Fei-Co. CytoVita-Olympus hyperspectral microscope was used to obtain the cells. Results FT-IR spectroscopy (Figure 1A) showed that the disease affects the protein folding, leading to amyloid formation (beta-sheets), lipid peroxidation and AGEs (Advanced Glycation end products) production. The detection of amorphous CaCO3 (1415 and 872 cm–1) deposits in high lipophilic regions was of high importance. Increased mineral concentration leads to increased formation of crystalline deposits, consisting of CaCO3, CaHPO4, Ca3(PO4)2 and inorganic hydroxyapatite, resulting in arterial stenosis. Hyperspectral images confirmed the formation of micelles (1) due to amyloidosis and calcified cells (2), in agreement with FT-IR, ImageJ analysis data. Mathematical simulation model based on finite element method (Figure 1E) showed that arterial wall damage and elasticity changes were not homogenous. This model provides the time of crystallization of the calcium salts, which play crucial role to stenosis. Conclusions FT-IR spectra showed that the formation of amorphous CaCO3 in the presence of Mg2+, in reach of oxidized lipids regions, inhibit the development of coronary artery stenosis. Excessive of Ca2+ efflux promotes the crystallinity of CaCO3 and Ca3(PO4)2 deposits, leading to the development of atherosclerotic plaques and coronary artery stenosis. Mathematical models approach in a much better way the progression of arterial atherosclerosis. Funding Acknowledgement Type of funding sources: None. Figure 1

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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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.046
GPT teacher head0.322
Teacher spread0.276 · 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 designSimulation or modeling
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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