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Patient-Specific Study of Post-Ischemic Cardiac Ventricular Remodeling: A Passive Simulation of Structural Changes in Myofiber Orientation and Stiffness

2022· article· en· W4323021342 on OpenAlexaff
Saeed Torbati, Alireza Heidari, Alireza Daneshmehr, Hamidreza Pouraliakbar, Seyed Hossein Ahmadi Tafti, Dominique Shum‐Tim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsCardiologyDiastoleInternal medicineCardiomyopathyMedicineVentricular remodelingStiffnessIschemic cardiomyopathyMaterials scienceHeart failureBlood pressureEjection fractionComposite material

Abstract

fetched live from OpenAlex

Despite recent advances in the computational modeling of cardiovascular diseases and therapies, the effect of post-ischemic remodeling has not been thoroughly studied while considering the unloaded ventricles. Further evaluation is, therefore, needed in order to better understand the effect of alterations in myocardial structure. Herein, we have developed a patient-specific computational model of ischemic cardiomyopathy to assess the influence of microstructure and material change on passive ventricular mechanics. The biventricular geometry has been built and unloaded based on cardiac magnetic resonance (CMR) images of a 64-year-old male patient at end-diastole (ED). Different fiber orientations and material scales were assumed for the model. Results indicated that although some fiber structures produce similar end-diastolic pressure-volume relationships (EDPVRs), differences in initial stress-free shapes and strain patterns determine the subsequent damage to the myocardium. Moreover, stiffening the healthy region means lower myofiber strain. However, material change in the ischemic areas of the ventricles does not alter the passive fiber strain considerably. Such evaluations are required when choosing optimal therapies to alleviate the adverse effects of ischemic cardiomyopathy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.217
Teacher spread0.206 · 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 teacher head, 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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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