A Novel Finite Element Model on Atherosclerotic Plaques: Effect of Heart Rate
Bibliographic record
Abstract
Atherosclerotic plaques are highly heterogeneous, nonlinear materials with uncharacteristic structural behaviors.It is well known that mechanics of atherosclerotic plaques significantly depend on plaque geometry, location, composition, and loading conditions.Computational studies have shown great potential to characterize this mechanical behavior.Different types of plaque morphologies and mechanical properties have been used in a computational platform to estimate the stability of rupture-prone plaques and detect their locations.In this study, we hypothesize that heart rate (HR) is also one of the major factors that should be taken into account while mechanics of plaques is studied.We propose a tunable viscoelastic constitutive material model for the fibrous cap tissue in order to calculate the peak cap stress (PCS) in normal physiological (dynamic) conditions while HR changes from 60 bpm to 150 bpm in 2D plane stress models.A critical discussion on stress distribution in the fibrous cap area is made with respect to HR for the first time.Results strongly suggest the viscoelastic properties of the fibrous cap tissue and HR together play a major role in the estimation of the PCS values.The results of current study may provide a better understanding on the mechanics of vulnerable atherosclerotic plaques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".