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Record W2886244720 · doi:10.1504/ijad.2018.10015270

Brinkman-Forchheimr model for fat accumulation in arterial wall

2018· article· en· W2886244720 on OpenAlexaff
M. Ziad Saghir, Mohammadali Ahmadipour, Md Abdur Rahman

Bibliographic record

VenueInternational Journal of Aerodynamics · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPulsatile flowHyperthermiaArterial wallMechanicsBlood flowArteryBiomedical engineeringFlow (mathematics)Materials scienceChemistryCardiologyInternal medicineMedicinePhysics

Abstract

fetched live from OpenAlex

A new model based on Brinkman-Forchheimr equation on transport of low-density lipoprotein (LDL) in artery wall has been developed that includes the multi-layered model. Artery wall has been considered as homogeneous porous media, Staverman filtration and osmotic reflection coefficient has been considered. The realistic physical properties are available and have been obtained from the literature. Various models have been compared with the proposed model. The results are consistent with the other numerical and experimental studies. Effects of hypertension and pulsatile flow on LDL transport in arterial multi-layered wall have been studied in detail. Effect of pulsatile flow is imperative since blood flow in artery is a pulsatile flow. Hyperthermia is important for the treatment of cancer. Soret effect on LDL concentration distribution has been analysed for the hyperthermia condition. This model will help to better understand the effect of hypertension and hyperthermia on atherosclerotic diseases. Results reveal that LDL accumulation is directly related to the transmural pressure. For the straight artery, effect of hyperthermia is negligible for LDL accumulation due to the very thin arterial wall. Effect of pulsatile flow in LDL transport has been found negligible.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.284

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 head0.041
GPT teacher head0.374
Teacher spread0.333 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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