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Modeling Lipid Uptake in Expanded Polytetrafluoroethylene Vascular Prostheses and Its Effects on Mechanical Properties

2000· article· en· W2095193650 on OpenAlexaff
Diego Mantovani, Patrick Vermette, Michel Fiset, Robert Guidoin, Gaétan Laroche

Bibliographic record

VenueArtificial Organs · 2000
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolytetrafluoroethyleneProsthesisBiomedical engineeringMicroporous materialMaterials scienceChemistryUltimate tensile strengthComposite materialSurgeryMedicine

Abstract

fetched live from OpenAlex

The radial transport across the wall of expanded polytetrafluoroethylene (ePTFE) arterial prostheses has a significant effect on lipid uptake observed in prostheses implanted in humans, which has been postulated to be one of the causes associated with implant failure. The goal of this study was to stimulate radial transport on a lipidic dispersion across the wall of an ePTFE prosthesis and investigate its effects on the circumferential mechanical properties of the prosthesis. An in vitro model was developed to simulate the lipidic radial transport across the wall. Lipids contained in a phosphatidylcholine dispersion were used as the transported molecules. Lipid concentration profiles were obtained after exposing commercial ePTFE prostheses to various transmural pressure and/or lipidic concentration gradients. Phospholipids gradually accumulated up to the external reinforcing wrap of the prosthesis, which clearly acted as a rigid barrier against lipid infiltration. Tensile tests performed on the virgin samples showed that the wrap was much more rigid than the microporous part of the prosthesis. After the lipid simulation, the rigidity of the wrap decreased with respect to what was observed for the virgin prosthesis. Finally, some clinical implications of this phenomena are discussed.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.036
GPT teacher head0.246
Teacher spread0.210 · 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".

Quick stats

Citations6
Published2000
Admission routes1
Has abstractyes

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