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Record W4388098524 · doi:10.1016/j.jmbbm.2023.106216

Active and passive mechanical characterization of a human descending thoracic aorta with Klippel-Trenaunay syndrome

2023· article· en· W4388098524 on OpenAlexafffund
Marco Amabili, Giulio Franchini, Meisam Asgari, Francesco Giovanniello, Mergen H. Ghayesh, Ivan D. Breslavsky

Bibliographic record

VenueJournal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · 2023
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill UniversityJohn Simon Guggenheim Memorial Foundation
KeywordsThoracic aortaAortaVasoactiveDescending aortaMedicineSmooth muscleVascular smooth muscleAnatomyCardiologyInternal medicineMaterials science

Abstract

fetched live from OpenAlex

A human aorta from a female donor affected by Klippel-Trenaunay syndrome was retrieved during a surgery for organ donation for transplant. The aorta was preserved in refrigerated Belzer UW organ preservation solution and tested within a few hours for mechanical characterization with and without vascular smooth muscle activation. KCl and Noradrenaline were used as vasoactive agents in bubbled Krebs-Henseleit buffer solution at 37 °C. A quasi-static and a dynamic mechanical characterization of the full wall and the three individual layers were carried out for strips taken in longitudinal and circumferential directions. The full wall in the descending portion of the aorta underwent mechanical tests with and without smooth muscle activation. Results were compared to data obtained from healthy aortas and show a reduced stiffness of the full wall in circumferential direction. Also, a significant reduction of the response to vasoactive agents in circumferential direction was observed, while the longitudinal response was similar to healthy cases.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

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