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E-004 Modeling the effect of hemodynamics on endothelial rna expression in cerebral aneurysms after endovascular flow diversion

2022· article· en· W4286701410 on OpenAlexaboutno aff
Guilherme Barros, E Federico, P Fillingham, Jiajie Xue, N Kaneko, Yihao Zheng, M Levitt

Bibliographic record

VenueSNIS 19th annual meeting electronic poster abstracts · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAneurysmPulsatile flowHemodynamicsOcclusionMedicineEmbolizationInternal carotid arteryThrombosisRadiologyCardiologyInternal medicineSurgery

Abstract

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Introduction Flow diverting stents (FDS) are a valuable endovascular option to treat unruptured cerebral aneurysms, with a single endoluminal device in the parent vessel ultimately promoting thrombosis and occlusion of the aneurysm sac. Flow diversion has yielded promising long-term aneurysm occlusion data; however, retreatment is required in up to 8% by 2 years. Questions remain about the biologic responses behind treatment successes and failures. The goal of this study is to understand how changes in hemodynamic flow after FDS placement affect aneurysmal endothelial RNA expression associated with treatment success or failure. Methods An in vitro, patient-specific three-dimensional aneurysm model with pre- and post-FDS treatment conditions was created to quantify the endothelial response specific to hemodynamic changes within the aneurysm dome seen on computational flow dynamic (CFD) simulations. Each model was seeded with human carotid endothelial cells and then subjected to pulsatile flow with patient-specific mean blood flow velocity for 24 hours (Figure 1). RNA was isolated from aneurysm dome, along with proximal and distal parent vessels serving as internal controls for each patient. The same experiments are performed both before and after FDS placement using commercially available FDS (Pipeline Embolization Device). Results Six unique patients with intracranial internal carotid artery aneurysms treated with FDS were included, of which three had successful treatments with complete occlusion, while three had treatment failure with persistent aneurysm filling requiring retreatment. Experiments are currently ongoing, with four of six pre-treatment models completed endothelial cell seeding, exposure to flow, and RNA isolation. Post-treatment models with implanted FDS will then undergo the same experiment. We then will use the colocalized CFD and RNA expression data to identify patterns consistent with treatment outcomes. Bulk RNA sequencing will be completed on each sample. Conclusions Direct study of the vascular endothelial response to changes in hemodynamic stress experienced in cerebral aneurysms after FDS has not yet been performed. This study could address the knowledge gap regarding the mechanisms of treatment outcome and angiographic occlusion, to ultimately improve endovascular treatments of cerebral aneurysms. Disclosures G. Barros: None. E. Federico: None. P. Fillingham: None. J. Xue: None. N. Kaneko: None. Y. Zheng: None. M. Levitt: 1; C; Stryker, Medtronic. 2; C; Medtronic, Metis Innovative. 4; C; Synchron, Cerebrotech, Proprio, Hyperion Surgical.

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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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

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)0.0030.001

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.

Opus teacher head0.003
GPT teacher head0.208
Teacher spread0.204 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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