Development of an in vitro system for cardiovascular flow measurements and computational study of cerebral aneurysm hemodynamics
Bibliographic record
Abstract
As the population ages, the prevalence of the cardiovascular disease, which is the leading cause of death in the world, is projected to increase. The aneurysm is a cardiovascular disease which can be defined as an excessive localized enlargement of an artery caused by a weakening of the artery wall. Abdominal aortic aneurysm (AAA) and intracranial aneurysm (IA) are the most propense aneurysm types to develop. When the aneurysm enlarges, there is a high risk of aneurysm rupture which can lead to serious bleeding, or even death. The causes of aneurysm initiation, progression, and rupture are complex and not fully comprehended. However, it is well accepted that hemodynamics has an essential role in the aneurysm development and progress. The main objective of this thesis is to implement a joint experimental-computational approach to study the aneurysms. The first specific objective was to design and build an in vitro experimental setup which can mimic the hemodynamics for different circulatory regions with tunable physiological conditions. The benchtop system was specifically developed to increase experimental efficiency and maintain high experimental accuracy. The experimental setup was used to replicate the physiological flow and pressure conditions as found in an AAA. The second specific objective was to study intracranial sidewall aneurysms. A computational fluid dynamics (CFD) analysis was conducted to study sidewall aneurysm hemodynamics. An idealized aneurysm geometry was used to determine the effect that a stent treatment device had on aneurysm hemodynamics. Newtonian and non-Newtonian working fluids, matching the human blood density and viscosity, were considered. The study showed that using a Newtonian model, the hemodynamic parameters were overestimated in the intra-aneurysmal sac region in comparison to the non-Newtonian model. Furthermore, the presence of the stent device showed an alteration on the flow patterns inside the aneurysm sac by reducing the overall aneurysmal blood flow velocity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".