Experimental Investigation Of The Effect Of Compliance Properties On The Flow In An Analogous Ex-Vivo Heart Perfusion System
Bibliographic record
Abstract
The heart transplant waiting list far exceeds supply.This shortage is generally attributed to the high number of discarded hearts and to the narrow six-hour time window currently available through the standard preservation method: static cold storage (SCS).An alternative method called exvivo heart perfusion (EVHP) maintains a human donor heart beating outside the body for the time preceding transplantation surgery.By keeping the heart working in a physiologically consistent way and monitoring its functions, the organ's health can be assessed and the transplant time window can be extended.In order to improve and optimize the EVHP system, the present work aims to further investigate the relationship between pulsatile flow and compliance by assessing the effect of different compliant tubes on the upstream and downstream pressure and flow fields.Hence, silicone tubes of variable compliance, length, and geometrical shape were developed for this studyalthough only one sample has been tested so far, in addition to an experimental setup containing a hydraulic circuit analogous to the left flow loop of the EVHP system.The flow fields downstream of the compliant section are assessed using laser Doppler velocimetry (LDV).Findings will include novel visualizations of these flow fields as well as comparisons of pressure waveforms from an assortment of compliance conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".