An in vitro time study of distensibility in porcine aortas using high resolution X-ray CT
Bibliographic record
Abstract
Porcine aortas have been used extensively for medical research because of their availability and similarities to human aortas. The elastic properties of porcine aortas have also been studied and quantified, yet there has been no initiative to study the effect of time on the elastic properties of in vitro porcine aortic specimens when pressurized with humidified air, which the authors use as contrast for the arterial wall when X-ray imaging (CT) is used to determine the effect of pressure on aortic dimensions. These experiments were designed to clarify whether the use of humidified air to pressurize the specimens affects the elastic properties of the tissue over a period of time. Seven porcine aortas (four thoracic and three abdominal) studied. The specimens were cleaned of adipose tissue and made pressure tight by tying off the side branches. Each specimen was pressurized from 4 to 24 kPa with humidified air and imaged on days 0, 3, 5 and 7 by mounting the aortas in a high-resolution laboratory computed tomography (CT) scanner. Between imaging sessions the aortas were stored in a saline solution and refrigerated at 5/spl deg/C. Distensibility was then calculated from luminal perimeter measurements obtained from 2-dimensional cross-sectional slices through the aorta at six different pressures. Approximately 50 minutes were needed to acquire each set of data. The lumen perimeter increased after each imaging session, presumably due to creep. The distensibility of the thoracic aortas decreased in the range of 4.2-12.7% over the duration of the experiments and their perimeter-pressure curves were linear. The distensibilities of the abdominal aortas varied considerably from an increase of 16.5% to a decrease of 58%, and their perimeter-pressure curves were nonlinear. The wide range in results, particularly with the abdominal aortas, indicates that further studies are required.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".