Effect of freezing on the passive mechanical properties of arterial samples
Bibliographic record
Abstract
Little mechanical data is available on human arteries because of the difficulty of testing artery samples often obtained from autopsy, while arteries are still considered “fresh”. Various solutions mimicking the physiological environment have been used to preserve artery samples from harvesting to testing. Cryopreservation might provide a means to preserve the mechanical properties of arteries for days or weeks after harvesting. The objective of this study is to investigate the effect of several preservation methods, including simplified cryopreservation methods, on the passive mechanical properties of arteries. Eighteen fresh cruciform samples were mechanically tested. Samples were divided in three groups based on preservation medium and freezing method: isotonic saline solution, Krebs-Henseleit buffer solution with dimethyl sulfoxide (DMSO), and dipped in liquid nitrogen. In each group, half of the samples were stored at -20℃ and the other half at -80℃. Two months later, all the tissues were thawed at 4℃ and mechanical tests were repeated. Preservation of arteries for two months in Krebs solution with DMSO (at -20℃ or at -80℃) or in isotonic saline solution at -20℃ were the methods that least changed the mechanical properties of the arteries.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".