Standardized static and dynamic evaluation of myocardial tissue properties
Bibliographic record
Abstract
Abstract Introduction. Quantifying the mechanical behaviors of soft biological tissues is of considerable research interest. However, validity and reproducibility between different researchers and apparatus is questionable. This study aims to quantify the mechanical properties of myocardium while investigating methodologies that can standardize biological tissue testing. Materials and Methods. Tensile testing was performed to obtain Young’s modulus and a dynamic mechanical analysis (DMA) determined the viscoelastic properties. A frequency range of 0.5 Hz (30bpm) to 3.5 Hz (210bpm) was analyzed. For tensile testing three different preconditioning settings were tested: no load, 0.05 N preload, and a cyclic preload at 2.5% strain and 10 cycles. Samples were placed in saline and tested at 37 °C. Five ovine and five porcine hearts were tested. Results and Discussion. Cyclic loading results in the most consistent moduli values. The modulus of ovine/porcine tissue was mean = 0.05/.06 MPa, SD = 0.02/0.03 MPa. The storage/loss modulus varied from = 0.02/0.003 MPa at 0.5 Hz to 0.04/0.008 MPa at 3.5 Hz; Stiffness increases linearly from 400 to 800 N m −1 with a tan delta around 0.175. Conclusions. Static analysis of the mechanical properties of myocardial tissue confirms that; preconditioning is necessary for reproducibility, and DMA provides a platform for reproducible testing of soft biological tissues.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".