Listening to bio-engineered tissues and materials: Assessing cellularity and stiffness using high-frequency ultrasound
Bibliographic record
Abstract
Current cardiac drug screening and testing methods lack the power to eliminate ineffective and harmful drug candidates to treat heart failure, a growing epidemic in Canada. Miniaturized three-dimensional heart models show great promise to better mimic human biology, disease, and drug responses, but their full potential requires innovative analytical tools to assess disease- and drug-induced alterations in tissue microstructure and mechanical properties. Current techniques to measure the microstructure and stiffness of miniature heart models are invasive, low-throughput, and incapable of long-term monitoring. In this thesis, an ultrasound imaging method is developed to evaluate the acoustic and mechanical properties of engineered tissues and biomaterials to address these analytical tool gaps. This high-frequency ultrasound technique is non-destructive, non-invasive, and capable of real-time monitoring. We show that high-frequency ultrasound can accurately measure the intrinsic acoustic properties of biomaterials and engineered tissues by accounting for attenuation effects in coupling media, hydrogel thickness, and interfacial transmission/reflection coefficients. This approach enabled independent assessment of hydrogel cellularity despite significant contraction, providing valuable insights into tissue structure. We also developed a novel high-frequency ultrasound elastography (USE) system with a highly focused acoustic radiation force (ARF) excitation transducer, achieving non-invasive and high-resolution measurements of biomaterial mechanical properties, with strong agreement to shear rheometry. Through in silico modelling, we demonstrated that shear wave frequency exhibits non-linear dependencies on ARF excitation duration and tissue properties, enhancing the accuracy of mechanical characterization. Finally, we applied this USE system to cardiac cell-laden fibrin domes with validation to previous studies. Our work advances high-frequency ultrasound as a powerful tool for assessing biomaterial mechanics and engineered tissue properties, with future work focused on system improvements and future applications in dynamic tissue systems and organ-on-a-chip platforms to better understand bioengineered tissue behaviour in healthy, diseased, and treated states.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".