High-frequency quantitative ultrasound to assess the acoustic properties of engineered tissues in vitro
Bibliographic record
Abstract
Abstract Acoustic properties of biomaterials and engineered tissues reflect their structure and cellularity. High-frequency ultrasound (US) can non-invasively characterize and monitor these properties with sub-millimetre resolution. We present an approach to estimate the acoustic properties of cell-laden hydrogels that accounts for frequency-dependent effects of attenuation in coupling media, hydrogel thickness, and interfacial transmission/reflection coefficients of US waves, all of which can bias attenuation estimates. Cell-seeded fibrin hydrogel disks were raster-scanned using a 40 MHz US transducer. Thickness, speed of sound, acoustic impedance, and acoustic attenuation coefficients were determined from the difference in the time-of-flight and ratios of the magnitudes of US signals, interfacial transmission/reflection coefficients, and acoustic properties of the coupling media. With this approach, hydrogel thickness was accurately measured by US, with excellent agreement to confocal microscopy (r 2 = 0.97). Accurate thickness measurement enabled acoustic property measurements that were independent of hydrogel thickness, despite up to 60% reduction in thickness due to cell-mediated contraction. Notably, acoustic attenuation coefficients increased with increasing cell concentration (p<0.001), reflecting hydrogel cellularity independent of contracted hydrogel thickness. This approach enables accurate measurement of the intrinsic acoustic properties of biomaterials and engineered tissues to provide new insights into their structure and cellularity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".