A high frequency ultrasound-based platform to non-destructively quantify geometric, acoustic and mechanical properties of thin, engineered soft connective tissues in vitro
Bibliographic record
Abstract
Assessment of temporal changes in the functional properties of in vitro tissue engineered (TE) constructs is typically done by destructive endpoint analysis, which increases experimental resources, analysis time, and financial burden. To overcome the limitations of existing practices, a novel Modular High Frequency Ultrasound Bulge Testing System (mHFUS-BTS) was designed to perform repeated, non-destructive and non-invasive characterization of cell-seeded biomaterial sheets throughout culture. Quantitative HFUS was validated to accurately measure the acoustic properties (i.e., speed of sound, acoustic impedance) to determine the physical properties (thickness and density) of TE-relevant polymeric scaffolds mounted inside a modified 6-well plate (<2 % mean error for all parameters). Components were interchanged to enable in situ bulge testing to estimate samples' Young's modulus (estimations <5 % mean error). Over an 18-day culture period, the mHFUS-BTS successfully monitored the thickening and softening of cell-seeded electrospun polyurethane polycarbonate scaffolds due to tissue synthesis while cell-free constructs experienced insignificant changes. Sterility was maintained throughout culture, with no effect of HFUS on cell viability or tissue composition compared to non-tested samples. These results demonstrate the mHFUS-BTS can repeatedly assess the physical, acoustic and mechanical properties of engineered tissues in vitro without influencing cell viability or tissue formation.
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 | 0.000 |
| 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 | 0.001 |
| 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".