Validation and application of a nondestructive and contactless method for rheological evaluation of biomaterials
Bibliographic record
Abstract
Hydrogels are extensively used for tissue engineering, cell therapy or controlled release of bioactive factors. Nondestructive techniques that can follow their viscoelastic properties during polymerization, remodeling, and degradation are needed, since these properties are determinant for their in vivo efficiency. In this work, we proposed the viscoelastic testing of bilayered materials (VeTBiM) as a new method for nondestructive and contact-less mechanical characterization of soft materials. The VeTBiM method measures the dynamic displacement response of a material, to a low amplitude vibration in order to characterize its viscoelastic properties. We validated VeTBiM by comparing data obtained on various agar and chitosan hydrogels with data from rotational rheometry, and compression tests. We then investigated its potential to follow the mechanical properties of chitosan hydrogels during gelation and in the presence of papain and lysozyme that induce fast or slow enzymatic degradation. Due to this nondestructive and contactless approach, samples can be removed from the instrument and stored in different conditions between measurements. VeTBiM is well adapted to follow biomaterials alone or with cells, over long periods of time. This new method will help in the fine tuning of the mechanical properties of biomaterials used for cell therapy and tissue engineering. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 2565-2573, 2017.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".