Characterization of hydrogel-scaffold mechanical properties and microstructure by using synchrotron propagation-based imaging
Bibliographic record
Abstract
Hydrogel-based scaffolds have been widely used in soft tissue regeneration due to their biocompatible and tissue-like environment for maintaining cellular functions and tissue regeneration. Understanding the mechanical properties and internal microstructure of hydrogel-based scaffold, once implanted, is imperative in tissue engineering applications and longitudinal studies. Notably, this has been challenging to date as various conventional characterization methods by, for example, mechanical testing (for mechanical properties) and microscope (for internal microstructure) are destructive as they require removing scaffolds from the implantation site and processing samples for characterization. Synchrotron radiation propagation-based imaging-computed tomography (SR-PBI-CT) is feasible and promising for non-destructive visualizing of hydrogel scaffolds. As inspired, this study aimed to perform a study on the characterization of mechanical properties and microstructure of hydrogel scaffolds based on the SR-PBI-CT. In this study, hydrogel biomaterial inks composed of 3% w/v alginate and 1% w/v gelatin were printed to form scaffolds, with some scaffolds being degraded over 3 days. Both degraded and undegraded scaffolds underwent compressive testing, with the strains being controlled at the preset values; meanwhile stresses within scaffolds were measuring, resulting the stress-strain curves. Concurrently, the scaffolds were also imaged and examined by SR-PBI-CT at Canadian Light Source (CLS). During the imaging process, the scaffolds were mechanically loaded, respectively, with the strains same as the ones in the aforementioned compressive testing, and at each strain, the scaffold was scanned with a pixel size of 13 μm. From the stress-strain curves obtained in the compression testing, the Young's modulus was evaluated to characterize the elastic behavior of scaffolds: with the range between around 5-25 kPa. From the images captured by SR-PBI-CT, the scaffolds microstructures were examined in terms of the strand cross-section area, pore size, and hydrogel volume. Further, from the SR-PBI-CT images, the stress within hydrogel of scaffolds were evaluated, showing the agreement with those obtained from compression testing. These results have illustrated that the mechanical properties and microstructures of scaffolds, ether being degraded or not, can be examined and characterized by the SR-PBI-CT imaging, in a non-destructive manner. This would represent a significant advance for facilitating longitudinal studies on the scaffolds once implanted in-vivo.
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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 | 0.000 | 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".