Characterization of Mechanical Properties of Tissue Scaffolds by Phase Contrast Imaging and Finite Element Modeling
Bibliographic record
Abstract
In tissue engineering, the cell and scaffold approach has shown promise as a treatment to regenerate diseased and/or damaged tissue. In this treatment, an artificial construct (scaffold) is seeded with cells, which organize and proliferate into new tissue. The scaffold itself biodegrades with time, leaving behind only newly formed tissue. The degradation qualities of the scaffold are critical during the treatment period, since the change in the mechanical properties of the scaffold with time can influence cell behavior. To observe in time the scaffold's mechanical properties, a straightforward method is to deform the scaffold and then characterize scaffold deflection accordingly. However, experimentally observing the scaffold deflection is challenging. This paper presents a novel study on characterization of mechanical properties of scaffolds by phase contrast imaging and finite element modeling, which specifically includes scaffold fabrication, scaffold imaging, image analysis, and finite elements (FEs) modeling of the scaffold mechanical properties. The innovation of the work rests on the use of in-line phase contrast X-ray imaging at 20 KeV to characterize tissue scaffold deformation caused by ultrasound radiation forces and the use of the Fourier transform to identify movement. Once deformation has been determined experimentally, it is then compared with the predictions given by the forward solution of a finite element model. A consideration of the number of separate loading conditions necessary to uniquely identify the material properties of transversely isotropic and fully orthotropic scaffolds is also presented, along with the use of an FE as a form of regularization.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".