Effects of Cell Density on Mechanical Properties of Alginate Hydrogel Tissue Scaffolds
Bibliographic record
Abstract
Cell-seeded hydrogel scaffolds have been widely used in various tissue engineering applications due to their excellent biocompatibility and biomimetic properties. One of the critical issues in successful use of hydrogel scaffolds is their mechanical properties. Since cells and hydrogels are physically different materials, the cells encapsulated in the hydrogels can change profoundly the mechanical properties of the hydrogel scaffolds. In this research, the effects of Schwann cell density on mechanical properties of alginate hydrogel scaffolds were investigated. It was found that increase of cell density decreases the strength of the scaffolds. It was also found that the Ogden model can best describe the mechanical properties of the scaffolds under the strain of 45% at varying cell densities. Based on the cell density-dependant mechanical properties, a simulation was performed to study the local stresses of on cells when cells are subjected to loading. Simulation shows that at the same strain, the stress concentration on cells decreases as the cell density increases. The experimental and simulation results obtained in this paper will allow one to rigorously design scaffolds with desired mechanical properties and provide a clue to avoid mechanical cell injury.
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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 | 0.001 |
| 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".