Tailoring Mechanical Properties of Collagen-Based Scaffolds for Vascular Tissue Engineering: The Effects of pH, Temperature and Ionic Strength on Gelation
Bibliographic record
Abstract
Collagen gels have been widely studied for applications in tissue engineering because of their biological implications. Considering their use as scaffolds for vascular tissue engineering, the main limitation has always been related to their low mechanical properties. During the process of in vitro self-assembly, which leads to collagen gelation, the size of the fibrils, their chemical interactions, as well as the resulting microstructure are regulated by three main experimental conditions: pH, ionic strength and temperature. In this work, these three parameters were modulated in order to increase the mechanical properties of collagen gels. The effects on the gelation process were assessed by turbidimetric and scanning electron microscopy analyses. Turbidity measurements showed that gelation was affected by all three factors and scanning electron images confirmed that major changes occurred at the microstructural level. Mechanical tests showed that the compressive and tensile moduli increased by four- and three-fold, respectively, compared to the control. Finally, viability tests confirmed that these gels are suitable as scaffolds for cellular adhesion and proliferation.
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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".