Control of dense collagen gel scaffolds for tissue engineering through measurement and modeling of hydraulic permeability
Bibliographic record
Abstract
Among various natural biopolymers, type I collagen gels have demonstrated the highest potential as biomimetic scaffolds for tissue engineering (TE). However, the successful application of collagen gels requires a greater understanding of the relationship between their microstructure and physical-mechanical properties. Therefore, a precise method to modulate collagen gel microstructure in order to attain optimal scaffold properties for diverse biomedical applications is necessary. This dissertation describes a new approach to produce collagen gels with defined microstructures, quantified by hydraulic permeability (k), in order to optimize scaffold properties for TE applications. It was hypothesized that the measurement of k can be used to study the role of microstructure in collagen gel properties, as well as cell function and cell-scaffold interactions. Applying increasing levels of plastic compression (PC) to the highly hydrated collagen gels resulted in an increase in collagen fibrillar density, reduced Happel model derived k values, increased gel stiffness, promoted MSC metabolic activity, osteogenic differentiation, and mineral deposition, while cell-induced gel contraction diminished. Thus, collagen gels with lower k and higher stiffness values exhibited greater potential for bone tissue engineering.Correlating between collagen gel microstructure, k, and fibroblast function within collagen gels indicated that increasing the level of PC yielded a reduction in pore size and an increase in fibril bundle diameter. Decrease in k values resulted in a decrease in gel contraction and an increase in cell metabolic activity. An increase in cell density accelerated contraction. Therefore, fibroblast function within collagen gels can be optimised by a balance between the microstructure, k, and cell seeding density.Developing a micromechanical model to measure experimental k of collagen gels during confined compression revealed the formation of a dense collagen lamella at the fluid expulsion boundary, thereby generating a two-layer model. By applying gel mass loss into Darcy's law, experimental k values of the lamella, along with the thickness of lamella (c) and hydrated gel layer (b) were measured. An increase in either compression level or compression time resulted in a decrease in k, decrease in b, and an increase in c. In conclusion, controlled compression of collagen gels can be used to produce multi-layered biomimetic scaffolds with defined microstructures and k in order to attain optimal properties for tissue engineering applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".