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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".