Effect of Fibrillization pH on Gelation Viscoelasticity and Properties of Biofabricated Dense Collagen Matrices via Gel Aspiration-Ejection
Bibliographic record
Abstract
Reconstituted hydrogels based on the self-assembly of acid-solubilized collagen molecules have been extensively used as in vitro models and precursors in biofabrication processes. This study investigated the effect of fibrillization pH-ranging from 4 to 11-on real-time rheological property changes during the gelation of collagen hydrogels and its interplay with the properties of subsequently biofabricated dense collagen matrices generated via automated gel aspiration-ejection (GAE). A contactless, nondestructive technique was used to characterize the temporal progression in shear storage modulus (G', or stiffness) during collagen gelation. There was a relative increase in G' of the hydrogels from 36 to 900 Pa with an increase in gelation pH. Automated GAE, which simultaneously imparts collagen fibrillar compaction and alignment, was then applied to these precursor collagen hydrogels to biofabricate native extracellular matrix-like densified gels. In line with viscoelastic properties, only hydrogels fibrillized in the 6.5 < pH ≤ 10 range could be densified via GAE. There was an increase in both fibrillar density and alignment in the GAE-derived matrices with an increase in gelation pH. These factors, combined with a higher G' in the alkaline precursor hydrogels, led to a significant increase in the micro-compressive modulus of GAE-densified gels of pH 9 and 10. Furthermore, NIH/3T3 fibroblast-seeded GAE-derived matrices densified from gels fibrillized in the pH range of 7 to 10 exhibited low cell mortality with >80% viability. It is anticipated that the results of this study can be potentially applicable to other hydrogel systems, as well as biofabrication techniques involving needles or nozzles, such as injection and bioprinting.
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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".