A gel aspiration-ejection system for the controlled production and delivery of injectable dense collagen scaffolds
Bibliographic record
Abstract
A gel aspiration-ejection (GAE) system has been developed for the advanced production and delivery of injectable dense collagen (I-DC) gels of unique collagen fibrillar densities (CFDs). Through the creation of negative pressure, GAE aspirates prefabricated highly hydrated collagen gels into a needle, simultaneously inducing compaction and meso-scale anisotropy (i.e., fibrillar alignment) on the gels, and by subsequent reversal of the pressure, I-DC gels can be controllably ejected. The system generates I-DC gels with CFDs ranging from 5 to 32 wt%, controlling the initial scaffold microstructure, anisotropy, hydraulic permeability, and mechanical properties. These features could potentially enable the minimally invasive delivery of more stable hydrogels. The viability, metabolic activity, and differentiation of seeded mesenchymal stem cells (MSCs) was investigated in the I-DC gels of distinct CFDs and extents of anisotropy produced through two different gauge needles. MSC osteoblastic differentiation was found to be relatively accelerated in I-DC gels that combined physiologically relevant CFDs and increased fibrillar alignment. The ability to not only support homogenous cell seeding, but also to direct and accelerate their differentiation through tissue-equivalent anisotropy, creates numerous opportunities in regenerative medicine.
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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) | 0.000 | 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".