Tunable Collagen Hydrogels are Modified by the Therapeutic Agents They are Designed to Deliver
Bibliographic record
Abstract
Injectable hydrogels are increasingly being developed for biomedical applications due to their ability to be delivered in a minimally invasive manner. One potential use for such materials is in cell delivery for cardiac regeneration. While the materials' properties are often characterized, how these properties (and in particular gelation) are affected by the addition of the therapeutic agent(s) they are designed to deliver is often overlooked. The aim of this study was to examine the interactive effects between collagen-based hydrogels and different additives (cells and microspheres). The results demonstrated that the incorporation of either cells or microspheres to a collagen hydrogel decreased its gelation time and increased its viscosity. Increased concentrations of the EDC/NHS cross-linker resulted in greater loss of cell viability. However, it was found that this cell loss could be minimized by delivering cells with the cross-linker scavenger glycine. A better understanding of how materials and cells (and other additives) respond to each other will help towards the goal of improving scaffolds being developed for regenerative therapy.
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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".