Tissue Response and Biodistribution of Injectable Cellulose Nanocrystal Composite Hydrogels
Bibliographic record
Abstract
Interest in cellulose nanocrystal (CNC)-based hydrogels for drug delivery, tissue engineering, and other biomedical applications has rapidly expanded despite the minimal in vivo research reported to date. Herein, we assess both in vitro protein adsorption and cell adhesion as well as in vivo subcutaneous tissue responses and CNC biodistribution of injectable CNC-poly(oligoethylene glycol methacrylate) (POEGMA) hydrogels. Hydrogels with different PEG side chain lengths, CNC loadings, and with or without in situ magnetic alignment of the CNCs are compared. CNC loading has a minimal impact on protein adsorption but significantly increases cell adhesion. In vivo, both CNC-only and CNC-POEGMA injections largely stay at their subcutaneous injection site over one month, with minimal bioaccumulation of CNCs in any typical clearance organ. CNC-POEGMA hydrogels exhibit mild acute and chronic inflammatory responses, although significant fibroblast penetration was observed with the magnetically aligned hydrogels. Collectively, these results suggest that CNC-POEGMA hydrogels offer promise in practical biomedical 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 | 0.000 | 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".