Injectable and Dynamically Crosslinked Zwitterionic Hydrogels for Anti‐Fouling and Tissue Regeneration Applications
Bibliographic record
Abstract
A zwitterionic injectable and degradable hydrogel based on hydrazide and aldehyde-functionalized [2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl)ammonium hydroxide (DMAPS) precursor polymers that can address practical in vivo needs is reported. Zwitterion fusion interactions between the zwitterionic precursor polymers create a secondary physically crosslinked network to enable much more rapid gelation than previously reported with other synthetic polymers, facilitating rapid gelation at much lower polymer concentrations or degrees of functionalization than previously accessible in addition to promoting zero swelling and long-term degradation responses and significantly stiffer mechanics than are typically accessed with previously reported low-viscosity precursor gelation systems. The hydrogels maintain the highly anti-fouling properties of conventional zwitterionic hydrogels against proteins, mammalian cells, and bacteria while also promoting anti-fibrotic tissue responses in vivo. Furthermore, the use of the hydrogels for effective delivery and subsequent controlled release of viable cells with tunable profiles both in vitro and in vivo is demonstrated, including the delivery of myoblasts in a mouse skeletal muscle defect model for reducing the time between injury and functional mobility recovery. The combination of the injectability, degradability, and tissue compatibility achieved offers the potential to expand the utility of zwitterionic hydrogels in minimally invasive therapeutic 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) | 0.001 | 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".