Synthesis and Characterization of In Situ Gelling Hydrogels Made From Hyperbranched Poly(oligoethylene glycol methacrylate)
Bibliographic record
Abstract
Hydrogels have attracted interest as biomaterials due to their similarity to native tissue and extracellular matrix as well as their versatility and tunability. Each of these characteristics allows hydrogels to be used in a wide variety of biomedical applications including drug delivery, tissue engineering, and regenerative medicine. Poly(oligoethylene glycol methacrylate) (POEGMA) has been shown to possess attractive biological and thermoresponsive properties, serving as an alternative to both poly(ethylene glycol) (PEG) and poly(N-isopropylacrylamide) (PNIPAM) depending on the number of ethylene oxide repeat units in the POEGMA side chain. Our group has shown the versatility of POEGMA and has successfully developed hydrazide- and aldehyde-functionalized polymer precursors that form an injectable in situ gelling hydrogel. By engineering the precursor polymer structure and crosslinking density (i.e. number of reactive functional groups in the precursor polymers), the properties of these hydrogels can be tuned. Herein, a hyperbranched structure was incorporated into POEGMA precursors to control the physical and biological properties of hydrogels independent of the chemistry while maintaining gel injectability. By varying the degree of branching (DoB) in these precursors, it was possible to tune the hydrogel properties based on reacting combinations of hyperbranched-linear and hyperbranched-hyperbranched precursor polymers. While it was feasible to tune the mechanical properties of the hyperbranched hydrogels based on the DoB, the hyperbranched-hyperbranched system showed diminished mechanical strength when compared to the hyperbranched-linear system. Overall, the mechanical properties of the whole hydrogel series were comparable to previously reported linear POEGMA hydrogels. In terms of swelling and degradation kinetics, the swelling and degradation rate in both acid-catalyzed conditions and in phosphate-buffered saline (PBS) at physiological temperature (37°C) correlated with DoB and polymer size. The precursor polymers showed minimal cytotoxicity in the presence of 3T3 mouse fibroblasts. Lastly, each of the hyperbranched hydrogels adsorbed higher quantities of protein compared to PEG-based hydrogels, but still relatively low amounts compared to other polymeric biomaterials. We have shown that it is possible to significantly tune the physicochemical properties by slightly changing the polymer precursor chemistry, namely by varying the amount of crosslinker and, thus, the degree of branching in the polymer network. Therefore, hyperbranched POEGMA offers a versatile platform to create tunable hydrogels based on polymer precursor structure for biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.030 | 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 teacher head, 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".