Injectable Self-Healing Zwitterionic Hydrogels Based on Dynamic Benzoxaborole–Sugar Interactions with Tunable Mechanical Properties
Bibliographic record
Abstract
Dynamic hydrogels based on arylboronic esters have been considered as ideal platforms for biomedical applications given their self-healing and injectable characteristics. However, there still exist some critical issues that need to be addressed or improved, including hydrogel biocompatibility, physiological usability, and tunability of mechanical properties. Here, two kinds of phospholipid bioinspired MPC copolymers, one is zwitterionic copolymer (PMB) containing a fixed 15 mol % of benzoxaborole (p K a ≈ 7.2) groups and the other is zwitterionic glycopolymers (PMG) with varied ratios of sugar groups (20%, 50%, 80%), were synthesized respectively via one-pot facile reversible addition–fragmentation chain transfer (RAFT) polymerization. PMBG hydrogels were formed spontaneously after mixing 10 wt % of PMB and PMG copolymer solutions because of dynamic benzoxaborole–sugar interactions. The mechanical properties of nine hydrogels (3 × 3) with different sugar contents and pHs (7.4, 8.4, 9.4) were carefully studied by rheological measurements, and hydrogels with higher sugar content and higher pH were found to have higher strength. Moreover, similar to other arylboronic ester-based hydrogels, PMBG hydrogels possessed not only self-healing and injectable properties but also pH/sugar responsiveness. Additionally, in vitro cytotoxicity tests of gel extracts on both normal and cancer cells further confirmed the excellent biocompatibility of the hydrogels, which should be ascribed to the biomimetic nature of phosphorylcholine (PC) and sugar residues of the copolymers. Consequently, the zwitterionic dynamic hydrogels provide promising future for diverse 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".