Recent advances in fabricating injectable hydrogels <i>via</i> tunable molecular interactions for bio-applications
Bibliographic record
Abstract
formation properties, simplify the implantation process and reduce tissue invasion, which can be directly delivered to target sites using a syringe injection, offering distinct advantages over traditional hydrogels. These injectable hydrogels incorporate physically non-covalent and/or dynamic covalent bonds, granting them self-healing abilities to recover their structural integrity after injection. This review summarizes our recent progress in preparing injectable hydrogels and discusses their performance in various bioengineering applications. Moreover, the underlying molecular interaction mechanisms that govern the injectable and functional properties of hydrogels were characterized by using nanomechanical techniques such as surface forces apparatus (SFA) and atomic force microscopy (AFM). The remaining challenges and future perspectives on the design and application of injectable hydrogels are also discussed. This work provides useful insights and guides future research directions in the field of injectable hydrogels for bioengineering.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".