Engineered Janus hydrogels: biomimetic surface engineering and biomedical applications
Bibliographic record
Abstract
Hydrogel bioadhesives, when applied to dysfunctional tissues substituting the epidermis or endothelium, exhibit compelling characteristics that enable revolutionary diagnostic and therapeutic procedures. Despite their demonstrated efficacy, these hydrogels as soft implants are still limited by improper symmetric surface functions, leading to postoperative complications and disorders. Janus hydrogel bioadhesives with unique asymmetric surface designs have thus been proposed as a reliable and biocompatible hydrogel interface, mimicking the structural characteristics of natural biological barriers. In this comprehensive review, we provide guidelines for the rational design of Janus hydrogel bioadhesives, covering methods for hydrogel surface chemistry and microstructure engineering. The engineering of Janus hydrogels is highlighted, specifically in tuning the basal surface to facilitate instant and robust hydrogel-tissue integration and modulating the apical surface as the anti-adhesion, anti-fouling, and anti-wear barrier. These asymmetric designs hold great potential in clinical translation, supporting applications including hemostasis/tissue sealing, chronic wound management, and regenerative medicine. By shedding light on the potential of Janus hydrogels as bioactive interfaces, this review paper aims to inspire further research and overcome current obstacles for advancing soft matter in next-generation healthcare.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".