Orchestrating Asymmetric Surface Functionalities on Hydrogel Stamps where Adhesion Meets Lubrication
Bibliographic record
Abstract
Bioadhesives applied on human organs are promising soft implants for interventional diagnostics and therapeutics. However, the conventional bioadhesive interfaces on organs typically lack apical–basal polarity to resemble the surface functions of original organ epithelia. To overcome the bioadhesive-induced dysfunction on treated organs, we have developed an innovative strategy via engineering asymmetric surface functionalities on a tough yet biodegradable polysaccharide-peptide-derived hydrogel platform, mimicking the functions of a ciliated columnar epithelium enabled by its adhesive basal surface and defensive apical ciliated surface. The resulting hydrogel bioadhesive serves as a “stamp” with a polyacrylic acid-functionalized adhesive side, facilitating instant and robust adhesion on wet tissues within 1 min via body liquid-removing mechanisms and Ca 2+ -assisted complexation. The back side is functionalized with hyaluronic acid, demonstrating an outstanding biolubrication performance (coefficient of friction of ∼0.038 in the synovial fluid). The hydrogel stamp can also be integrated with biosensing and drug encapsulation/release functions for diagnostics and therapeutics. Our strategy devises a new path to simultaneously enable reliable wet tissue adhesion and reproduce the characteristics of original tissues, with useful insights into designing bioactive interfaces for broad 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".