Bionic Engineered Protein Coating Boosting Anti‐Biofouling in Complex Biological Fluids
Bibliographic record
Abstract
Implantable medical devices have been widely applied in diagnostics, therapeutics, organ restoration, and other biomedical areas, but often suffer from dysfunction and infections due to irreversible biofouling. Inspired by the self-defensive "vine-thorn" structure of climbing thorny plants, a zwitterion-conjugated protein is engineered via grafting sulfobetaine methacrylate (SBMA) segments on native bovine serum albumin (BSA) protein molecules for surface coating and antifouling applications in complex biological fluids. Unlike traditional synthetic polymers of which the coating operation requires arduous surface pretreatments, the engineered protein BSA@PSBMA (PolySBMA conjugated BSA) can achieve facile and surface-independent coating on various substrates through a simple dipping/spraying method. Interfacial molecular force measurements and adsorption tests demonstrate that the substrate-foulant attraction is significantly suppressed due to strong interfacial hydration and steric repulsion of the bionic structure of BSA@PSBMA, enabling coating surfaces to exhibit superior resistance to biofouling for a broad spectrum of species including proteins, metabolites, cells, and biofluids under various biological conditions. This work provides an innovative paradigm of using native proteins to generate engineered proteins with extraordinary antifouling capability and desired surface properties for bioengineering 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) | 0.000 | 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 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".