Enhancing Osseointegration and Mitigating Bacterial Biofilms on Medical-Grade Titanium with Chitosan-Conjugated Liquid-Infused Coatings
Bibliographic record
Abstract
Abstract Titanium alloys, in particular, medical-grade Ti-6Al-4V is heavily used in orthopaedic applications due to its high moduli, strength, and biocompatibility. Implant infection can result in biofilm formation and failure of prosthetics. The formation of a biofilm on implants protect bacteria from antibiotics and the immune response, resulting in the propagation of the infection and ultimately result in device failure. Recently, slippery liquid-infused surfaces (LIS) have been investigated for their stable liquid interface, which provide excellent repellent properties to suppress biofilm formation. One of the current limitations of LIS coatings lies in the indistinctive repellency of bone cells in orthopaedic applications, therefore causing poor integration between tissue and implant. Here, we report a chitosan impregnated LIS coating that facilitates cell adhesion and osseointegration while preventing biofilm formation. Our results indicate that chitosan-conjugated LIS increased cell adhesion of osteoblast-like SaOS-2 cells and significantly promoted proliferation compared to conventional titanium liquid-infused surfaces. Furthermore, the chitosan conjugated LIS significantly reduced biofilm formation of methicillin-resistant Staphylococcus aureus (MRSA) when compared to untreated and chitosan-coated titanium. Our engineered coating can be easily modified with other biopolymers or capture molecules to be applied to other biomaterials where both tissue integration and biofilm prevention is needed.
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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".