Drug release from polymer-coated TiO<sub>2</sub> nanotubes on additively manufactured Ti-6Al-4V bone implants: a feasibility study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Insufficient osseointegration, inflammatory response and bacterial infection are responsible for the majority of bone implant failures. Drug-releasing implants subjected to adequate surface modification can concurrently address these challenges to improve the success of implant surgeries. This work investigates the use of Ti-6Al-4V (Ti64) with a dual-scale surface topography as a platform for local drug delivery. Dual-scale topography was obtained combining the inherent microscale roughness of the Ti64 samples manufactured by selective laser melting (SLM) with the nanoscale roughness of TiO 2 nanotubes (TNTs) obtained by subsequent electrochemical anodization at 60 V for 30 min. TNTs were loaded with a solution of penicillin-streptomycin, a common antibiotic, and drug release was tested in vitro. Three biocompatible and biodegradable polymers, i.e. chitosan, poly(ε-caprolactone) and poly(3-hydroxybutyrate), were deposited by spin coating, while preserving the microscale topography of the substrate underneath. The presence of polymer coatings overall modified the drug release pattern, as revealed by fitting of the experimental data with a power-law model. A slight extension in the overall duration of drug release (about 17% for a single layer and 33% for two layers of PCL and PHB) and reduced burst release was observed for all polymer-coated samples compared to uncoated, especially when two layers of coatings were applied.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 | 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 it