Nanoporous Titanium Surfaces for Sustained Elution of Proteins and Antibiotics
Bibliographic record
Abstract
Current medically relevant metals for prosthetic reconstructions enjoy a relatively good success rate, but their performance drops significantly in patients with compromised health status, and post-surgical infections still remain an important challenge. To address these problems, different nanotechnology-based strategies have been exploited to create implantable metals with an enhanced bioactivity and antibacterial capacities. Among these, oxidative nanopatterning has emerged as a very effective approach to engender nanoporous surfaces that stimulate and guide the activity of adhering cells. The resulting nanoporosity is also attractive because it offers nanoconfined volumes that can be exploited to load bioactive compounds and modulate their release over time. Such extended elution is needed since a single exposure to growth factors and/or antibiotics, for instance, may not be adequate to further sustain bone regeneration and/or to counteract bacterial colonization. In this article, we assessed the capacities of nanoporous titanium surfaces generated by oxidative nanopatterning to provide controlled and sustained elution of proteins and antibiotic molecules. To this end, we have selected bovine serum albumin (BSA) and vancomycin to reflect commonly used compounds, and investigated their adsorption and elution by Fourier-transform infrared (FT-IR) and ultraviolet-visible (UV-VIS) spectroscopy. Our results demonstrate that while the elution of albumin is not significantly affected by the nanoporosity, in the case of vancomycin, nanoporous surfaces provided an extended release. These findings were successively correlated to the establishment of interactions with the surface and physical-entrapment effects exerted by the nanopores, ultimately highlighting their synergistic contribution to the release profiles and thus their importance in the design of nanostructured eluting platforms for applications in medicine.
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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 | 0.001 | 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".