Nanoscale surface modifications of medically relevant metals: state-of-the art and perspectives
Bibliographic record
Abstract
Evidence that nanoscale surface properties stimulate and guide various molecular and biological processes at the implant/tissue interface is fostering a new trend in designing implantable metals. Cutting-edge expertise and techniques drawn from widely separated fields, such as nanotechnology, materials engineering and biology, have been advantageously exploited to nanoengineer surfaces in ways that control and direct these processes in predictable manners. In this review, we present and discuss the state-of-the-art of nanotechnology-based approaches currently adopted to modify the surface of metals used for orthopedic and dental applications, and also briefly consider their use in the cardiovascular field. The effects of nanoengineered surfaces on various in vitro molecular and cellular events are firstly discussed. This review also provides an overview of in vivo and clinical studies with nanostructured metallic implants, and addresses the potential influence of nanotopography on biomechanical events at interfaces. Ultimately, the objective of this work is to give the readership a comprehensive picture of the current advances, future developments and challenges in the application of the infinitesimally small to biomedical surface science. We believe that an integrated understanding of the in vitro and particularly of the in vivo behavior is mandatory for the proper exploitation of nanostructured implantable metals and, indeed, of all biomaterials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".