Improving Biocompatibility of Implantable Metals by Nanoscale Modification of Surfaces: An Overview of Strategies, Fabrication Methods, and Challenges
Bibliographic record
Abstract
The human body is an intricate biochemical-mechanical system, with an exceedingly precise hierarchical organization in which all components work together in harmony across a wide range of dimensions. Many fundamental biological processes take place at surfaces and interfaces (e.g., cell-matrix interactions), and these occur on the nanoscale. For this reason, current health-related research is actively following a biomimetic approach in learning how to create new biocompatible materials with nanostructured features. The ultimate aim is to reproduce and enhance the natural nanoscale elements present in the human body and to thereby develop new materials with improved biological activities. Progress in this area requires a multidisciplinary effort at the interface of biology, physics, and chemistry. In this Review, the major techniques that have been adopted to yield novel nanostructured versions of familiar biomaterials, focusing particularly on metals, are presented and the way in which nanometric surface cues can beneficially guide biological processes, exerting influence on cellular behavior, is illustrated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".