Tuning the Biointerface: Low-Temperature Surface Modification Strategies for Orthopedic Implants to Enhance Osteogenic and Antimicrobial Activity
Bibliographic record
Abstract
As both the average life expectancy and incidence of bone tissue reconstruction increases, development of load-bearing implantable materials that simultaneously enhance osseointegration while preventing postoperative infection is crucial. To address this need, significant research efforts have been dedicated to developing surface modification strategies for metallic load-bearing implants and scaffolds. Despite the abundance of strategies reported, many address only one factor, for example, surface chemistry or topography. Furthermore, the incorporation of surface features to increase osteocompatibility can increase the probability of infection, by encouraging the formation of bacterial biofilms. To truly advance this field, research efforts must focus on developing multifunctional coatings that concurrently address these complex and competing requirements. In addition, particular emphasis should be placed on utilizing surface modification processes that are versatile, low cost, and scalable, for ease of translation to mass manufacturing and clinical use. The aim of this short Review is to highlight recent advances in scalable and multifunctional surface modification techniques that obtain a programmed response at the bone tissue/implant interface. Low-temperature approaches based on macromolecule immobilization, electrochemical techniques, and solution processes are discussed. Although the strategies discussed in this Review have not yet been approved for clinical use, they show great promise toward developing the next generation of ultra-long-lasting biomaterials for joint and bone tissue repair.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".