Biomaterial strategies to combat implant infections: new perspectives to old challenges
Bibliographic record
Abstract
Peri-implant infection is rapidly becoming an – if not the most – important clinical challenge for indwelling medical devices. To alleviate the global rise in antibiotic use for the treatment of such infections, a plethora of biomaterials/bioengineering-based antimicrobial strategies are emerging to restrict or ideally to eliminate microbial adhesion and biofilm formation on implant surfaces. Yet, the development of such approaches faces specific challenges, like biocompatibility concerns, reduced antimicrobial effectiveness, long-term stability issues and antibiotic resistance development, which limit translation to the clinic. This review provides insights into the antimicrobial activity of current state-of-the-art biomaterial-based approaches to address the aforementioned issues. Translational research strategies and regulatory framework are also emphasised as key elements facilitating clinical implementation of anti-infective biomaterials. This review closes with the vision that the integration of computational tools and experimental databases using artificial intelligence (AI) would provide new insights for the accelerated development of next-generation biomaterial-based antimicrobial strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".