Prosthetic Joint Infections: Biofilm Formation, Management, and the Potential of Mesoporous Bioactive Glass as a New Treatment Option
Bibliographic record
Abstract
Infection of prosthetic joints is one of the biggest challenges to a successful replacement of the joint after a total joint arthroplasty. Such infections are caused by bacterial colonies that are difficult to treat by systemic delivery of antibiotics. Local delivery of antibiotics can prove to be the solution to such a devastating outcome that impacts patients' health and ability to regain function in their joints as well as costs the healthcare system millions of dollars every year. This review will discuss prosthetic joint infections in detail with a focus on the development, management, and diagnosis of the infections. Surgeons often opt to use polymethacrylate cement locally to deliver antibiotics; however, due to the rapid release of antibiotics, non-biodegradability, and high chance of reinfection, the search for alternatives is in high demand. One of the most researched alternatives to current treatments is the use of biodegradable and highly compatible bioactive glass. The novelty of this review lies in its focus on mesoporous bioactive glass as a potential alternative to current treatments for prosthetic joint infection. Mesoporous bioactive glass is the focus of this review because it has a higher capacity to deliver biomolecules, stimulate bone growth, and treat infections after prosthetic joint replacement surgeries. The review also examines different synthesis methods, compositions, and properties of mesoporous bioactive glass, highlighting its potential as a biomaterial for the treatment of joint infections.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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".