Recent Advances in pH-Responsive Coatings for Orthopedic and Dental Implants: Tackling Infection and Inflammation and Enhancing Bone Regeneration
Bibliographic record
Abstract
Bone is a structurally complex and dynamic tissue that plays a crucial role in mobility and skeletal stability. However, conditions such as osteoporosis, osteoarthritis, trauma-induced fractures, infections, and malignancies often necessitate the use of orthopedic and dental implants. Despite significant progress in implant biomaterials, challenges such as bacterial infection, inflammation, and loosening continue to compromise implant longevity, frequently leading to revision surgeries and extended recovery times. Smart coatings have emerged as a next-generation solution to these problems by providing on-demand, localized therapeutic responses to microenvironmental changes around implants and promoting bone regeneration. Such coatings can minimize antibiotic resistance by enabling controlled, stimulus-triggered drug release. Although the idea of using pH-sensitivity as a tool to make smart coatings is not a new thought, there are no options currently good enough to enter clinical studies. This review provides a comprehensive overview of recent advances in pH-sensitive polymers, hybrid composites, porous architectures, and bioactive linkers designed to dynamically respond to pathological pH variations at implant sites. By investigating the mechanisms of action, antibacterial and anti-inflammatory effects, and roles in bone regeneration, it is shown that the ability to provide time-dependent drug release for both short-term and long-term infections, as well as keeping the environment welcoming to the bone cell growth and replacement, is not an easy goal to reach, even with a fully biocompatable, non-toxic, and semi-biodegradable (one that releases the drug, but does not fade away) coating material compound. Reviewing all available options, including their functions and failures, finally, emerging trends, translational barriers, and future opportunities for clinical implementation are highlighted, underscoring the transformative potential of bioresponsive coatings in orthopedic and dental implant technologies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".