The Role of Ion-Doped Hydroxyapatite in Drug Delivery, Tissue Engineering, Wound Healing, Implants, and Imaging
Bibliographic record
Abstract
The ion doping of hydroxyapatite (HA) has gained appeal as a chemical method of improving and adding new characteristics to materials used in biomedical engineering. Dimension, morphology, porosity, surface charge, topology, composition, and other material characteristics make doped HA more suitable for specific biomedical applications. The main aim of this review study was to highlight the role of iHA (iHA) in developing drug delivery systems, tissue engineering, implant coating, wound healing, and multimodal imaging. To the best of our knowledge, depending on the dopant, iHA can have inherent distinct mechanical, physicochemical, and biological properties that make it eligible for biomedical application. More importantly, some ions make iHA a potent antibacterial agent and drug carrier for wound healing (e.g., silver, copper, zinc), have tissue engineering capabilities, improved proangiogenic and osteoconductive properties (e.g., strontium, cobalt, nickel), drug loading capacity (e.g., magnesium, ferric, strontium), metallic implant coating properties (e.g., manganese, silver, copper), and multimodal imaging potential (e.g., terbium, ytterbium, cerium). The concentration of ions and the number of dopants played a vital role in developing new approaches based on iHA. In conclusion, iHA, compared to HA, could show better improvements in biomedical applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".