Osteoregeneration of Critical-Size Defects Using Hydroxyapatite–Chitosan and Silver–Chitosan Nanocomposites
Bibliographic record
Abstract
Bone is a natural nanocomposite composed of proteins and minerals that can regenerate itself. However, there are conditions in which this process is impaired, such as extensive bone defects and infections of the bone or surrounding tissue. This study evaluates the osteoregenerative capacity of bone grafting materials in animals with induced bone defects. Colloidal chitosan dispersion nanocomposites, nanohydroxyapatite−chitosan (NHAP-Q) and nanosilver−chitosan (AgNP-Q), were synthesized and characterized. Non-critical-size defects in Wistar rats were used to evaluate the material’s biocompatibility, and critical-size defects in the calvarias of guinea pigs were used to evaluate the regenerative capacity of the bones. Moreover, the toxicity of the nanocomposites was evaluated in the heart, liver, spleen, kidneys, and skin. Histological, radiographic, and electron microscopy tests were also performed. The results showed that neither material produced pathological changes. Radiographic examination showed a significant reduction in defects (75.1% for NHAP-Q and 79.3% for AgNP-Q), angiogenesis, and trabecular formation. A toxicological assessment of all the organs did not show changes in the ultrastructure of tissues, and the distribution of silver was different for different organs (spleen > skin > heart > kidney > liver). The results suggest that both materials are highly biocompatible, and AgNP-Q achieved similar bone regeneration to that reported with autologous bone. The main research outcome of the present study was the combination of two types of NPs to enhance antimicrobial and osteoregeneration activities. These colloidal chitosan dispersions show promise as future biomaterials in the medical field for applications in fast-healing fractures, including broken bones in the oral cavity and hip replacement 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".