Biomimetic PLGA/Strontium-Zinc Nano Hydroxyapatite Composite Scaffolds for Bone Regeneration
Bibliographic record
Abstract
Synthetic bone graft substitutes have attracted increasing attention in tissue engineering. This study aimed to fabricate a novel, bioactive, porous scaffold that can be used as a bone substitute. Strontium and zinc doped nano-hydroxyapatite (Sr/Zn n-HAp) were synthesized by a water-based sol-gel technique. Sr/Zn n-HAp and poly (lactide-co-glycolide) (PLGA) were used to fabricate composite scaffolds by supercritical carbon dioxide technique. FTIR, XRD, TEM, SEM, and TGA were used to characterize Sr/Zn n-HAp and the composite scaffolds. The synthesized scaffolds were adequately porous with an average pore size range between 189 to 406 µm. The scaffolds demonstrated bioactive behavior by forming crystals when immersed in the simulated body fluid. The scaffolds after immersing in Tris/HCl buffer increased the pH value of the medium, establishing their favorable biodegradable behavior. ICP-MS study for the scaffolds detected the presence of Sr, Ca, and Zn ions in the SBF within the first week, which would augment osseointegration if implanted in the body. nHAp and their composites (PLGA-nHAp) showed ultimate compressive strength ranging between 0.4-19.8 MPa. A 2.5% Sr/Zn substituted nHAp-PLGA composite showed a compressive behavior resembling that of cancellous bone indicating it as a good candidate for cancellous bone substitute.
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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) | 0.001 | 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".