Employing Polymer and Gel to Fabricate Scaffold-like Cancellous Orthopedic Screw: Polycaprolactone/Chitosan/Hydroxyapatite
Bibliographic record
Abstract
Using metallic/polymeric orthopedic screws causes cavities in bone trauma after the attachment of broken bones, which prolongs the healing. Yet, it remains unknown how to overcome such a challenge. The main aim of this research was to use both polymers and gels to fabricate and study a new PCL/chitosan/hydroxyapatite scaffold-like orthopedic screw for cancellous bone trauma. This screw, because of its low stiffness and its scaffold-based matrix (due to the gel part), can facilitate bone healing. Different concentrations of PCL (60–95% w/v) and chitosan (0–5% w/v) were blended according to the Response Surface Methodology using the Central Composite Design. The screws were fabricated using the freeze-drying technique. The screws were assessed mechanically, physically, and biologically (cell viability, cell attachment, DAPI, ALP staining, and Alizarin Red staining), and in vivo (a rat subcutaneous implantation model). Based on the results, screws depending on the PCL and gel content depicted different but notable mechanical behavior (10–60 MPa of compressive strength and 100–600 N force). The gel part could affect the physical properties of screws including water uptake (120%), degradation (18% after 21 days), porosities (23%), and mechanical strength (elastic modulus = 59.47 Mpa). The results also demonstrated no cytotoxicity towards MC3T3 cells (>80% cell viability) with good cell attachment, cell concentration, and mineralization (>90%) that was justified by the gel content. The results also showed good in vivo biocompatibility. To sum up, fabricated scaffold-like screws with gel content can be a good candidate for cancellous-bone-based orthopedic purposes. However, more in vitro and in vivo studies are required to optimize the PCL:gel ratio.
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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) | 0.000 | 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".