A novel collagen/hydroxyapatite/poly(lactide‐<i>co</i>‐ε‐caprolactone) biodegradable and bioactive 3D porous scaffold for bone regeneration
Bibliographic record
Abstract
The goal of this study was to design a nontoxic scaffold with both composition and microstructure suitable for bone engineering using collagen (Coll), hydroxyapatite (HA), and poly(lactide-co-ε-caprolactone) (PLCL). Mineralized type I Coll was produced by direct nucleation of HA particles inside self-assembled Coll fibers to obtain a Coll/HA complex, which was then added to dissolved PLCL (70:30) in 1,4-dioxane. A 3D porous Coll/HA/PLCL scaffold was subsequently produced through freeze-drying/lyophilization and salt-leaching procedures. The resulting Coll/HA/PLCL scaffold displayed a high uniform porosity and highly interconnected pores. X-ray photoelectron spectrometer and Fourier transform infrared analyses revealed the presence of both collagen and HA particles on the surface of the Coll/HA/PLCL scaffold. Proliferation assay, microscopic observations, and gene analysis with quantitative RT-PCR showed that osteoblast cells were able to attach, proliferate, and maintain an osteoblastlike phenotype when cultured on the Coll/HA/PLCL scaffold. In summary, we produced a nontoxic scaffold that contains natural polymers (Coll and HA) and synthetic polymer (PLCL). Through its chemical composition and porous morphology, this scaffold may be useful for osteoblast growth, differentiation, and bone tissue formation.
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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".