Mechanobiological enhancement of electrospun PCL/nHA membranes for guided tissue regeneration applications
Bibliographic record
Abstract
This study aims to investigate the effects of adding nano-hydroxyapatite (nHA) to electrospun polycaprolactone (PCL) membranes for use in dental root regeneration. Porous membranes containing varying amounts of nHA (0, 1, 1.5, and 2.5 wt%) were fabricated using the electrospinning method. The physicochemical, mechanical, and biological properties of the membranes were evaluated. The synthesized nHA particles had an average size of 52 nm. Electrospun membranes exhibited uniform fibrous morphology with porosities ranging from 56% to 86%. Cyclic thermal stress (5°C–50°C) improved the mechanical properties of the composite membranes, resulting in a decrease in ultimate tensile strength (UTS) for pristine PCL from 3 ± 0.12 MPa to 1.7 ± 0.11 MPa, while the UTS for PCL membranes containing 1.5% nHA increased from 3.3 ± 0.30 MPa to 4.18 ± 0.28 MPa. In vitro bioactivity in simulated body fluid (SBF) showed enhanced apatite formation, particularly after 21 and 28 days. Cytotoxicity assays with MG-63 osteoblast-like cells demonstrated good biological performance. The incorporation of nHA not only improved the mechanical properties but also enhanced the bioactivity and cytocompatibility of the electrospun PCL membranes, making them promising candidates for guided tissue regeneration (GTR) 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 | 0.000 | 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".