Fine‐Tuning of Hydrophilic Properties of Asymmetrically Porous Poly(ε‐Caprolactone)‐Based Nanofibrous Scaffolds Containing Dexamethasone for Bone Tissue Engineering Applications
Bibliographic record
Abstract
ABSTRACT Bone abnormalities and injuries provide serious medical issues. Bone has the ability for regeneration; however, its regenerative potential is limited. Tissue engineering has gained significant attention as a potential treatment for bone abnormalities. In this study, poly‐caprolactone (PCL)‐based nanofibers containing various concentrations of poly‐ethyl‐2‐oxazoline (PEtOx) and loaded with Dexamethasone (Dex) were prepared and evaluated as multifunctional bioscaffolds for bone regeneration. Various techniques were employed to characterize the feature of the electrospun scaffolds including 1 H‐NMR, Fourier transform infrared spectroscopy (FT‐IR), scanning electron microscopy (SEM), and gel permission chromatography (GPC). The swelling degree, mechanical property, degradation behavior, and drug release profile were also evaluated. The cell viability of the electrospun nanofibers on human adipose tissue‐derived mesenchymal stem cells (hAMSCs) were examined by MTT, and osteogenic differentiation potency was studied by alkaline phosphatase (ALP) activity, and calcium deposition assessments. According to the findings, a higher PEtOx concentration in the polymer solution reduced the nanofiber diameter while increasing the swelling rate, mass loss amount, and Young's modulus of the produced scaffolds. The release profile of Dex from the electrospun scaffold influenced osteogenic differentiation in stem cells. The scaffold revealed promising features that could be employed for further bone injury studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".