Iconografía de San Isidro Labrador y Santa María de la Cabeza
Bibliographic record
Abstract
Appropriate selection of suitable materials and methods is essential for scaffolds fabrication in tissue engineering. The major challenge is to mimic the structure and functions of the extracellular matrix (ECM) of the native tissues. In this study, an optimized 3D structure containing poly(3-hydroxybutyrate) (P3HB), multiwalled carbon nanotubes (MCNTs) and curcumin (CUR) was created by electrospinning a novel biomimetic scaffold. CUR, a natural anti-inflammatory compound, has been selected as a bioactive component to increase the biocompatibility and reduce the potential inflammatory reaction of electrospun scaffolds. The presence of CUR in electrospun scaffolds was confirmed by <sup>1</sup>H NMR and Fourier-transform infrared spectroscopy (FTIR). Scanning electron microscopy (SEM) revealed highly interconnected porosity of the obtained 3D structures. Addition of up to 20 wt% CUR has enhanced mechanical properties of the scaffolds. CUR has also promoted in vitro bioactivity and hydrolytic degradation of the electrospun nanofibers. The developed P3HB-MCNT composite scaffolds containing 20 wt% of CUR revealed excellent in vitro cytocompatibility using mesenchymal stem cells and in vivo biocompatibility in rat animal model study. Importantly, the reduced inflammatory reaction in the rat model after 8 weeks of implantation has also been observed for scaffolds modified with CUR. Overall, newly developed P3HB-MCNTs-CUR electrospun scaffolds have demonstrated their high potential for tissue engineering applications.
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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 | 0.000 | 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 | 0.001 | 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 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".