Catechin as a functional additive in electrospun <scp>PCL</scp>/gelatin/<scp>nHA</scp> nanocomposite fibers for tissue engineering applications
Bibliographic record
Abstract
Abstract The promise of polymeric nanocomposite fibers in the biomedical field is well‐documented due to their adjustable properties and versatility. Electrospun fiber mats, which mimic the extracellular matrix, are particularly noted for their potential in tissue regeneration and repair. In this study, we investigate the role of catechin, a flavonoid, in enhancing cellular response to electrospun nanocomposite fibers of polycaprolactone (PCL), gelatin, and nanohydroxyapatite (nHA). Nanocomposite fibers were fabricated with varying concentrations of catechin and comprehensive analyses of the composite fibers were performed to evaluate their structural and chemical properties. In vitro, assays were performed to examine cell viability and ascertain the influence of catechin on cellular responses to these nanocomposite fibers. Simultaneously, in vivo studies assessed tissue responses to these materials. The findings revealed that incorporating catechin into PCL/gelatin/nHA nanocomposite fibers improved cellular responses in vitro as demonstrated by enhanced cell viability. Furthermore, in vivo investigations displayed positive tissue responses with these fibers, indicating their capacity to enhance cell growth and accelerate tissue regeneration. This study elucidates the potential of catechin as an integral component in designing polymeric nanocomposite fibers, potentially broadening the scope of their biomedical 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".