Magnetically Guided Fabrication of Multilayered Iron Oxide/Polycaprolactone/Gelatin Nanofibrous Structures for Tissue Engineering and Theranostic Application
Bibliographic record
Abstract
A persistent challenge in tissue engineering is the fabrication of manipulatable scaffolds for implantation in clinical treatments and use in disease models for drug screening. Electrospinning of nanofibrous membranes is an emerging technology in artificial extracellular matrix (ECM) design that can offer precisely tunable microenvironments upon assembly into three-dimensional (3D) scaffolds that mimic the in vivo ECM structure. In this study, we report a facile and versatile strategy for preparing 3D multilayered constructs from Fe3O4/polycaprolactone (PCL)/gelatin nanofibrous membranes. This method combines membrane assembly with noncontact magnetic force to preserve the mechanical integrity and interconnectivity of the 3D scaffolds. An ordered layer structure can be achieved using a magnetic control technique through the addition of magnetic nanoparticles into the PCL/gelatin nanofibers. We first verified the magnetic properties and structures of magnetic nanofibers according to X-ray diffraction, hysteresis, scanning electron microscopy, and transmission electron microscopy. We tested the potential toxicity and osteogenic differentiation of mesenchymal stem cells seeded on the layered scaffolds. To add further functionality to the scaffolds, the membranes were coated with silver nanoparticles and shown to inhibit the growth of Escherichia coli and Staphylococcus aureus, which are responsible for most cases of infection-related implant failure. Finally, we tested the utility of magnetic membranes implanted in an animal model as a contrast agent for magnetic resonance imaging. Scaffolds formed using the presented magnetically guided fabrication strategy have the potential to mimic the structure and function of human tissues and also may be applied in disease models to study cell-cell interactions.
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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".