Structure and properties of slow-resorbing nanofibers obtained by (co-axial) electrospinning as tissue scaffolds in regenerative medicine
Bibliographic record
Abstract
With the rapid advancement of regenerative medicine technologies, there is an urgent need for the development of new, cell-friendly techniques for obtaining nanofibers—the raw material for an artificial extracellular matrix production. We investigated the structure and properties of PCL 10 nanofibers, PCL 5 /PCL 10 core-shell type nanofibers, as well as PCL 5 /PCL Ag nanofibres prepared by electrospinning. For the production of the fiber variants, a 5–10% solution of polycaprolactone (PCL) (M w = 70,000–90,000), dissolved in a mixture of formic acid and acetic acid at a ratio of 70:30 m/m was used. In order to obtain fibers containing PCL Ag 1% of silver nanoparticles was added. The electrospin was conducted using the above-described solutions at the electrostatic field. The subsequent bio-analysis shows that synthesis of core-shell nanofibers PCL 5 /PCL 10 , and the silver-doped variant nanofiber core shell PCL 5 /PCL Ag , by using organic acids as solvents, is a robust technique. Furthermore, the incorporation of silver nanoparticles into PCL 5 /PCL Ag makes such nanofibers toxic to model microbes without compromising its biocompatibility. Nanofibers obtained such way may then be used in regenerative medicine, for the preparation of extracellular scaffolds: (i) for controlled bone regeneration due to the long decay time of the PCL, (ii) as bioscaffolds for generation of other types of artificial tissues, (iii) and as carriers of nanocapsules for local drug delivery. Furthermore, the used solvents are significantly less toxic than the solvents for polycaprolactone currently commonly used in electrospin, like for example chloroform (CHCl 3 ), methanol (CH 3 OH), dimethylformamide (C 3 H 7 NO) or tetrahydrofuran (C 4 H 8 O), hence the presented here electrospin technique may allow for the production of multilayer nanofibres more suitable for the use in medical field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".