Hydroentangled nonwoven eri silk fibroin scaffold for tissue engineering applications
Bibliographic record
Abstract
Electrospun scaffolds are being widely studied for its potential application in tissue engineering because of its nanostructure that mimics the extracellular matrices. Although it has several advantages, it lacks mechanical strength and causes structural deformation during handling of the scaffold. It is well known that the textile-based structures like woven and nonwoven fabrics have excellent structural stability. In this study, a woven and a hydroentangled nonwoven fabric were fabricated from eri silk fibroin and their characteristics were compared with electrospun scaffold. The functional groups, contact angle, thermal degradation, hemocompatibility studies showed that all the three scaffolds can be used as biomaterials. The pore and pore size distribution were better with electrospun scaffold due to smaller fiber diameter and more number of layers of fibers. The tensile behaviour was found to be better for woven and nonwoven scaffolds. The enzymatic degradation showed that the stability of woven and nonwoven scaffolds were better and electrospun scaffold degrades and disintegrates quickly. Mouse 3T3 L1 fibroblast and Human Wharton’s jelly Mesenchymal Stem Cells adhered on all the three scaffolds and a higher attachment and growth coverage were obtained on the electrospun and nonwoven scaffolds. It was found that the hydroentangled nonwoven silk scaffold exhibited similar biological characteristics as that of electrospun scaffold and also had higher mechanical strength and structural stability. Hence, it is inferred that the hydroentangled nonwoven scaffold can be considered as a suitable structure for tissue engineering applications.
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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) | 0.001 | 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 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".