Characterization of keratin–collagen 3D scaffold for biomedical applications
Bibliographic record
Abstract
Abstract Fabrication of keratin–collagen (KC) 3D scaffold with improved thermal denaturation rate is reported. In vitro application of (KC) scaffold stimulates basic extra cellular matrix constituents. KC Scaffold considerably reduced undesirable properties of both collagen and keratin while collagen incorporation reduces the fragility with increases of strength and flexibility in the scaffold. In addition to this, the scaffold showed homogenous well‐interconnected pores in the range of 10–100 µm when observed in scanning electron microscope. Usage of keratin in KC scaffold offers increased biodegradation rate and higher denaturation rate in addition to its rapid cell growth with normal morphology ultimately reaching cell population of 3.9–9.7 million per cm3 after 48 hr in KC scaffold. Circular dichroism (CD) and Fourier transform spectroscopy (FT‐IR) of KC showed presence of helical structure of collagen and ß‐turns of keratin confirming retention of native structures of both the proteins KC scaffold showed good swelling behavior and water uptake. Our study strongly supports the superidity of KC scaffold over the collagen or keratin when they are independently used for tissue engineering applications. Copyright © 2011 John Wiley & Sons, Ltd.
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
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".