Electrospun Carboxymethylcellulose as a Scaffold for Biomedical Applications
Bibliographic record
Abstract
Abstract Electrospinning of pure carboxymethylcellulose (CMC) and its derivatives for biomedical applications is attractive due to their interesting biology and biomimetic properties. However, the main challenges in electrospinning pure CMC are strong electrostatic repulsions and its highly viscous nature. In this research, electrospun membranes consisting of grafted CMC-polyethylene glycol (CMC-PEG) and polycaprolactone (PCL) were successfully fabricated using emulsion electrospinning. Membranes with a PCL:CMC-PEG ratio of 80:20 formed uniform fiber with an average diameter of 930.2 ± 31.0 nm. Furthermore, PCL/CMC-PEG membranes demonstrated excellent mechanical properties suitable for use as scaffolds for soft tissue repair and skin wound healing. Water contact angle analysis showed that the incorporation of grafted CMC-PEG improved the membrane wettability. Electrospun membranes with a PCL: CMC-PEG ratio of 80:20 exhibited the highest in vitro degradation, with 82.0 ± 8.7% weight loss over 10 weeks of incubation. In vitro studies confirmed the non-cytotoxic properties of PCL:CMC-PEG (80:20) membranes when tested with normal human dermal fibroblast (NHDF) cells. Morphological analysis further confirmed the attachment of NHDF cells followed by cell proliferation and migration. These membranes demonstrated optimal mechanical properties, hydrophilicity, and biocompatibility, making them promising tissue scaffolds for tissue engineering and regenerative medicine applications. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".