Macroporous scaffolds based on biomass polymers and their applications in wound healing
Bibliographic record
Abstract
The rapid advancement of biomedical polymers has raised significant concerns about the disposal of medical polymer waste. Sustainable biomass materials derived from renewable sources in nature have emerged as promising alternatives to petroleum-based polymers for medical applications and tissue engineering due to their abundance, biodegradability, and environmental friendliness. In tissue engineering, interconnected macropores within biomaterials are crucial as they provide space and interfaces for cells, enhancing permeability for nutrient and waste transport. In this review, we summarize recent developments in the use of biomass materials to engineer macroporous tissue engineering scaffolds. We highlight key techniques, such as microparticles assembly, leaching template, and bioprinting that can create macropores within scaffolds composed of biomass materials and their composites. In addition, we investigate the applications of the macroporous scaffolds in wound healing, with a focus on cell behaviors within macroporous constructs and their role in treating chronic wounds. We envision that the combination of the bicontinuous macropores and biomass-based materials can create an ideal cellular environment and provide a powerful platform for wound healing and tissue regeneration.
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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".