Bibliographic record
Abstract
Abstract Nanofiber technology is an exciting area attracting the attention of many researchers as a potential solution to the current challenges in the biomedical field such as burn and wound care, organ repair, and treatment for osteoporosis and various diseases. Nanofibers are attractive in this field for several reasons. First, surface area on nanofibers is much higher compared to bulk materials, which allows for enhanced adhesion of cells, proteins, and drugs. Second, nanofibers can be fabricated into sophisticated macro‐scale structures. The ability to fabricate nanofibers allows renewed efforts in developing hierarchical structures that mimic those in animals and human. On top of that, a wide range of polymers can be fabricated into nanofibers to suit different applications. Nanofibers are most commonly fabricated through electrospinning, which is a low cost method that allows control over fiber morphology and is capable of being scaled‐up for mass production. This review explored two popular areas of biomedical nanofiber development: tissue regeneration and drug delivery, and included discussions on the basic principles for how nanofibers promote tissue regeneration and drug delivery, the parameters that affect nanofiber performance and the recent progress in these areas. The recent work on biomedical nanofibers showed that the large surface area on nanofibers could be translated into enhanced cell activities, drug encapsulation, and drug release rate control. Furthermore, by optimizing the electrospinning process via adjusting the material choices and fiber orientation, for example, further enhancement in cell differentiation and drug release control could be achieved. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".