Pioneering a New Era in Oral Cancer Treatment with Electrospun Nanofibers: A Comprehensive Insight
Bibliographic record
Abstract
Oral cancer, currently ranked 16th among the most prevalent malignancies worldwide according to GLOBOCAN, presents significant challenges to global oral health. Conventional treatment modalities such as surgery, radiation, and chemotherapy often have limitations, prompting the need for innovative therapeutic approaches. Tissue engineering has emerged as a promising solution aimed at developing biocompatible, functional, and biologically responsive tissue constructs. This approach involves the integration of cells, bioactive compounds, and scaffolds to enhance treatment efficacy. Electrospun nanofibers, mimicking the extracellular matrix, exhibit considerable potential in addressing complex oral health issues by influencing cellular behavior. The versatility of electrospinning technology allows for the fabrication of fiber scaffolds with high surface area, making them ideal for localized delivery of bioactive compounds or pharmaceuticals. Enhancing these electrospun scaffolds with growth factors, nanoparticles, and biologically active substances significantly increases their therapeutic appeal in oral cancer management. This review offers a comprehensive examination of the various applications of electrospun nanofibers in oral cancer therapy. Utilizing electronic databases such as PubMed, CrossREF, and Google Scholar, we conducted an extensive review of relevant literature concerning "electrospun nanofibers" and their therapeutic potential in oral cancer treatment. Key topics addressed include engineering methodologies, drug diffusion mechanisms, factors influencing nanofiber scaffold design, toxicity concerns, and clinical implications. The findings underscore the transformative potential of electrospun nanofibers in revolutionizing oral cancer therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".