Nanoparticle-Based Treatment Approaches for Skin Cancer: A Systematic Review
Bibliographic record
Abstract
Nanoparticles have shown marked promise as both antineoplastic agents and drug carriers. Despite strides made in immunomodulation, low success rates and toxicity remain limitations within the clinical oncology setting. In the present review, we assess advances in drug delivery nanoparticles, for systemic and topical use, in skin cancer treatment. A systematic review of controlled trials, meta-analyses, and Cochrane review articles was conducted. Eligibility criteria included: (1) a primary focus on nanoparticle utility for skin cancer; (2) available metrics on prevention and treatment outcomes; (3) detailed subject population; (4) English language; (5) archived as full-text journal articles. A total of 43 articles were selected for review. Qualitative analysis revealed that nanoscale systems demonstrate significant antineoplastic and anti-metastasis properties: increased drug bioavailability, reduced toxicity, enhanced permeability and retention effect, as well as tumor growth inhibition, among others. Nanoformulations for skin cancers have largely lagged behind those tested in other cancers-several of which have commercialized formulae. However, emerging evidence has indicated a powerful role for these carriers in targeting primary and metastatic skin cancers.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".