Potential of Polymeric Films Loaded with Gold Nanorods for Local Hyperthermia Applications
Bibliographic record
Abstract
Current strategies for the treatment of superficial non-melanoma skin cancer (NMSC) lesions include topical imoquimod, 5-fluorouracil, and photodynamic therapy. Although these treatments are effective, burning pain, blistering, and dermatitis have been reported as frequent side effects, making these therapies far from ideal. Plasmonic materials have been investigated for the induction of hyperthermia and use in cancer treatment. In this sense, the effectiveness of intratumorally and systemically injected gold nanorods (GnRs) in inducing cancer cell death upon near-infrared light irradiation has been confirmed. However, the in vivo long-term toxicity of these particles has not yet been fully documented. In the present manuscript, GnRs were included in a crosslinked polymeric film, evaluating their mechanical, swelling, and adhesion properties; moreover, their ability to heat up neonatal porcine skin (such as a skin model) upon irradiation was tested. Inclusion of GnRs into the films did not affect mechanical or swelling properties. GnRs were not released after film swelling, as they remained entrapped in the polymeric network; moreover, films did not adhere to porcine skin, altogether showing the enhanced biocompatibility of the material. GnR-loaded films were able to heat up the skin model over 40 °C, confirming the potential of this system for non-invasive local hyperthermia applications.
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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".