Dehydropeptide-based plasmonic magnetogels: a supramolecular composite nanosystem for multimodal cancer therapy
Bibliographic record
Abstract
Supramolecular hydrogels are highly promising candidates as biomedical materials owing to their wide array of properties, which can be tailored and modulated. Additionally, their combination with plasmonic/magnetic nanoparticles to form plasmonic magnetogels further improves their potential in biomedical applications through the combination of complementary strategies, such as photothermia, magnetic hyperthermia, photodynamic therapy and magnetic-guided drug delivery. Here, a new dehydropeptide hydrogelator, Npx-l-Met-Z-ΔPhe-OH, was developed and combined with two different plasmonic/magnetic nanoparticle architectures, i.e., core/shell manganese ferrite/gold nanoparticles and gold-decorated manganese ferrite nanoparticles with ca. 55 nm and 45 nm sizes, respectively. The magnetogels were characterized via HR-TEM, FTIR spectroscopy, circular dichroism and rheological assays. The gels were tested as nanocarriers for a model antitumor drug, the natural compound curcumin. The incorporation of the drug in the magnetogel matrices was confirmed through fluorescence-based techniques (FRET, fluorescence anisotropy and quenching). The curcumin release profiles were studied with and without the excitation of the gold plasmon band. The transport of curcumin from the magnetogels towards biomembrane models (small unilamellar vesicles) was assessed via FRET between the fluorescent drug and the lipid probe Nile Red. The developed magnetogels showed promising results for photothermia and photo-triggered drug release. The magnetogels bearing gold-decorated nanoparticles showed the best photothermia properties, while the ones containing core/shell nanoparticles had the best photoinduced curcumin release.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".