Accelerated cascade melanoma therapy using enzyme-nanozyme-integrated dissolvable polymeric microneedles
Bibliographic record
Abstract
Dissolvable polymeric microneedles (DPMNs) have emerged as a powerful technology for the localized treatment of diseases, such as melanoma. Herein, we fabricated a DPMN patch containing a potent enzyme-nanozyme composite that transforms the upregulated glucose consumption of cancerous cells into lethal reactive oxygen species via a cascade reaction accelerated by endogenous chloride ions and external near-infrared (NIR) irradiation. This was accomplished by combining glucose oxidase (Gox) with a NIR-responsive chloroperoxidase-like copper sulfide (CuS) nanozyme. In contrast with subcutaneous injection, the microneedle system highly localizes the treatment, enhancing nanomedicine uptake by the tumor and reducing its systemic exposure to the kidneys and spleen. NIR irradiation further controls the potency and toxicity of the formulation by thermally disabling Gox. In a mouse melanoma model, this unique combination of photothermal, starvation, and chemodynamic therapies resulted in complete tumor eradication (99.2 ± 0.8 % reduction in tumor volume within 10 d) without producing signs of systemic toxicity. By comparison, other treatment combinations only resulted in a 42-76.5 % reduction in tumor growth. The microneedle patch design is therefore not only highly potent but also with regulated toxicity and improved safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".