Supramolecular nanomedicine for selective cancer therapy <i>via</i> sequential responsiveness to reactive oxygen species and glutathione
Bibliographic record
Abstract
Cancer cells are generally immersed in an oxidative stress environment with a high intracellular reduction level. Thus, nanocarriers with sequential responsiveness to oxidative and reductive species, matching the traits of high oxidation in the tumor tissue microenvironment and high reduction potential inside cancer cells, are highly desired for specific cancer therapy. Herein, we report a supramolecular nanomedicine comprised of a reduction-responsive nanoparticle (NP) core whose surface was modified by an oxidation-responsive polyethylene glycol (PEG) derivative via strong host-guest interactions. In this delicate design, the PEGylation of NPs not only reduced their immunogenicity and extended systemic circulation, but also enabled oxidation-responsive de-PEGylation in the tumor tissues and subsequent intracellular payload release in response to glutathione (GSH) inside tumor cells. As a proof of concept, this supramolecular nanomedicine exhibited specific chemotherapeutic effects against cancer in vitro and in vivo with a decent safety profile.
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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".