Hydrophobic Cysteine Poly(disulfide)‐based Redox‐Hypersensitive Nanoparticle Platform for Cancer Theranostics
Bibliographic record
Abstract
Selective tumor targeting and drug delivery are critical for cancer treatment. Stimulus-sensitive nanoparticle (NP) systems have been designed to specifically respond to significant abnormalities in the tumor microenvironment, which could dramatically improve therapeutic performance in terms of enhanced efficiency, targetability, and reduced side-effects. We report the development of a novel L-cysteine-based poly (disulfide amide) (Cys-PDSA) family for fabricating redox-triggered NPs, with high hydrophobic drug loading capacity (up to 25 wt% docetaxel) and tunable properties. The polymers are synthesized through one-step rapid polycondensation of two nontoxic building blocks: L-cystine ester and versatile fatty diacids, which make the polymer redox responsive and give it a tunable polymer structure, respectively. Alterations to the diacid structure could rationally tune the physicochemical properties of the polymers and the corresponding NPs, leading to the control of NP size, hydrophobicity, degradation rate, redox response, and secondary self-assembly after NP reductive dissociation. In vitro and in vivo results demonstrate these NPs' excellent biocompatibility, high selectivity of redox-triggered drug release, and significant anticancer performance. This system provides a promising strategy for advanced anticancer theranostic 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".