Actively Targeting Redox-Responsive Multifunctional Micelles for Synergistic Chemotherapy of Cancer
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Stimuli-responsive polymeric micelles decorated with cancer biomarkers represent an optimal choice for drug delivery applications due to their ability to enhance therapeutic efficacy while mitigating adverse side effects. Accordingly, we synthesized a digoxin-modified novel multifunctional redox-responsive disulfide-linked poly(ethylene glycol- b -poly(lactic- co -glycolic acid) copolymer (Bi(Dig–PEG-PLGA)-S 2 ) for the targeted and controlled release of doxorubicin (DOX) in cancer cells. Within the micellar aggregate, the disulfide bond confers redox responsiveness, while the presence of the digoxin moiety acts as a targeting agent and chemosensitizer for DOX. Upon self-assembly in aqueous solution, Bi(Dig–PEG-PLGA)-S 2 formed uniformly distributed spherical micelles with a hydrodynamic diameter ( D h ) of 58.36 ± 0.78 nm and a zeta potential of −24.71 ± 1.01 mV. The micelles exhibited desirable serum and colloidal stability with a substantial drug loading capacity (DLC) of 6.26% and an encapsulation efficiency (EE) of 83.23%. In addition, the release of DOX demonstrated the redox-responsive behavior of the micelles, with approximately 89.41 ± 6.09 and 79.64 ± 6.68% of DOX diffusing from DOX@Bi(Dig–PEG-PLGA)-S 2 in the presence of 10 mM GSH and 0.1 mM H 2 O 2, respectively, over 96 h. Therefore, in HeLa cell lines, DOX@Bi(Dig–PEG-PLGA)-S 2 showed enhanced intracellular accumulation and subsequent apoptotic effects, attributed to the targeting ability and chemosensitization potential of digoxin. Hence, these findings underscore the promising characteristics of Bi(Dig–PEG-PLGA)-S 2 as a multifunctional drug delivery vehicle for cancer treatment.
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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".