pH and Reduction Dual-Sensitive Copolymeric Micelles for Intracellular Doxorubicin Delivery
Bibliographic record
Abstract
This study develops novel pH and reduction dual-sensitive micelles for the anticancer drug doxorubicin (DOX) delivery owing to the fact that the tumor tissues show low pH and high reduction environment. These sub-100 nm micelles present a core-shell structure under physiological conditions, but quickly release the loaded drugs responding to acidic and reductive stimuli. With disulfide bonds in each repeat unit of poly(β-amino ester)s, the novel copolymer was synthesized via Michael addition polymerization from 2,2'-dithiodiethanol diacrylate, 4,4'-trimethylene dipiperidine, and methoxy-PEG-NH(2). DOX released faster from micelles in a weakly acidic environment (pH 6.5) than at pH 7.4 or in the presence of a higher concentration (5 mM) of reducing agent (DTT). The release is even more effective in a scenario of both stimuli (pH 6.5 and 5 mM DTT). MTT assay showed that the DOX-loaded micelles had a higher cytotoxicity for HepG2 tumor cells than DOX at higher concentrations, and that blank micelles had a very low cytotoxicity to the tumor cells. Confocal microscopy observation showed that the micelles can be quickly internalized, effectively deliver the drugs into nuclei, and inhibit cell growth. These results present the copolymer as a novel and effective pH and reduction dual-responsive nanocarrier to enhance drug efficacy for cancer cells.
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) | 0.001 | 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".