Doxorubicin-loaded graphene oxide nanocomposites in cancer medicine: stimuli-responsive carriers, co-delivery and suppressing resistance
Bibliographic record
Abstract
INTRODUCTION: The application of doxorubicin (DOX) in cancer therapy has been limited due to its drug resistance and poor internalization. Graphene oxide (GO) nanostructures have the capacity for DOX delivery while promoting its cytotoxicity in cancer. AREAS COVERED: The favorable characteristics of GO nanocomposites, preparation method, and application in cancer therapy are described. Then, DOX resistance in cancer, GO-mediated photothermal therapy, and DOX delivery for cancer suppression are described. Preparation of stimuli-responsive GO nanocomposites, surface functionalization, hybrid nanoparticles, and theranostic applications are emphasized in DOX chemotherapy. EXPERT OPINION: GO nanoparticle-based photothermal therapy maximizes the anti-cancer activity of DOX against cancer cells. Besides DOX delivery, GO nanomaterials are capable of loading anti-cancer agents and genetic tools to minimize drug resistance and enhance the cytolytic impact of DOX in cancer eradication. To enhance DOX accumulation, stimuli-responsive (redox-, light-, enzyme- and pH-sensitive) GO nanoparticles have been developed for DOX delivery. Development of targeted delivery of DOX-loaded GO nanomaterials against cancer cells may be achieved by surface modification of polymers such as polyethylene glycol, hyaluronic acid, and chitosan. DOX-loaded GO nanoparticles have demonstrated theranostic potential. Hybridization of GO with other nanocarriers such as silica and gold nanoparticles further broadens their potential anti-cancer therapy 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".