Synergistic performance of triggered drug release and photothermal therapy of MCF7 cells based on laser activated PEGylated GO + DOX
Bibliographic record
Abstract
Graphene oxide is used as a singular 2D nano-carrier in cancer therapy. Here, graphene oxide is used as a hybrid chemo-drug graphene oxide (GO) + doxorubicin (DOX), mainly due to its unique chemical and optical properties. The laser triggers GO + DOX for selective drug delivery to optimize the drug release. The characterization of GO is investigated in terms of laser properties at 808 nm. Furthermore, the laser activates GO + DOX compounds to treat MCF7 cancerous cells. The drug release strongly depends on the temperature rise that mainly effects on the viability of the cancerous cells of interest. DOX simultaneously acts as a chemo-drug and as an optical fluorescent agent, whereas GO performs as an efficient photothermal nano-carrier. In fact, the GO-DOX hybrid drug demonstrates multifunctional during malignant cell treatment. We have shown that the laser heating of GO enhances the release percentage up to a treatment yield of 90%. This arises from the synergistic nature of DOX and GO compounds in simultaneous chemo/photo thermal therapy. Furthermore, the fluorescence property of DOX is used to assess the GO uptake using confocal microscope imaging.
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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".