Thio-glucose bound gold nanoparticles enhance radio-cytotoxic targeting of ovarian cancer
Bibliographic record
Abstract
The treatment of ovarian cancer has traditionally been intractable, and required novel approaches to improve therapeutic efficiency. This paper reports that thio-glucose bound gold nanoparticles (Glu-GNPs) can be used as a sensitizer to enhance ovarian cancer radiotherapy. The human ovarian cancer cells, SK-OV-3, were treated by gold nanoparticles (GNPs) alone, irradiation alone, or GNPs in addition to irradiation. Cell uptake was assayed using inductively coupled plasma atomic emission spectroscopy (ICP-AES), while cytotoxicity induced by radiotherapy was measured using both 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide and clonogenic assays. The presence of reactive oxygen species (ROS) was determined using CM-H2-DCFDA confocal microscopy and cell apoptosis was determined by an Annexin V-FITC/propidium iodide (PI) kit with flow cytometry. The cells treated by Glu-GNPs resulted in an approximate 31% increase in nanoparticle uptake compared to naked GNPs (p < 0.005). Compared to the irradiation alone treatment, the intracellular uptake of Glu-GNPs resulted in increased inhibition of cell proliferation by 30.48% for 90 kVp and 26.88% for 6 MV irradiation. The interaction of x-ray radiation with GNPs induced elevated levels of ROS production, which is one of the mechanisms by which GNPs can enhance radiotherapy on ovarian cancer.
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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".