Utilizing two-dimensional monolayer and three-dimensional spheroids to enhance radiotherapeutic potential by combining gold nanoparticles and docetaxel
Bibliographic record
Abstract
Background: Much in vitro research on the applicability of gold nanoparticles (GNPs) in cancer treatment has been focused on two-dimensional (2D) monolayer models. To improve this, we explored the effect of the combination of GNPs and docetaxel (DTX) with radiotherapy (RT) in a more complex three-dimensional (3D) spheroid that can better mimic a real tumour microenvironment. Methods: and with DTX at a dose that inhibited growth-rate by 50%. Samples were irradiated 24 h after drug dosing with 2 Gy, 5 Gy, or 10 Gy using a 6 MV beam. Monolayer cells had the DNA double-strand breaks (DSBs) probed 24 h post-radiation, and cell proliferation observed over 7 days. Spheroid proliferation was monitored over 14 days along with spheroid volume measurements. Results: In DTX and GNP-treated monolayer samples, there is decreased survival after irradiation with 5 and 10 Gy of 16-24% and an increase in DSBs of 91.6-109.9%, compared to DTX. In spheroids, GNPs decreased the surviving cells by 10.54-15.61% compared to control, while GNPs and DTX decreased survival by 20.9-31.04%. There is reduced spheroid volume 14 days after treatment with the triple combination. Conclusions: Combining GNPs and DTX leads to a synergistic radiosensitization effect in spheroids, which can better mimic the tumour microenvironment. Testing treatment modalities with spheroids and RT may allow a quicker translation to the clinic. Supplementary Information: The online version contains supplementary material available at 10.1186/s12645-023-00231-5.
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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".