Utilizing two-dimensional monolayer and three-dimensional spheroids to enhance radiotherapeutic potential by combining gold nanoparticles and docetaxel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it