AGuIX nanoparticles as a promising platform for image-guided radiation therapy
Bibliographic record
Abstract
AGuIX are gadolinium-based nanoparticles developed mainly for imaging due to their MR contrast properties. They also have a potential role in radiation therapy as a radiosensitizer. We used MRI to quantify the uptake of AGuIX in pancreatic cancer cells, and TEM for intracellular localization. We measured the radiosensitization of a pancreatic cancer cell line in a low-energy (220 kVp) beam, a standard 6 MV beam (STD) and a flattening filter free 6 MV beam (FFF). We demonstrated that the presence of nanoparticles significantly decreases cell survival when combined with an X-ray beam with a large proportion of low-energy photons (close to the k-edge of the nanoparticles). The concentration of nanoparticles in the cell achieves its highest level after 15 min and then reaches a plateau. The accumulated nanoparticles are mainly localized in the cytoplasm, inside vesicles. We found that the 6 MV FFF beams offer the best trade-off between penetration depth and proportion of low-energy photons. At 10 cm depth, we measured a DEF 20 % of 1.30 ± 0.47 for the 6 MV FFF beam, compared to 1.23 ± 0.26 for the 6 MV STD beam. Additional measurements with un-incubated nanoparticles provide evidence that chemical processes might also be contributing to the dose enhancement effect.
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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 | 0.000 |
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".