Significant Radiation Enhancement Effects by Gold Nanoparticles in Combination with Cisplatin in Triple Negative Breast Cancer Cells and Tumor Xenografts
Bibliographic record
Abstract
Gold nanoparticles (AuNPs) and cisplatin have been explored in concomitant chemoradiotherapy, wherein they elicit their effects by distinct and overlapping mechanisms. Cisplatin is one of the most frequently utilized radiosensitizers in the clinical setting; however, the therapeutic window of cisplatin-aided chemoradiotherapy is limited by its toxicity. The goal of this study was to determine whether AuNPs contribute to improving the treatment response when combined with fractionated cisplatin-based chemoradiation in both in vitro and in vivo models of triple-negative breast cancer (MDA-MB-231Luc ). Cellular-targeting AuNPs with receptor-mediated endocytosis (AuNP-RME) in vitro at a noncytotoxic concentration (0.5 mg/ml) or cisplatin at IC25 (12 μM) demonstrated dose enhancement factors (DEFs) of 1.25 and 1.14, respectively; the combination of AuNP-RME and cisplatin resulted in a significant DEF of 1.39 in vitro. Transmission electron microscopy (TEM) images showed effective cellular uptake of AuNPs at tumor sites 24 h after intratumoral infusion. Computed tomography (CT) images demonstrated that the intratumoral levels of gold remained stable up to 120 h after infusion. AuNPs (0.5 mg gold per tumor) demonstrated a radiation enhancement effect that was equivalent to three doses of cisplatin at IC25 (4 mg/kg), but did not induce intrinsic toxicity or increased radiotoxicity. Results from this study suggest that AuNPs are the true radiosensitizer in these settings. Importantly, AuNPs enhance the treatment response when combined with cisplatin-based fractionated chemoradiation. This combination of AuNPs and cisplatin provides a promising approach to improving the therapeutic ratio of fractionated radiotherapy.
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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) | 0.000 | 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 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".