Peptide-functionalized gold nanoparticles for boron neutron capture therapy with the potential to use in Glioblastoma treatment
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
Glioblastoma is a highly aggressive glioma with limited treatment options. Boron neutron capture therapy (BNCT) offers a promising approach for refractory cancers, utilizing boron-10 (10B) and thermal neutrons to generate cytotoxic particles. Effective BNCT depends on selective targeting and retention of 10B in tumors. Current BNCT drugs face issues with rapid clearance and poor tumor accumulation. To address this, we developed gold nanoparticles (AuNPs) functionalized with cyclic arginine-glycine-aspartic acid (cRGD) peptides as a nanocarrier for Sodium Mercaptododecaborate (BSH), resulting in AuNPs-BSH&PEG-cRGD. In vitro, AuNPs-BSH&PEG-cRGD increased 10B content in GL261 glioma cells by approximately 2.5-fold compared to unmodified AuNPs-BSH&PEG, indicating enhanced targeting due to cRGD's affinity for integrin receptor αvβ3. In a subcutaneous glioma mouse model, 6 h post-intratumoral administration, the 10B concentration in tumors was 17.98 μg/g for AuNPs-BSH&PEG-cRGD, significantly higher than 0.45 μg/g for BSH. The tumor-to-blood (T/B) and tumor-to-normal tissue (T/N) ratios were also higher for AuNPs-BSH&PEG-cRGD, suggesting improved targeting and retention. This indicates that AuNPs-BSH&PEG-cRGD may enhance BNCT efficacy and minimize normal tissue toxicity. In summary, this study provides a novel strategy for BSH delivery and may broaden the design vision of BNCT nano-boron capture agents.
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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 | 0.000 |
| 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 it