Preparation and Characterization of Chlorin e6-Conjugated Au Nanoparticles as the Radiosensitizer for Enhanced Radiotherapy
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The interaction between ionizing radiation and materials composed of high Z-number elements could be applied to enhance radiotherapy. In this work, we fabricated an ionizing radiation-sensitive nanoplatform by grafting chlorin e6 (Ce6) onto the surface of ultrasmall gold nanoparticles (Au NPs), aiming to enhance the radiation effects induced by different radiation sources. Poly(ethylene glycol) (PEG) was applied as the shape-controlling agent during the synthesis of Au nanoparticles. The as-prepared Au NPs show excellent monodispersity, with an average hydrodynamic diameter of around 5 nm. U87 and HeLa cell lines were utilized to evaluate the biological properties of the as-prepared Ce6–Au NPs. The Cell Counting Kit-8 (CCK-8) results reveal that the Ce6–Au NPs conjugate can significantly affect the growth of U87 cells under X-ray and 68 Ga exposure, which is not seen for the pure Au NPs, Ce6, and physically mixed Ce6 and Au NPs. Moreover, the Ce6–Au NPs conjugate show evident cell prefoliation inhibition of U87 and HeLa cells under both X-ray and 18 F-radiolabeled fluorodeoxyglucose ( 18 F-FDG) exposure. These results indicate that interaction exists between Ce6 and Au NPs under radiation exposure. The mRNA sequencing results show that the tumor killing performance induced by Ce6–Au NPs may be due to regulation of the tumor microenvironment (TME) and immune-relevant signaling pathway. Our research proves that the rational combination of Au NPs and Ce6 can make better use of ionizing radiation energy and thus improve the therapeutic outcome of 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) | 0.001 | 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".