Evidence of energy transfer in nanoparticle-porphyrins conjugates for radiation therapy enhancement
Bibliographic record
Abstract
We report progress towards combining radiation therapy (RT) and photodynamic therapy (PDT) using scintillating nanoparticle (NP)-photosensitizer conjugates. In this approach, scintillating NPs are excited by clinically relevant ionizing radiation sources and subsequently transfer energy to conjugated photosensitizers via FRET, acting as an energy mediator between ionizing radiation and photosensitizer molecules. The excited photosensitizers generate reactive oxygen species that can induce local damage and immune response. Advantages of the scheme include: 1) Compared with traditional radiation therapy, a possible decrease of the total radiation dose needed to eliminate the lesion; 2) Compared with traditional PDT, the ability to target deeper and more highly pigmented lesions; 3) The possibility of additional photosensitizing effects due to the scintillation of the nanoparticles. In this work, the photosensitizer molecule chlorin e6 was covalently bound to the surface of LaF3:Ce NPs. After conjugation, the photoluminescence intensity of NPs decreased, and fluorescence lifetime of conjugated chlorin e6 became sensitive to excitation wavelength, suggesting rapid FRET. In addition, scintillation spectra of nanoparticles were measured. Preliminary calculations suggest that the observed scintillation efficiencies are sufficient to enhance RT. In vitro cancer cell studies suggest conjugates are taken up by cells. Survival curves with radiation exposure suggest that the particles alone cause radiosensitization comparable to that seen with gold nanoparticles.
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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".