Combining Pr<sup>3+</sup>-Doped Nanoradiosensitizers and Endogenous Protoporphyrin IX for X-ray-Mediated Photodynamic Therapy of Glioblastoma Cells
Bibliographic record
Abstract
Glioblastoma multiforme is an aggressive type of brain cancer with high recurrence rates due to the presence of radioresistant cells remaining after tumor resection. Here, we report the development of an X-ray-mediated photodynamic therapy (X-PDT) system using NaLuF 4:25% Pr 3+ radioluminescent nanoparticles in conjunction with protoporphyrin IX (PPIX), an endogenous photosensitizer that accumulates selectively in cancer cells. Conveniently, 5-aminolevulinic acid (5-ALA), the prodrug that is administered for PDT, is the only drug approved for fluorescence-guided resection of glioblastoma, enabling dual detection and treatment of malignant cells. NaLuF 4:Pr 3+ nanoparticles were synthesized and spectroscopically evaluated at a range of Pr 3+ concentrations. This generated radioluminescent nanoparticles with strong emissions from the 1 S 0 excited state of Pr 3+, which overlaps with the Soret band of PPIX to perform photodynamic therapy. The spectral overlap between the nanoparticles and PPIX improved treatment outcomes for U251 cells, which were used as a model for the thin tumor margin. In addition to sensitizing PPIX to induce X-PDT, our nanoparticles exhibit strong radiosensitizing properties through a radiation dose-enhancement effect. We evaluate the effects of the nanoparticles alone and in combination with PPIX on viability, death, stress, senescence, and proliferation. Collectively, our results demonstrate this as a strong proof of concept for nanomedicine.
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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".