High quantum efficiency ruthenium coordination complex photosensitizer for improved radiation-activated Photodynamic Therapy
Bibliographic record
Abstract
Traditional external light-based Photodynamic Therapy (PDT)’s application is limited to the surface and minimal thickness tumors because of the inefficiency of light in penetrating deep-seated tumors. To address this, the emerging field of radiation-activated PDT (radioPDT) uses X-rays to trigger photosensitizer-containing nanoparticles (NPs). A key consideration in radioPDT is the energy transfer efficiency from X-rays to the photosensitizer for ultimately generating the phototoxic reactive oxygen species (ROS). In this study, we developed a new variant of pegylated poly-lactic-co-glycolic (PEG-PLGA) encapsulated nanoscintillators (NSCs) along with a new, highly efficient ruthenium-based photosensitizer (Ru/radioPDT). Characterization of this NP via transmission electron microscopy, dynamic light scattering, UV-Vis spectroscopy, and inductively coupled plasma mass-spectroscopy showed an NP size of 120 nm, polydispersity index (PDI) of less than 0.25, high NSCs loading efficiency over 90% and in vitro accumulation within the cytosolic structure of endoplasmic reticulum and lysosome. The therapeutic efficacy of Ru/radioPDT was determined using PC3 cell viability and clonogenic assays. Ru/radioPDT exhibited minimal cell toxicity until activated by radiation to induce significant cancer cell kill over radiation alone. Compared to protoporphyrin IX-mediated radioPDT (PPIX/radioPDT), Ru/radioPDT showed higher capacity for singlet oxygen generation, maintaining a comparable cytotoxic effect on PC3 cells.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".