Singlet Oxygen Luminescence Dosimetry (SOLD) for Photodynamic Therapy: Current Status, Challenges and Future Prospects
Bibliographic record
Abstract
As photodynamic therapy (PDT) continues to develop and find new clinical indications, robust individualized dosimetry is warranted to achieve effective treatments. We posit that the most direct PDT dosimetry is achieved by monitoring singlet oxygen (1O2), the major cytotoxic species generated photochemically during PDT. Its detection and quantification during PDT have been long-term goals for PDT dosimetry and the development of techniques for this, based on detection of its near-infrared luminescence emission (1270 nm), is at a noteworthy stage of development. We begin by discussing the theory behind singlet-oxygen luminescence dosimetry (SOLD) and the seminal contributions that have brought SOLD to its current status. Subsequently, technology developments that could potentially improve SOLD are discussed, together with future areas of research, as well as the potential limitations of this method. We conclude by examining the major thrusts for future SOLD applications: as a tool for quantitative photobiological studies, a point of reference to evaluate other PDT dosimetry techniques, the optimal means to evaluate new photosensitizers and delivery methods and, potentially, a direct and robust clinical dosimetry system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".