Monitoring Photoproduct Formation and Photobleaching by Fluorescence Spectroscopy Has the Potential to Improve PDT Dosimetry with a Verteporfin-like Photosensitizer¶
Bibliographic record
Abstract
In current clinical practice, photodynamic therapy (PDT) is carried out with prescribed drug doses and light doses as well as fixed drug-light intervals and illumination fluence rates. This approach can result in undesirable treatment outcomes of either overtreatment or undertreatment because of biological variations between different lesions and patients. In this study, we explore the possibility of improving PDT dosimetry by monitoring drug photobleaching and photoproduct formation. The study involved 60 mice receiving the same drug dose of a novel verteporfin-like photosensitizer, QLT0074, at 0.3 mg/kg body weight, followed by different light doses of 5, 10, 20, 30, 40 or 50 J/cm2 at 686 nm and a fluence rate of 70 mW/cm2. Photobleaching and photoproduct formation were measured simultaneously, using fluorescence spectroscopy. A ratio technique for data processing was introduced to reliably detect the photoproduct formed by PDT on mouse skin in vivo. The study showed that the QLT0074 photoproduct is stable and can be reliably quantified. Three new parameters, photoproduct score (PPS), photobleaching score (PBS) and percentage photobleaching score (PBS%), were introduced and tested together with the conventional dosimetry parameter, light dose, for performance on predicting PDT-induced outcome, skin necrosis. The statistical analysis of experimental results was performed with an ordinal logistic regression model. We demonstrated that both PPS and PBS improved the prediction of skin necrosis dramatically compared to light dose. PPS was identified as the best single parameter for predicting the PDT outcome.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".