Photobleaching kinetics of Photofrin<i>in vivo</i>and in multicell tumour spheroids indicate two simultaneous bleaching mechanisms
Bibliographic record
Abstract
We present a detailed investigation of Photofrin photobleaching and photoproduct accumulation. Fisher rats were sensitized with 10 mg kg(-1) Photofrin and irradiated 24 h later with 514 nm light at 5 or 100 mW cm(-2). Fluorescence spectra were collected from the skin throughout treatment, and sensitizer bleaching and fluorescent photoproduct formation were quantified using spectral analysis. Photofrin bleaching was slightly more rapid at the higher irradiance under these conditions. However, accumulation of photoproduct was significantly enhanced at lower irradiance. To interpret these unexpected findings, we developed a new mathematical model in which reactions between singlet oxygen (1O2) and the photosensitizer and reactions between the sensitizer triplet and biological targets are both allowed to contribute to bleaching. Predictions of this model were tested in experiments performed on EMT6 spheroids sensitized with concentrations of 2.5, 10 and 30 microg mL(-1) Photofrin and subjected to PDT. Photofrin bleaching and photoproduct formation in these spheroids were measured using confocal fluorescence spectroscopy. In qualitative agreement with the mixed-mechanism model predictions, at the highest drug concentration Photofrin bleaching was more efficient via 1O2 reactions, while at the lowest concentration triplet reactions were more efficient. At all concentrations, photoproduct accumulation was greater under conditions of abundant oxygen.
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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".