A dynamic model for ALA-PDT of skin: simulation of temporal and spatial distributions of ground-state oxygen, photosensitizer and singlet oxygen
Bibliographic record
Abstract
Singlet oxygen (¹O₂) direct dosimetry and photosensitizer fluorescence photobleaching are being investigated and applied as dosimetric tools during 5-aminolevulinic acid (ALA)-induced protophorphyrin IX (PpIX) photodynamic therapy (PDT) of normal skin and skin cancers. The correlations of photosensitizer fluorescence and singlet oxygen luminescence (SOL) emission signals to ¹O2 distribution and cumulative ¹O₂dose are difficult to interpret because of the temporal and spatial variations of three essential components (light fluence rate, photosensitizer concentration and oxygen concentration) in PDT. A one-dimensional model is proposed in this paper to simulate the dynamic process of ALA-PDT of normal human skin in order to investigate the time-resolved evolution of PpIX, ground-state oxygen (³O₂and ¹O₂ distributions. The model incorporates a simplified three-layer semi-infinite skin tissue, Monte Carlo simulations of excitation light fluence and both PpIX fluorescence and SOL emission signals reaching the skin surface, ¹O₂-mediated photobleaching mechanism for updating PpIX, ³O₂ and ¹O₂ distributions after the delivery of each light dose increment, ground-state oxygen supply by diffusion from the atmosphere and perfusion from blood vessels, a cumulative ¹O₂-dependent threshold vascular response, and the initial non-uniform distribution of PpIX. The PpIX fluorescence simulated using this model is compared with clinical data reported by Cottrell et al (2008 Clin. Cancer Res. 14 4475-83) for a range of irradiances (10-150 mW cm⁻²). Except for the vascular response, one set of parameters is used to fit data at all irradiances. The time-resolved depth-dependent distributions of PpIX, ³O₂ and ¹O₂ at representative irradiances are presented and discussed in this paper, as well as the PDT-induced vascular response at different depths. Tissue hypoxia and shutdown of oxygen supply occur in the upper dermis, where PpIX is also preserved at the end of treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".