Comparison of photodynamic therapy with different excitation wavelengths using a dynamic model of aminolevulinic acid-photodynamic therapy of human skin
Bibliographic record
Abstract
Different wavelength light sources are used in photodynamic therapy (PDT) of the skin to treat different conditions. Clinical studies show inconsistent results for the effectiveness of aminolevulinic acid (ALA)-PDT performed at different wavelengths. In order to understand the effect of treatment wavelength, a theoretical study was performed to calculate time-resolved depth-dependent distributions of PDT components including ground-state oxygen, sensitizer, and reacted singlet oxygen for different treatment wavelengths (405, 523, and 633 nm) using a numerical model of ALA-PDT of human skin. This model incorporates clinically relevant features of the PDT process including light attenuation, photobleaching, oxygen consumption, and diffusion, as well as tissue perfusion. The calculations show that the distributions of these quantities are almost independent of the treatment wavelength to a depth of about 1 mm. In this surface region, PDT-induced hypoxia is the dominant process. At greater depths, the production of singlet -oxygen is governed by the penetration of the treatment light. Two noninvasive PDT dosimetry approaches: the cumulative singlet oxygen luminescence (CSOL) and the fractional fluorescence bleaching metric, were investigated and compared for all three wavelengths. Although CSOL was more robust, both metrics provided correlations with the singlet oxygen dose in the upper dermis that were almost independent of treatment wavelength. This relationship breaks down at greater depths because light penetration depends on wavelength.
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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".