Dual-Agent Photodynamic Therapy with Optical Clearing Eradicates Pigmented Melanoma in Preclinical Tumor Models
Bibliographic record
Abstract
Treatment using light-activated photosensitizers (photodynamic therapy, PDT) has shown limited efficacy in pigmented melanoma, mainly due to the poor penetration of light in this tissue. Here, an optical clearing agent (OCA) was applied topically to a cutaneous melanoma model in mice shortly before PDT to increase the effective treatment depth by reducing the light scattering. This was used together with cellular and vascular-PDT, or a combination of both. The effect on tumor growth was measured by longitudinal ultrasound/photoacoustic imaging in vivo and by immunohistology after sacrifice. In a separate dorsal window chamber tumor model, angiographic optical coherence tomography (OCT) generated 3D tissue microvascular images, enabling direct in vivo assessment of treatment response. The optical clearing had minimal therapeutic effect on the in control, non-pigmented cutaneous melanomas but a statistically significant effect (p < 0.05) in pigmented lesions for both single- and dual-photosensitizer treatment regimes. The latter enabled full-depth eradication of tumor tissue, demonstrated by the absence of S100 and Ki67 immunostaining. These studies are the first to demonstrate complete melanoma response to PDT in an immunocompromised model in vivo, with quantitative assessment of tumor volume and thickness, confirmed by (immuno) histological analyses, and with non-pigmented melanomas used as controls to clarify the critical role of melanin in the PDT response. The results indicate the potential of OCA-enhanced PDT for the treatment of pigmented lesions, including melanoma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".