Quantitative <i>In Vitro</i> Demonstration of Two‐Photon Photodynamic Therapy Using Photofrin<sup>®</sup> and Visudyne<sup>®</sup>
Bibliographic record
Abstract
Photodynamic therapy (PDT), the combined action of a photosensitizer and light to produce a cytotoxic effect, is an approved therapy for a number of diseases. At present, clinical PDT treatments involve one-photon excitation of the photosensitizer. A major limitation is that damage may be caused to healthy tissues that have absorbed the drug and lie in the beam path. Two-photon excitation may minimize this collateral damage, as the probability of absorption increases with the square of the light intensity, enabling spatial confinement of the photosensitizer activation. A potential application is the treatment of the wet-form of age-related macular degeneration, the foremost cause of central vision loss in the elderly. Herein, the commercial photosensitizers Visudyne and Photofrin are used to demonstrate quantitative in vitro two-photon PDT. A uniform layer of endothelial cells (YPEN-1) was irradiated with a Ti:sapphire laser (300 fs, 865 nm, 90 MHz) using a confocal scanning microscope. Quantification of the two-photon PDT effect was achieved using the permeability stain Hoechst 33258 and a SYTOX Orange viability stain. Visudyne was found to be around seven times more effective as a two-photon photosensitizer than Photofrin under the conditions used, consistent with its higher two-photon absorption cross-section. We also demonstrate for the first time the quadratic intensity dependence of cellular two-photon PDT. This simple in vitro method for quantifying the efficacy of photosensitizers for two-photon excited PDT will be valuable to test specifically designed two-photon photosensitizers before proceeding to in vivo studies in preclinical animal models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".