Drug and light dose responses to focal photodynamic therapy of single blood vessels in vivo
Bibliographic record
Abstract
As part of an ongoing program to develop two-photon (2-gamma) photodynamic therapy (PDT) for treatment of wet-form age-related macular degeneration (AMD) and other vascular pathologies, we have evaluated the reciprocity of drug-light doses in focal-PDT. We targeted individual arteries in a murine window chamber model, using primarily the clinical photosensitizer Visudyne/liposomal-verteporfin. Shortly after administration of the photosensitizer, a small region including an arteriole was selected and irradiated with varying light doses. Targeted and nearby vessels were observed for a maximum of 17 to 25 h to assess vascular shutdown, tapering, and dye leakage/occlusion. For a given end-point metric, there was reciprocity between the drug and light doses, i.e., the response correlated with the drug-light product (DLP). These results provide the first quantification of photosensitizer and light dose relationships for localized irradiation of a single blood vessel and are compared to the DLP required for vessel closure between 1-gamma and 2-gamma activation, between focal and broad-beam irradiation, and between verteporfin and a porphyrin dimer with high 2-gamma cross section. Demonstration of reciprocity over a wide range of DLP is important for further development of focal PDT treatments, such as the targeting of feeder vessels in 2-gamma PDT of AMD.
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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) | 0.000 | 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".