Two-photon photodynamic therapy and its potential application to age related macular degenerations
Bibliographic record
Abstract
Photodynamic therapy (PDT) using verteporfin is widely used for treatment of age related macular degeneration (AMD). Due to non-perfect selectivity of the drug accumulation in the neovasculature some collateral damage to healthy tissue arises during the treatment. Damage to healthy structures in the eye is always a concern because of a high probability of reducing visual acuity. Two-photon (2-g) photodynamic therapy potentially offers much higher treatment selectivity than its one-photon (1-g) counterpart. By utilizing focused light for 2-g excitation, treatment volumes on the order of microliters can be achieved thus maximizing localized insult to abnormal blood vessels and sparing healthy tissue. We propose that 2-g photodynamic therapy will be valuable in the treatment of choroidal neovascularization secondary to age related macular degeneration as well as other conditions. To ascertain feasibility of 2-g photodynamic therapy we measured 2-g spectrum and cross sections of verteporfin (80 GM at 940 nm, 1 GM = 10 -50 cm 4 s/photon), chlorin e6 (14 GM at 800 nm) and tetrasulfonated aluminum phthalocyanine (140 GM at 900 nm) and investigated their in vitro efficiency under 2-g excitation. Only verteporfin demonstrated cell kill under the used irradiation parameters (average light intensity 9.1 mW, wavelength 850 nm, total light dose 6900 J/cm 2 ). Dorsal skinfold window chamber model in mouse was used to test efficiency of 2-g PDT with verteporfin in vivo . Although we were able to induce photodynamic damage to a blood vessel using 1-g excitation, 2-g excitation resulted in no visible damage to irradiated blood vessel. The most probable reason is low efficiency of verteporfin as a 2-g photosensitizer. We also report 2-g spectrum of new photosensitizer, HCC4 (4300 GM at 830 nm), specifically designed for efficient 2-g excitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".