One- and two-photon activated phototoxicity of conjugated porphyrin dimers with high two-photon absorption cross sections
Bibliographic record
Abstract
Two-photon excited photodynamic therapy (PDT) has the potential to provide a highly targeted treatment for neoplastic diseases, as excitation can be pin-pointed to small volumes at the laser focus. In addition, two-photon PDT offers deeper penetration into mammalian tissue due to the longer wavelength of irradiation. Here we report the one-photon and two-photon excited PDT results for a collection of conjugated porphyrin dimers with high two-photon absorption cross sections. These dimers demonstrate high one-photon PDT efficacy against a human ovarian adenocarcinoma cell line (SK-OV-3) and exhibit no significant dark-toxicity at concentrations of up to 20 microM. Their one-photon excited PDT efficiencies, following irradiation at 657 nm, approach that of Visudyne, a drug used clinically for PDT. We investigated and optimised the effect of the photosensitizer concentration, incubation time and the light dose on the PDT efficacy of these dimers. These studies led to the selection of P2C2-NMeI as the most effective porphyrin dimer. We have demonstrated that P2C2-NMeI undergoes a two-photon activated process following excitation at 920 nm (3.6-6.8 mW, 300 fs, 90 MHz) and compared it to Visudyne. We conclude that the in vitro two-photon PDT efficacy of P2C2-NMeI is about twice that of Visudyne. This result highlights the potential of this series of porphyrin dimers for two-photon PDT.
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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".