Comparison of red and green light for treating non‐muscle invasive bladder cancer in rats using singlet oxygen‐cleavable prodrugs with <scp>PPIX‐PDT</scp>
Bibliographic record
Abstract
Abstract It has been 30 years since Photofrin‐PDT was approved for the treatment of bladder cancer in Canada. However, Photofrin‐PDT failed to gain popularity due to bladder complications. The PDT with red light and IV‐administered Photofrin could permanently damage the bladder muscle. We have been developing a new combination strategy of PpIX‐PDT with singlet oxygen‐cleavable prodrugs for NMIBC with minimal side effects, avoiding damage to the bladder muscle layer. PpIX can be excited by either green (532 nm) or red (635 nm) light. Red light could be more efficacious in vivo due to its deeper tissue penetration than green light. Since HAL preferentially produces PpIX in tumors, we hypothesized that illuminating PpIX with red light might spare the muscle layer. PpIX‐PDT was used to compare green and red laser efficacy in vitro and in vivo. The IC50 of in vitro PpIX‐PDT was 18 mW/cm2 with the red laser and 22 mW/cm2 with the green laser. The in vivo efficacy of the red laser with 50, 75, and 100 mW total dose was similar to the same dose of green laser in reducing tumor volume. Combining PpIX‐PDT with prodrugs methyl‐linked mitomycin C (Mt‐L‐MMC) and rhodamine‐linked SN‐38 (Rh‐L‐SN‐38) significantly improved efficacy (tumor volume comparison). PpIX‐PDT or PpIX‐PDT + prodrug combination did not cause muscle damage in histological analysis. Overall, a combination of PpIX‐PDT and prodrug with 635 nm laser is promising for non‐muscle invasive bladder cancer treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".