Photodynamic therapy using Verteporfin (benzoporphyrin derivative monoacid ring A, BPD‐MA) and 630 nm laser light in canine esophagus*
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Verteporfin is a new photosensitizer with short-term skin photosensitivity. The objective of this preclinical study was to find the light dose that effectively ablates canine esophageal mucosa when delivered 30 minutes after Verteporfin injection. STUDY DESIGN/MATERIALS AND METHODS: Verteporfin was administered intravenously (0.75 mg/kg). 630 nm light from KTP/Dye laser was delivered using an esophageal Photodynamic therapy (PDT) balloon. In Phase I study, animals were treated 30 minutes after drug injection using 40, 60, and 80 J/cm to find the desired light dose. Using results from phase I and application of reciprocity principle (light dose vs. plasma concentration of drug), additional light doses were calculated for delivery at other times. In phase II, animals were treated at 15, 60, and 120 minutes, using the calculated light doses of 60, 145, and 200 J/cm, respectively. Animals were followed for 2 days to 4 weeks. RESULTS: In Phase I, 80 J/cm at 30 minutes induced total mucosal ablation. In Phase II, light doses of 60, 145, and 200 J/cm induced similar mucosal injuries when delivered at 15, 60, and 120 minutes, respectively. CONCLUSIONS: Effective mucosal ablation in canine esophagus was achieved using Verteporfin and 630 nm light doses of 60, 80, 145, and 200 J/cm when delivered at 15, 30, 60, and 120 minutes after the drug injection, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".