Effects of TOOKAD-PDT on canine prostates pre-treated with ionizing radiation
Bibliographic record
Abstract
PDT in prostate cancer will likely be implemented clinically with patients who have failed prior ionizing radiation therapy (RT). The current study is to develop an in vivo model to evaluate the effects of PDT on prostatic tissue after RT. To produce a physiological and anatomical environment in prostate similar to that in patients who have failed RT, canine prostates (n=4) were subjected to a definitive course of ionizing radiation therapy (2.7 Gy x 20 fractions) 5 to 6 months prior to PDT. A laparotomy was performed to expose the prostate for PDT. Second generation photosensitizer Tookad (Palladium-Bacteriopheophorbide, Steba Biotech, The Netherlands) acts primarily on tissue vasculature and is very effective in destroying normal prostatic tissue, as shown by our prior studies. Due to the extremely fast clearance of the photosensitizer, interstitial light irradiation (760 nm, 50-200 J/cm, 150 mW/cm from a 1 cm diffuser fiber) was delivered 4 minutes after the onset of Tookad infusion (i.v. 2.5 mg/ml, 2 mg/kg, total infusion time 10 min). The prostates were harvested for histopathology one week after PDT. At one week, the lesions were characterized by acute hemorrhagic necrosis with patchy sub-capsular hyperemia and edema. The maximum lesion diameter for 50, 100 and 200 J/cm PDT was approximately 15, 20 and 28 mm, respectively. The lesion size is well correlated with light fluence and comparable to that in prostates treated with identical PDT doses but without prior-RT. Under light-microscopy, the PDT induced necrosis is clearly distinguishable from the radiation induced fibrosis. No urethral lesions were observed. Dyer’s Verhoeff stain showed the loss of stromal connective tissue and the acinar collagen in the PDT treated area. There was no noticeable damage on the bladder or underlying colon section. In conclusion, Tookad-PDT can effectively destroy prostate tissue with prior-RT induced fibrosis, thus, may provide an alternative modality for those prostate-cancer patients who have failed RT.
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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 | 0.001 |
| 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".