Preventing Bladder Tumor Implantation With Photodynamic Therapy in a Rat Model Mimicking Post-Fluorescence Guided Transurethral Resection
Bibliographic record
Abstract
PURPOSE: Fluorescence guided transurethral resection has gained acknowledgment from the urological community and it is progressively becoming more applied. It has been shown to decrease the recurrence rate of nonmuscle invasive bladder cancer due to incomplete resection due to lack of visualization. The implantation of viable tumor cells seeded during transurethral resection is another reason for recurrence. We investigated whether applying photodynamic therapy on sensitized tumor cells would decrease the amount of viable intraluminal cells and tumor cell implantation. MATERIAL AND METHODS: Two models were designed to mimic the situation after fluorescence guided transurethral resection, including partly or fully de-epithelialized bladders and circulating tumor cells loaded with protoporphyrin IX. Photodynamic therapy was performed. Controls consisted of no drug with no light, light only and drug only. Immediately after photodynamic therapy the intravesical contents were retrieved and clonogenic assays were performed on cells. Bladders were harvested 10 days after cell administration and subjected to pathological analysis. RESULTS: In the photodynamic therapy and control groups tumor volume was proportional to the instilled cell load. Clonogenic assays showed that viable cells were decreased a tenth of the initial administered amount. Tumor implantation decreased to less than a fifth of control values. CONCLUSIONS: Photodynamic therapy can effectively decrease the amount of viable tumor cells in the bladder lumen. This results in a significant decrease in tumor implantation. This technique could possibly be used to further decrease the recurrence rate of nonmuscle invasive bladder cancer.
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 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 | 0.001 |
| 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".