Antitumor Efficacy of Intravesical BCG, Gemcitabine, Interferon-α and Interleukin-2 as Mono- or Combination-Therapy for Bladder Cancer in an Orthotopic Tumor Model
Bibliographic record
Abstract
OBJECTIVE: To reduce adverse effects and improve efficacy of intravesical BCG for bladder cancer, alternative treatment options were investigated in an orthotopic rat tumor model. METHODS: Superficial bladder cancer was established in syngeneic female rat bladders by instillation of AY-27 cells. Animals were randomly assigned to treatment groups including dose escalation of intravesical BCG with or without interferon-α (IFN-α) or interleukin-2 (IL-2); or graded doses of gemcitabine alone; or BCG plus gemcitabine. Treatments were given twice weekly for 3 weeks. Rats in control groups received saline instillations. Treatment response was monitored by animals' well-being, survival days, tumor growth inhibition, and histological examination at necropsy. RESULTS: Rats receiving monotherapy with intravesical BCG, gemcitabine, or IFN-α, attained significantly better survival and tumor reduction compared with control (P = 0.002; 0.001; 0.002, respectively, Log-rank Test). A dose-dependent treatment response was observed in animals with established bladder tumor receiving escalated BCG instillations. Only high-dose BCG significantly improved animal survival. Although high-dose BCG plus gemcitabine or IFN-α did not increase benefit over monotherapies, low-dose BCG plus IL-2 did show improved efficacy (P = 0.01). CONCLUSION: Intravesical monotherapies with gemcitabine and IFN-α were as effective as BCG for treatment of early non-muscle-invasive urothelial bladder cancer in this immune competent rat model. Combining these agents with high-dose BCG did not further increase efficacy. However, combining low-dose BCG with IL-2 enhanced BCG effectiveness.
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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".