Paclitaxel and cisplatin as intravesical agents against non‐muscle‐invasive bladder cancer
Bibliographic record
Abstract
OBJECTIVES: To investigate the effects of cisplatin and paclitaxel against human bladder cancer cells in vitro, and to obtain both pharmacokinetic and pharmacodynamic data after intravesical administration in mice. MATERIALS AND METHODS: Six bladder cancer cell lines (J82, KU7, RT4, SW780, T24, UMUC3) were treated with various combined doses of both drugs and cell proliferation was evaluated 3 days later. In vivo, solutions of cisplatin and micellar paclitaxel were instilled transurethrally in female mice and pharmacokinetic data were acquired using high-performance liquid chromatography-mass spectrometry and atomic absorption methods. To obtain efficacy data, mice with orthotopic KU7-luc tumours were administered cisplatin and/or micellar paclitaxel intravesically, and the tumour burden quantified using bioluminescence imaging. RESULTS: In vitro, both cisplatin and paclitaxel potently decreased the proliferation of all cell lines tested, and in combination had an additive but not a synergistic effect. After intravesical instillation, mouse serum concentrations of cisplatin and paclitaxel were in the low microgram/millilitre range and bladder tissue concentrations achieved were 82 and 241 microg/g, respectively. Similar drug levels were reached using combined therapy. In vivo, all chemotherapeutic agents significantly inhibited bladder tumour growth, with the best results for combined therapy and micellar paclitaxel alone. However, there was toxicity in the combined treatment arm. CONCLUSIONS: Both cisplatin and paclitaxel were absorbed at effective amounts into bladder tissues. As intravesical agents, paclitaxel had slightly stronger anticancer potency than cisplatin. Due to increased adverse events, caution should be exercised when combining both cisplatin and paclitaxel intravesically.
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 | 0.000 | 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".