Evaluating a combination treatment of NK cells and reovirus against bladder cancer cells using an in vitro assay to simulate intravesical therapy
Bibliographic record
Abstract
Intravesical treatment using either reovirus or natural killer (NK) cells serves as an efficient strategy for the treatment of bladder cancer cells (BCCs); however, corresponding monotherapies have often shown modest cytotoxicity. The potential of a locoregional combination using high-dose reovirus and NK cell therapy in an intravesical approach has not yet been studied. In this study, we evaluated the effectiveness of reoviruses and expanded NK cells (eNK) as potential strategies for the treatment of bladder cancer. The anti-tumor effects of mono-treatment with reovirus type 3 Dearing strain (RC402 and RP116) and in combination with interleukin (IL)-18/-21-pretreated eNK cells were investigated on BCC lines (5637, HT-1376, and 253J-BV) using intravesical therapy to simulate in vitro model. RP116 and IL-18/-21-pretreated eNK cells exhibited effective cytotoxicity against grade 1 carcinoma (5637 cells) when used alone, but not against HT-1376 (grade 2 carcinoma) and 253J-BV cells (derived from a metastatic site). Notably, combining RP116 with IL-18/-21-pretreated eNK cells displayed effective cytotoxicity against both HT-1376 and 253J-BV cells. Our findings underscore the potential of a combination therapy using reoviruses and NK cells as a promising strategy for treating 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".