Intraperitoneal Alpha-Radioimmunotherapy of Advanced Ovarian Cancer in Nude Mice using Different High Specific Activities
Bibliographic record
Abstract
Background: The aim of this stu dy was to investigate the therapeutic efficacy of advanced ovarian cancer in mice, using alpha- radioimmunotherapy with different high specific activities. The study was performed using the monoclonal antibody (mAb) MX35 F (ab') 2 labeled with the α-particle emitter 211 At . Methods: Animals were intraperitoneally inoculated with >= 1 × 10 7 cells of the ovarian cancer cell line NIH:OVCAR-3. Four weeks later 9 groups of animals were given 25, 50, or 400 kBq 211 At-MX35 F(ab') 2 with specific activities equal to 1/80, 1/500, or 1/1200 ( 211 At atom/number of mAbs) for every activity level respectively ( n = 10 in each group). As controls, animals were given PBS or unlabeled MX35 F (ab') 2 in PBS ( n = 10 in each group). Eight weeks after treatment the animals were sacrificed and the presence of macroscopic tumors was determined by meticulous ocular examination of the abdominal cavity. Cumulated activity and absorbed dose calculations on tumor cells and tumors were performed using in house developed program. Specimens for scanning electron-microscopy analysis were collected from the peritoneum at the time of dissection. Results: Summing over the different activity levels ( 25, 50, and 400 kBq 211 At -MX35 F(a b') 2) t he number of animals with macroscopic tumors was 13, 17, and 22 ( n = 30 for each group) for the specific activities equal to 1/80, 1/500, or 1/1200, respectively. Logistic-regression analysis showed a significant trend that higher specific activity means less probability for macroscopic tumors ( P = 0.02). Conclusions: Increasing the specific activity indicates a way to enhance the therapeutic outcome of advanced ovarian cancer, regarding macroscopic tumors. Further studies of the role of the specific activity are therefore justified. World J Oncol. 2010;1(3):101-110 doi: https://doi.org/10.4021/wjon2010.05.208w
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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".