11th Biennial Canadian Neuro-Oncology Meeting
Bibliographic record
Abstract
Immunotherapy has been considered as an effective adjuvant for cancer therapy, however, its eff i c a c y, such as systemic using lymphokine-activated killer (LAK) cells for glioma patients, was unsatisfactory.The obstacle could be CNS an immunologically privileged site or poor anti-glioma activity of the LAK cells.Thus, local delivery of immune active cells has theoretical advantage.In order to apply more active immune cells for local treatment of gliomas, we have generated cytokine-induced killer (CIK) cells by incubation of peripheral blood monocytes from glioma patient, and the anti-tumor activity of the CIK cells were tested.The activity of the CIK cells against glioma cells, at effector:target (E:T) cells ratio of 25:1, 50:1 and 100:1, as expressed by cell kill rate, was 37.12%, 60.69% and 71.08%, respectively.At E:T ratio of 50:1, the tumor cell kill rate of CIK cells on the 10, 18, and 26 days was 52.39%, 67.31% and 58.89%, respectively.The antitumor activity, at E:T ratio of 50:1, of CIK cells (67.31%) was significantly higher than that of LAK cells (55.76%).Our current results indicate that CIK cells derived from glioma patient could be efficiently employed as an adjuvant immunotherapeutic strategy for local treatment of glioma patient.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.166 | 0.045 |
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".