Interleukin‐12 immunotherapy in a murine model of leukemia
Bibliographic record
Abstract
To improve the efficacy of interleukin‐12 (IL‐12) therapy using different dosage and administration protocols than those previously used in clinic, a model of murine acute lymphoblastic leukemia (ALL) was developed employing the well‐characterized cell line 70Z/3. Administration of low dose IL‐12 by two separate methods each led to efficient tumor clearance but by way of distinct mechanisms. Direct IL‐12 administration resulted in tumor rejection by the canonical pathway, ultimately mediated by CD8 + cytotoxic T lymphocytes (CTL). However, when 70Z/3 cells themselves were transduced with a novel lentiviral vector system to express IL‐12, in vivo depletion experiments demonstrated that a CD4 + cell population alone was absolutely required for tumor rejection but not CD8 + CTLs. We also observed that the outcome of therapy is dependent more on the number and quality of cellular interactions than on the absolute amount of IL‐12 administered. An example of such a cellular interaction might be that of an IL‐12 secreting tumor cell with an antigen presenting cell, a dendritic cell or macrophage, which subsequently interacts with a CD4 + cell to induce an effector response. For this interaction to be effective, the tumor cell must produce IL‐12 above a certain threshold. Overall, our studies highlight that the method of IL‐12 administration can have a significant impact on the outcome of treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".