Priming with Dendritic Cells Can Generate Strong Cytotoxic T Cell Responses to Chronic Myelogenous Leukemia Cells In Vitro
Bibliographic record
Abstract
Dendritic cells (DC) are antigen-presenting cells that can elicit potent antigen-specific responses. Since the development of techniques to cultivate these cells from peripheral blood, there has been a great deal of interest in their use in immunotherapeutic strategies. Here we show that morphologically, phenotypically, and functionally characteristic DC can be generated in vitro from peripheral blood mononuclear cells (PBMC) isolated from frozen apheresis product (AP) of cancer patients. These DC, when pulsed with whole-tumor lysate, protein, or RNA from a chronic myelogenous leukemia (CML) cell line, can induce anti-CML specific cytotoxicity in vitro by autologous cytotoxic T lymphocytes (CTL). RNA and protein-pulsed DC were more effective than lysate-pulsed DC at inducing cytotoxicity at low effector:target (E:T) ratios. These results were comparable to those obtained when fresh healthy peripheral blood was used as the source of PBMC, indicating that neither the malignant state of the patient nor the storage period detrimentally affected the generation or functionality of DC. CML cells were found to increase their level of MHC class I expression after exposure to CTL and pulsed DC thereby becoming better targets. These investigations lend support for the utilization of DC to generate anti-tumor responses in CML.
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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) | 0.002 | 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".