Generation of Dendritic Cells Ex Vivo: Differences in Steady State Versus Mobilized Blood from Patients with Breast Cancer, with Lymphoma, and from Normal Donors
Bibliographic record
Abstract
Dendritic cells (DC) are potent antigen-presenting cells that are integral to the initiation of T cell immunity. The ability to culture these cells in vitro has allowed DC immunotherapy to be investigated as a mechanism of enhancing immune responses against various malignancies. We examined the optimal time for generating DC and compared DC generated from normal donors for allogeneic blood stem cell transplantation, or patient's with non-Hodgkin's lymphoma or breast cancer undergoing high-dose chemotherapy and autologous stem cell transplantation. Experiments were conducted to compare DC cultured prior to and post mobilization chemotherapy. Blood was obtained from consenting patients prior to granulocyte colony-stimulating factor (G-CSF) administration with (non-Hodgkin lymphoma and breast cancer) or without (normal donors) chemotherapy. A sample of apheresis product (AP) was obtained at the time of apheresis. DC were generated from peripheral blood mononuclear cells by culturing the adherent cells in the presence of interleukin-4 and granulocyte-macrophage colony-stimulating factor. Resultant DC were harvested and examined for yield, morphology, phenotype, and function. All cell populations yielded highly pure DC, as assessed by light microscopy and flow cytometry. The average cellular yield was significantly greater from AP than steady-state blood in paired and unpaired samples. Yield did not correlate with the percentage of CD14(+) cells, and it negatively correlated with CD34 counts. DC from breast cancer patients functioned significantly better than DC from lymphoma patients in a mixed lymphocyte reaction. These data suggest that the optimal timing of culturing DC is after mobilization, and that differences may exist in the functional capabilities of DC derived from different patient populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".