Comparison of CD34 and Monocyte‐Derived Dendritic Cells from Mobilized Peripheral Blood from Cancer Patients
Bibliographic record
Abstract
Dendritic cells (DCs) are potent antigen-presenting cells that are integral to the initiation of T-cell immunity. Two cell types can be used as a source for generating DCs: monocytes and CD34(+) stem cells. Despite many investigations characterizing DCs, none have performed a direct paired comparison of monocyte and stem cell-derived DCs. Therefore, it is unclear whether one cell source has particular advantages over the other, or whether inherent differences exist between the two populations. We undertook the following study to determine if there were any differences in DCs generated from monocytes or CD34(+) cells from mobilized peripheral blood. DCs were generated by culturing the adherent cells (monocytes) in interleukin-4 and GM-CSF for 7 days, or by culturing nonadherent cells (CD34(+)) in the presence of GM-CSF and tumor necrosis factor alpha for 14 days. The resulting DCs were compared morphologically, phenotypically, functionally, and by yield. We could generate morphologically and phenotypically similar DCs. Differences were encountered when expression levels of some cell surface markers were examined (CD86, HLA-DR). There was no difference in how the DCs performed in a mixed lymphocyte reaction (p = .3). Further, no statistical difference was discovered when we examined cellular (DC) yield (p = .1); however, there was a significant difference when yield was normalized to the starting number of monocytes or CD34(+) cells (p = .016). Together, these data demonstrate that differences do exist between monocyte-derived DCs and CD34-derived DCs from the same cellular product (apheresis) from the same individual.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".