A simple one-step method for isolating highly purified plasmacytoid dendritic cells from human peripheral blood (78.33)
Bibliographic record
Abstract
Abstract Plasmacytoid dendritic cells (pDC) and myeloid DC (mDC) are the two major human dendritic cell populations. Each subset comprises only a small fraction of peripheral blood mononuclear cells (PBMC) and current protocols for their isolation are time consuming, involve multiple steps and often require special equipment. We have developed a rapid and efficient method for the isolation of pDCs from normal blood that yields excellent cell purities and recoveries. Briefly, PBMCs are isolated by Ficoll-Paque PLUS density gradient sedimentation and pDCs are isolated using immuno-magnetic, column-free, negative selection (EasySep®). Our EasySep® technology involves specifically labeling unwanted cells with a cocktail of bi-specific tetrameric antibody complexes and dextran-coated magnetic particles. Using a hand-held magnet, unwanted cells can then be easily removed from the unlabeled pDCs. Flow cytometric assessment of EasySep® isolated pDCs (Lin-, HLA-DR+, BDCA-4+) demonstrate purities of 93.8% ± 3.8% with cell recoveries of 65.6% ± 16.2% (n=9). Using our RoboSep® cell separator, the entire separation procedure can be fully automated with equivalent cell purities and recoveries. A.I.K is supported by an NSERC Industrial R&D Fellowship
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".