A method for rapid isolation of highly purified human monocytes using fully automated negative cell selection (36.25)
Bibliographic record
Abstract
Abstract The preparation of highly purified monocytes for experimentation has traditionally been difficult, requiring multiple steps and many hours of work. We report the successful development of a rapid negative selection method that enables the preparation of highly purified monocytes from human peripheral blood using EasySep® column-free immunomagnetic cell separation technology. A cocktail of monoclonal antibodies incorporated into tetrameric antibody complexes was used to crosslink unwanted cells in the sample to EasySep® magnetic particles. The tube containing the labelled cell suspension was then placed in an EasySep® magnet for 2.5 minutes. Unlabelled cells were recovered by pouring off the cell suspension while labelled unwanted cells were held to the walls of the tube by the magnetic field. The whole procedure was completed in 30 minutes and yielded CD14+CD16− monocyte fractions that were on average 90% pure with an average recovery above 60%. Stimulation of purified monocytes with GM-CSF, IL-4 and LPS led to efficient differentiation into dendritic cells (DC) as identified by the expression of the DC markers CD1a and CD83, and the loss of the monocyte marker CD14. Differentiated DC induced robust allogeneic CD4+ T cell proliferation, confirming that the isolated monocytes were fully competent to differentiate into functional DCs. This method was also fully automated using the RoboSep® cell separator.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".