A simple new method for negative enrichment of monocytes from mouse blood and bone marrow (134.39)
Bibliographic record
Abstract
Abstract The mononuclear phagocyte system is comprised of tissue macrophages, dendritic cells, blood monocytes and their bone marrow (BM) progenitors. Transgenic mouse models have recently provided insight into the biology of monocytes in vivo. However a general method for isolating monocytes from mice is needed to better study their functions. We describe a rapid and simple method for the enrichment of monocytes from mouse BM and peripheral blood that does not require a density gradient and yields high purity and recovery. BM was harvested from femurs and tibia by crushing the bones. Blood was collected with heparin and red blood cells were removed by ammonium chloride lysis. The monocytes were then enriched using immunomagnetic, column-free negative selection (EasySep®). Briefly, unwanted cells were specifically labeled with dextran-coated magnetic particles using a cocktail of bispecific tetrameric antibody complexes. The sample was placed in a magnet and the supernatant containing unlabeled monocytes was collected. The separation procedure can be automated with a pipetting robot (RoboSep®). Purity of CD11b+Ly6G- cells as assessed by flow cytometry ranged from 80-93% for BM and 92-98% for blood with recovery of 46 ±11 % (n=38) and 25 ±10 % (n=20) respectively. This protocol will provide easy access to monocytes, enriched from peripheral blood and BM, for further studies of immune and inflammatory responses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".