A rapid method for the isolation of Eosinophils without Ficoll or RBC Lysis (38.1)
Bibliographic record
Abstract
Abstract Eosinophils typically comprise 1–5% of blood leucocytes in non-allergic, healthy humans. Allergic and asthmatic reactions result in activation of eosinophils with resultant release of cytoplasmic granules, cytokines and lytic enzymes. Eosinophils are also mediators of responses against parasitic infections. Many protocols for their isolation stress the avoidance of ammonium chloride lysis as this interferes with eosinophil antigen processing, cytokine responses and alters cell morphology. We describe a rapid and simple method for the enrichment of eosinophils from normal blood that does not require Ficoll or lysis steps and yields good purity and recovery. Blood was collected with heparin and RBC were removed by HetaSep sedimention. The eosinophils were then enriched using immunomagnetic, column-free negative selection (EasySep®). Briefly, cells were labeled with a cocktail of antibodies targeting unwanted cells. These were then coupled to magnetic nanoparticles and the sample was placed in a magnet. The labeled cells were thus removed leaving unlabeled eosinophils. The entire separation procedure can be automated with a pipetting robot (RoboSep®). Purity of eosinophils assessed by cytospin and Wright’s stain was 98–99%. By flow cytometry (FACS) eosinophils were defined as CD45+CD66b+ and CD16 negative with low side scatter. Final purity assessed by FACS was 94 ± 3%. Recovery averaged 69 ± 21 (n=11).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".