A Rapid New Procedure for Basophil Isolation from Human Peripheral Blood
Bibliographic record
Abstract
Basophil research has been limited by the difficulty in isolating pure basophils in large numbers. Typically, basophils comprise < 1% of peripheral blood leucocytes in non‐allergic, healthy humans. Current protocols for their isolation are time consuming involving multiple steps and often special equipment. Prolonged manipulation of basophils can lead to undesirable activation and spontaneous histamine release. We describe a rapid (within 1.5 hours) and simple method for the enrichment of basophils from normal blood that does not require Ficoll or lysis steps and yields good purity and recovery. Blood was collected with heparin and red blood cells were removed by HetaSep sedimention. The basophils 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 labeled cells were then removed using a magnet leaving unlabeled basophils. The entire separation procedure can be automated with a pipetting robot (RoboSep®). Final basophil purity range as assessed by flow cytometry was 92–99% CD45+IgE+ CD123+. May‐Grunwald stained cytospins showed typical morphology and characteristic blue‐black granules. Expression of cell‐surface CD203c was similar between whole blood and enriched basophils. Recovery averaged 49 ± 17 % (n=7).
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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) | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".