Fast, easy and column-free procedure for the isolation of human memory B Cells (45.14)
Bibliographic record
Abstract
Abstract Human memory B cells have the unique ability to rapidly proliferate and differentiate when re-exposed to the same antigen. They can be distinguished from naïve B cells by the presence of somatic hypermutations in their Ig-V region sequences. CD27 is widely used as a marker of memory B cells because its expression correlates with the presence of such mutations. Several CD27+ memory subsets collectively make up 20-60% of peripheral B cells. Using a two-step EasySep™ method, memory CD27+ B cells are isolated from fresh or previously frozen peripheral blood nucleated cells. First, non-B cells are targeted for depletion with dextran-coated magnetic particles using a cocktail of tetrameric antibody complexes (TAC). Labeled cells are separated in an EasySep™ magnet without the use of columns and pre-enriched, unlabeled B cells are collected. Next, CD27+ B cells are selected from the pre-enriched fraction using TAC recognizing CD27 and dextran-coated magnetic particles. Labeled cells are separated and remain in the tube in the magnet while unwanted cells are poured off. The unwanted cell fraction may be used to obtain naïve B cells. The selection steps can be fully automated using RoboSep™. Starting with a frequency of 4.1 ± 1.2% CD19+CD27+ B cells, purities of 93.0 ± 3.4% (n=14) can be obtained. Isolation of memory B cells from human samples is increasingly important for the investigation of B cell signaling pathways and regulation mechanisms central to a robust immune response.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.020 |
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".