Fully immunomagnetic isolation of untouched purified lymphocyte populations directly from whole blood without the need for red blood cell sedimentation, hypotonic lysis or density gradient centrifugation in just 25 minutes. (TECH2P.871)
Bibliographic record
Abstract
Abstract Human whole blood is made up of 93-96% red blood cells (RBCs), 4-7% platelets and 0.1-0.2% leukocytes consisting mainly of granulocytes, lymphocytes and monocytes. Due to the low frequency of lymphocyte subsets within whole blood, their isolation typically requires a pre-processing step such as Lymphoprep™ or hypotonic lysis to deplete RBCs prior to cell isolation. We have therefore developed a new, fully immunomagnetic method for the negative selection of untouched cells directly from whole blood, without the need for any pre-processing steps. The EasySep™ Direct procedure involves labeling RBCs, platelets and unwanted leukocytes present in human whole blood with antibody complexes and magnetic particles. The magnetically labelled unwanted cells are separated from the untouched desired cells using an EasySep™ magnet by simply pouring or pipetting the desired cells into a new tube. In just 25 minutes, purities of 92-99% total lymphocytes, 93-98% T cells, 87-97% CD4+ T cells, 75-91% CD8+ T cells, 91-99% B cells, 87-98% naïve B cells and 65-84% NK cells can be achieved with little to no residual RBC contamination. The isolation of cells directly from whole blood will enable rapid access to highly purified cells immediately ready for downstream functional assays with minimal sample handling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".