EasySep&[trade] T75 Magnet: A Novel Magnetic Platform for Large-Volume Cell Isolation from Whole Blood and Leukapheresis Packs
Bibliographic record
Abstract
Abstract Isolating cells from large volume-samples such as leukapheresis or whole blood bags is a common procedure that precedes many immunological studies. Working with large volumes can be challenging as the sample often needs to be split and processed in parallel, which can be tedious and may delay downstream studies. The EasySep™ T75 Magnet simplifies cell separation procedures when processing up to 225 mL of leukapheresis sample or 125 mL of whole blood, and is based on column-free immunomagnetic EasySep™ cell isolation technology. Negative selection protocols have been optimized for isolation of human T cells (purity: 95.3 ± 2.5%, n=5), CD4+ T cells (purity: 97.2 ± 1.4%, n=4), CD8+ T cells (purity: 91.9 ± 2.2%, n=5), and monocytes (purity: 91.5 ± 1.0%, n=3). Positive selection protocols have been optimized for isolation of CD3+ cells (purity: 95.4 ± 2.9%, n=9), CD4+ cells (purity: 88.8 ± 3.2%, n=3), CD8+ cells (purity: 95.1 ± 2.4%, n=6), and CD14+ cells (purity: 96.2 ± 0.7%, n=3). Protocols for depletion of red blood cells (RBCs) (residual RBCs: 2.5 ± 1.7%, n=5) and enrichment of peripheral blood mononuclear cells from whole blood (purity: 99.2 ± 0.5%, n=5) have also been developed. Notably, these protocols can be completed in 20 – 26 minutes, making the EasySep™ T75 Magnet the fastest and simplest method for isolating highly purified immune cells from large-volume whole blood and leukapheresis samples.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".