Column-free methods for the fast and versatile isolation of highly purified, functional and expandable human regulatory T cell populations (LL3-3)
Bibliographic record
Abstract
Learn how to rapidly and efficiently obtain the highest purity human regulatory T cells (Tregs) directly from whole blood or PBMC in less time with STEMCELL's new range of products. This seminar will demonstrate how you can get your desired population of Tregs based on expression of CD25, CD127, and/or CD49d, while saving hours on the isolation procedure. STEMCELL's Complete Kits for the isolation of human Tregs reduce cell isolation time by combining two of STEMCELL's rapid and simple cell separation platforms, RosetteSep® and EasySep®. The isolation of Tregs is now a simple two-step process: pre-enrichment of CD4+, CD4+CD127low or CD4+CD49d−CD127low cells by negative selection with RosetteSep®, followed by positive selection of CD25high cells with EasySep®. The entire procedure from whole blood or buffy coat to purified Tregs takes only three hours, unlike other magnetic separation methods that take up to five hours to complete. The column-free kits are easy to use and are gentle on cells, providing highly functional Tregs that are immediately ready for use in downstream assays or expanded in vitro while maintaining their functionality. Depending on the desired Treg population, purities of up to 90% CD4+CD25highFOXP3+ human Tregs can be achieved.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.054 |
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".