Rapid, column-free two-step procedure for the enrichment of human Th17 Cells from peripheral blood (56.13)
Bibliographic record
Abstract
Abstract Human Th17 cells play a critical role in the regulation of autoimmune disease and antimicrobial host defenses through production of pro-inflammatory cytokines IL17A and IL-17F. They can be characterized by expression of surface receptors including IL-23R, CCR6, CD161 and lineage-specific transcription factor RORC, but lack of CXCR3 expression. The major disadvantages of current isolation methods are the requirement for in vitro stimulation and the co-secretion of IFN-γ. We have developed a 2-step EasySepTM immunomagnetic column-free method for the enrichment of CD4+CXCR3-CCR6+ cells from fresh peripheral blood nucleated cells (PBNC). First, non-CD4 T cells and CXCR3+ cells are targeted for depletion using dextran-coated magnetic particles and a cocktail of antibody complexes. Labeled cells are separated using an EasySepTM magnet, and pre-enriched CD4 T-cells are poured off. Next, CCR6+ cells are positively selected from the pre-enriched fraction. The procedure can be automated using RoboSepTM. Starting with a frequency of 5 ± 2% CD4+CXCR3-CCR6+ cells, purities of 94 ± 3% (n=10) can be obtained. The resulting cells produce high levels of IL-17 with minimal IFN-γ when measured by ELISA and intracellular staining and increased RORC mRNA expression over total CD4 T cells and CD4+CXCR3+ T cells (Th1 cells). Enrichment of unstimulated human Th17 cells enables the investigation of adaptive immune responses and regulation mechanisms required for the development of future therapies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.016 |
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".