Bibliographic record
Abstract
Peripheral αβTCR(+)CD3(+)CD4(-)CD8(-) NK1.1/CD56(-) double-negative (DN) Treg cells are a relatively rare subset of regulatory cells found in both humans and mice, typically comprising less than 5% of the total peripheral T-cell pool. Numerous studies have shown that DN Tregs can inhibit CD4(+) and CD8(+) T-cell responses in vitro and in vivo using a variety of model systems [Zhang et al., Nature Medicine 6:782, 2000; Young et al., Blood 100:3408, 2002; Ford et al., Experimental Medicine 196:261, 2002; Young et al., Journal of Immunology 171:134, 2003; Ford et al., European Journal of Immunology 37:2234, 2007; Zhang et al., Blood 109:4071, 2007; Fischer et al., Blood 105:2828, 2005]. This chapter describes published methods for the phenotypic identification of DN Tregs, their isolation from secondary lymphoid organs of mice or human peripheral blood, activation and expansion, and assays for their ability to suppress T-cell proliferation, induce apoptosis, and promote tolerance to allografts in vivo.
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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) | 0.000 | 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) | 0.009 | 0.004 |
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".