Differentiation and Functions of T Cell Subsets
Bibliographic record
Abstract
The Tc1 and Tc2 subsets of CD8+ T effector cells secrete different patterns of cytokines, but have similar functions, including perforin- and Fas-dependent cytotoxicity, and induction of delayed type hypersensitivity (DTH) reactions involving oedema and granulocytic infiltration. The characteristic cytokines of Tc1 (gamma-interferon) and Tc2 (interleukins 4 and 5) are expressed in vivo during the DTH reaction. Tc1 cells that are deficient in cytokine synthesis also induce similar levels of DTH, supporting the lack of correlation between CD8+ T cell cytokine patterns and DTH. CD8+ T cells often produce lower cytokine levels than CD4 cells because the CD8 cells kill their antigen-presenting cells before full stimulation can occur. This effect can be counteracted by increasing the frequency of stimulation, or using perforin-deficient T cells. A multiparameter analysis of cytokine effects on CD8+ T cell differentiation has been initiated, on the basis of the principle that normal immune responses involve complex cytokine mixtures. All combinations of seven cytokines were tested. In some combinations, the combined effect could not have been predicted from individual cytokine functions. Conditions were identified in which each of interleukins 4, 10 and 12 could have opposite effects on CD8+ T cell differentiation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".