Regulators of T‐cell memory generation: TCR signals versus CD4<sup>+</sup> help?
Bibliographic record
Abstract
I n the event of pathogen entry, antigen (Ag)-specific naive CD8 + T cells undergo activation and rapid clonal expansion that results in the generation of millions of effector CD8 + cytotoxic T lymphocytes (CTLs), and subsequently, a small cohort of memory cells.This dynamic event is largely controlled by signaling provided by the immunological synapse, proinflammatory cytokines and CD4 + T cells.1,2 However, how these signals contribute to the generation of heterogeneous populations of effector and memory cells from a relatively homogeneous and rare naive CD8 + T-cell population is still not clearly understood.Two recent reports in Blood from Smith-Garvin et al. 3 and Wiehagen et al. 4 now show that altered T-cell receptor (TCR) signals can affect differentiation, heterogeneity, and the functions of effector and memory cells, supporting growing evidence that the strength of TCR signals, at least in part, determines the fate of CD8 + T-cell lineage choices.To verify whether altered TCR signals impact effector and memory CD8 + T-cell differentiation fates, Smith-Garvin et al. use genomic knock-in mice that express tyrosine to phenylalanine mutations in SH2 domaincontaining leukocyte phosphorylation of 76 kDa (SLP-76), and a well-defined infectious model, Armstrong strain of lymphocytic choriomeningitis virus (LCMV).On the other hand, Wiehagen et al. 4 used conditional knockout mice where they ablated the SLP-76 gene by administering estrogen analog,
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".