Multifunctional cytokine production reveals functional superiority of memory CD4 T cells
Bibliographic record
Abstract
T cell protective immunity is associated with multifunctional memory cells that produce several different cytokines. Currently, our understanding of when and how these cells are generated is limited. We have used an influenza virus mouse infection model to investigate whether the cytokine profile of memory T cells is reflective of primary responding cells or skewed toward a distinct profile. We found that, in comparison to primary cells, memory T cells tended to make multiple cytokines simultaneously. Analysis of the timings of release of cytokine by influenza virus-specific T cells, demonstrated that primary responding CD4 T cells from lymphoid organs were unable to produce a sustained cytokine response. In contrast CD8 T cells, memory CD4 T cells, and primary responding CD4 T cells from the lung produced a sustained cytokine response throughout the restimulation period. Moreover, memory CD4 T cells were more resistant than primary responding CD4 T cells to inhibitors that suppress T cell receptor signaling. Together, these data suggest that memory CD4 T cells display superior cytokine responses compared to primary responding cells. These data are key to our ability to identify the cues that drive the generation of protective memory CD4 T cells following infection.
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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) | 0.002 | 0.000 |
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".