CD4 <sup>+</sup> T cells support cytotoxic T lymphocyte priming by controlling lymph node input
Bibliographic record
Abstract
Rapid induction of CD8(+) cytotoxic T lymphocyte (CTL) responses is critical to combat acute infection with intracellular pathogens. CD4(+) T cells help prime antigen-specific CTLs in secondary lymphoid organs after infection in the periphery. Although the frequency of naïve precursors is very low, the immune system is able to efficiently screen for cognate CTLs through mechanisms that are not well understood. Here we examine the role of CD4(+) T cells in early phases of the immune response. We show that CD4(+) T cells help optimal CTL expansion by facilitating entry of naïve polyclonal CD8(+) T cells into the draining lymph node (dLN) early after infection or immunization. CD4(+) T cells also facilitate input of naïve B cells into reactive LNs. Such "help" involves expansion of the arteriole feeding the dLN and enlargement of the dLN through activation of dendritic cells. In an antigen- and CD40-dependent manner, CD4(+) T cells activate dendritic cells to support naïve lymphocyte recruitment to the dLN. Our results reveal a previously unappreciated mode of CD4(+) T-cell help, whereby they increase the input of naïve lymphocytes to the relevant LN for efficient screening of cognate CD8(+) T cells.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".