A New Dynamic Model of CD8+ T Effector Cell Responses via CD4+ T Helper-Antigen-Presenting Cells
Bibliographic record
Abstract
A long-standing paradox in cellular immunology has been the conditional requirement for CD4(+) Th cells in priming of CD8(+) CTL responses. We propose a new dynamic model of CD4(+) Th cells in priming of Th-dependent CD8(+) CTL responses. We demonstrate that OT II CD4(+) T cells activated by OVA-pulsed dendritic cells (DC(OVA)) are Th1 phenotype. They acquire the immune synapse-composed MHC II/OVAII peptide complexes and costimulatory molecules (CD54 and CD80) as well as the bystander MHC class I/OVAI peptide complexes from the DC(OVA) by DC(OVA) stimulation and thus also the potential to act themselves as APCs. These CD4(+) Th-APCs stimulate naive OT I CD8(+) T cell proliferation through signal 1 (MHC I/OVAI/TCR) and signal 2 (e.g., CD54/LFA-1 and CD80/CD28) interactions and IL-2 help. In vivo, they stimulate CD8(+) T cell proliferation and differentiation into CTLs and induce effective OVA-specific antitumor immunity. Taken together, this study demonstrates that CD4(+) Th cells carrying acquired DC Ag-presenting machinery can, by themselves, efficiently stimulate CTL responses. These results have substantial implications for research in antitumor and other aspects of immunity.
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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 | 0.001 |
| 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) | 0.006 | 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".