A new dynamic model of three cell interactions by CD4+ T-licensed DCs and DC-primed CD4+ T cells (100.38)
Bibliographic record
Abstract
Abstract CD8 cytotoxic T lymphocytes (CTLs) kill pathogen-infected cells. Recent in vivo imaging study suggested that presence of DC-CD4 T clusters during CTL priming are essential for T-cell memory, challenging independent roles for the DCs or the CD4 T-cells that dissociate following DC-CD4 T interactions. We have developed a novel in vivo experimental system based on the rapid inducible ablation of diphtheria toxin receptor (DTR) transgenic-DCs and antibody-mediated depletion of CD4 and CD8+ T cells in mice to create DC-CD4 T interactions in vivo first, followed by killing of either CD4 T-licensed DCs or DC-primed CD4 T cells. Later, adoptively transferring naive polyclonal CD8 T cells facilitated their interactions with either licensed-DCs or primed-CD4 T cells, thereby revealing their respective roles in CTL immunity. Our results provide direct in vivo evidence that CD4 T-cells and DCs, once activated by DC-CD4 T interactions, can stimulate CTL responses and programme memory on CTLs independent of DC-CD4 T clusters, uncovering the co-existence of direct and indirect mechanisms of T-cell help for CTLs by licensed DCs and primed CD4 T cells in non-inflammatory situations. In situations in which CTL immunity is heavily dependent on CD4 T-cell help, our findings suggest a new dynamic model of three cell interactions in CTL responses, that dissociated stage of primed CD4 T-cells and licensed DCs represent crucial “immune-intermediates” for CTL responses and memory development.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".