<scp>CD4</scp> <sup>+</sup> T Cells Mediate Dendritic Cell Licensing to Promote Multi‐Antigen Anti‐Leukemic Immune Response
Bibliographic record
Abstract
BACKGROUND: Single antigen (Ag)-targeted immunotherapies for acute lymphoblastic leukemia (ALL) are highly effective; however, up to 50% of patients relapse after these treatments. Most of these relapses lack target Ag expression, suggesting targeting multiple Ags would be advantageous. MATERIALS & METHODS: The multi-Ag immune responses to ALL induced by transducing cell lines with xenoAgs green fluorescent protein and firefly luciferase was elucidated using flow cytometry, ELISA, and ELISpot assays. RESULTS: In our model, leukemia responsiveness correlates with in vivo CD4+ T cell activation and DC maturation, supporting a role for DC licensing. In contrast, tolerance is characterized by in vivo increased expression of negative immune checkpoints (IC) which may suppress rather than license DC. In vitro assays confirm the ability of CD4+ T cells from leukemia-responsive mice to promote robust maturation of naïve bone marrow DC in the presence of non-immunogenic leukemia antigens. CONCLUSION: Together these findings support a CD4+ T cell-mediated mechanism of DC licensing to promote multi-Ag immune responses that may augment current targeted immunotherapies and avoid relapses in treated children with ALL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".