CD8+ T Cell Immunity Against a Tumor/Self-Antigen Is Augmented by CD4+ T Helper Cells and Hindered by Naturally Occurring T Regulatory Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CD4(+) T cells control the effector function, memory, and maintenance of CD8(+) T cells. Paradoxically, we found that absence of CD4(+) T cells enhanced adoptive immunotherapy of cancer when using CD8(+) T cells directed against a persisting tumor/self-Ag. However, adoptive transfer of CD4(+)CD25(-) Th cells (Th cells) with tumor/self-reactive CD8(+) T cells and vaccination into CD4(+) T cell-deficient hosts induced autoimmunity and regression of established melanoma. Transfer of CD4(+) T cells that contained a mixture of Th and CD4(+)CD25(+) T regulatory cells (T(reg) cells) or T(reg) cells alone prevented effective adoptive immunotherapy. Maintenance of CD8(+) T cell numbers and function was dependent on Th cells that were capable of IL-2 production because therapy failed when Th cells were derived from IL-2(-/-) mice. These findings reveal that Th cells can help break tolerance to a persisting self-Ag and treat established tumors through an IL-2-dependent mechanism, but requires simultaneous absence of naturally occurring T(reg) cells to be effective.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it