Overcoming CD4 Th1 Cell Fate Restrictions to Sustain Antiviral CD8 T Cells and Control Persistent Virus Infection
Bibliographic record
Abstract
Viral persistence specifically inhibits CD4 Th1 responses and promotes Tfh immunity, but the mechanisms that suppress Th1 cells and the disease consequences of their loss are unclear. Here, we demonstrate that the loss of CD4 Th1 cells specifically leads to progressive CD8 T cell decline and dysfunction during viral persistence. Therapeutically reconstituting CD4 Th1 cells restored CD4 T cell polyfunctionality, enhanced antiviral CD8 T cell numbers and function, and enabled viral control. Mechanistically, combined interaction of PD-L1 and IL-10 by suppressive dendritic cell subsets inhibited new CD4 Th1 cells in both acute and persistent virus infection, demonstrating an unrecognized suppressive function for PD-L1 in virus infection. Thus, the loss of CD4 Th1 cells is a key event leading to progressive CD8 T cell demise during viral persistence with important implications for restoring antiviral CD8 T cell immunity to control persistent viral infection.
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 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 | 0.002 |
| 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 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".