Distinctive features of CD4+ T cell dysfunction in chronic viral infections
Bibliographic record
Abstract
PURPOSE OF REVIEW: To describe recent advances in the understanding of virus-specific CD4 T cell dysfunction in chronic viral infections, with an emphasis on HIV disease. We highlight features that are distinctive for CD4 T cells, as opposed to their CD8 T cell counterparts. RECENT FINDINGS: CD4 T cell activation and differentiation are tightly controlled. Regulation of these processes depends on the context of initial encounter of the naïve CD4 T cell with the cognate antigen and on ongoing external cues to the antigen-experienced CD4 T cell, in particular the inflammatory environment, which is prominent in HIV infection. Virus-specific CD4 T cell dysfunction results from a combination of an exhaustion program and skewing in T helper lineage differentiation which impact function. The CD4 and CD8 T cell exhaustion programs present similarities and distinct features. The sets of inhibitory coreceptors expression differ, although programmed-death 1 (PD-1) and T cell immunoglobulin mucin-3 (Tim-3) are upregulated on both HIV-specific CD4 and CD8 T cells, cytotoxic T-lymphocyte antigen 4 (CTLA-4) is largely specific to CD4 T cells, whereas 2B4 and CD160 are biased toward CD8 T cells. SUMMARY: Understanding the molecular basis of HIV-specific CD4 T cell exhaustion and identifying key differences with CD8 T cell impairment will be critical to design effective therapeutic and preventive immunotherapies against HIV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".