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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".