Regulatory T Cells in HIV Infection: Can Immunotherapy Regulate the Regulator?
Bibliographic record
Abstract
Regulatory T cells (Tregs) have a dominant role in self-tolerance and control of autoimmune diseases. These cells also play a pivotal role in chronic viral infections and cancer by limiting immune activation and specific immune response. The role of Tregs in HIV pathogenesis remains poorly understood as their function, changes according to the phases of infection. Tregs can suppress anti-HIV specific responses and conversely can have a beneficial role by reducing the deleterious impact of immune activation. We review the frequency, function and homing potential of Tregs in the blood and lymphoid tissues as well as their interaction with dendritic cells in the context of HIV infection. We also examine the new insights generated by recombinant IL-2 and IL-7 clinical trials in HIV-infected adults, including the immunomodulatory effects of Tregs. Based on their detrimental role in limiting anti-HIV responses, we propose Tregs as potential targets for immunotherapeutic strategies aimed at decreasing Tregs frequency and/or immunosuppressive function. However, such approaches require a better understanding of the time upon infection when interfering with Treg function may not cause a deleterious state of hyperimmune activation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".