Impact of IFNγ on HIV-specific CD4 T cell and antigen presenting cell function (P6163)
Bibliographic record
Abstract
Abstract One of the major impediments of HIV infection is the abnormally high levels of immune activation that contributes to immune dysregulation and impairs viral clearance. While IFNγ secretion is used as a marker of the functional state of T lymphocytes and NK cells, its role in regulating immune responses is poorly understood. We isolated CD8-depleted PBMCs from HIV infected individuals (n=13). We measured proliferation and cytokine production by HIV-specific CD4 T cells stimulated with HIV Gag in the presence of isotype control, anti-IFNγ and/or anti-PD-L1. We performed apoptosis measurements using Annexing-V binding assays. We also determined the modulatory impact of IFNγ on APCs by measuring expression of PD-1 ligands, HLA-DR, HLA-I and IL-12 secretion. Neutralization of IFNγ produced by Gag stimulated CD4 T cells enhanced proliferation (p=0.0161) and reduced apoptosis of HIV-specific CD4 T cells. IFNγ blockade enhanced IL-13 secretion (p=0.0039) but had no effect on IL-2, IL-10 and TNFα levels. IFNγ induced strong up-regulation of PD-L1, HLA-DR and HLA-I on monocytes but not on B, T and NK cells. IFNγ also induced IL-12 secretion by APCs that acted in a positive feedback loop to further enhance IFNγ secretion. Concurrent blockade of IFNγ and PD-L1 led to a more prominent increase in proliferation of HIV-specific CD4 T cells than blockade of individual pathways. These data provide mechanistic insight on the causal role of IFNγ in T-helper dysregulation in HIV infection.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".