Decreased frequency of CD73+CD8+ T cells of HIV-infected patients correlates with immune activation and T cell exhaustion
Bibliographic record
Abstract
Recent studies indicate that murine Tregs highly express the ENTDP1, as well as the 5'-NT and thereby, suppress Teff function by extracellular adenosine production. Furthermore, CD73 seems to play a role as costimulatory molecule for T cell differentiation. In this study, we analyzed the expression of CD73 on peripheral and lymph nodal Teffs and Tregs in a cohort of 95 HIV patients at different stages of disease, including LTNP and ECs. In contrast to murine Tregs, CD73 was only expressed on a small minority (∼10%) of peripheral Tregs. In contrast, we see high expression of CD73 on peripheral CD8(+) T cells. In HIV infection, CD73 is markedly reduced on all Teffs and Tregs, regardless of the memory subtype. On CD8(+) T cells, a positive correlation between CD73 expression and CD4 counts (P=0.0003) was detected. CD73 expression on CD8(+) T cells negatively correlated with HLA-DR (<0.0001) and PD1 (P=0.0457) expression. The lower CD73 expression on CD8(+) T cells was partially reversible after initiation of ART (P=0.0016). Functionally, we observed that CD8(+)CD73(+) T cells produce more IL-2 upon HIV-specific and unspecific stimulation than their CD73(-) counterparts and show a higher proliferative capacity. These data indicate that down-regulation of CD73 on CD8(+) T cells correlates with immune activation and leads to functional deficits 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".