Double-Negative T-Cells during Acute Human Immunodeficiency Virus and Simian Immunodeficiency Virus Infections and Following Early Antiretroviral Therapy Initiation
Bibliographic record
Abstract
HIV infection significantly affects the frequencies and functions of immunoregulatory CD3+CD4−CD8− double-negative (DN) T-cells, while the effect of early antiretroviral therapy (ART) initiation on these cells remains understudied. DN T-cell subsets were analyzed prospectively in 10 HIV+ individuals during acute infection and following early ART initiation compared to 20 HIV-uninfected controls. In this study, 21 Rhesus macaques (RMs) were SIV-infected, of which 13 were assessed during acute infection and 8 following ART initiation four days post-infection. DN T-cells and FoxP3+ DN Treg frequencies increased during acute HIV infection, which was not restored by ART. The expression of activation (HLA-DR/CD38), immune checkpoints (PD-1/CTLA-4), and senescence (CD28−CD57+) markers by DN T-cells and DN Tregs increased during acute infection and was not normalized by ART. In SIV-infected RMs, DN T-cells remained unchanged despite infection or ART, whereas DN Treg frequencies increased during acute SIV infection and were not restored by ART. Finally, frequencies of CD39+ DN Tregs increased during acute HIV and SIV infections and remained elevated despite ART. Altogether, acute HIV/SIV infections significantly changed DN T-cell and DN Treg frequencies and altered their immune phenotype, while these changes were not fully normalized by early ART, suggesting persistent HIV/SIV-induced immune dysregulation despite early ART initiation.
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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) | 0.001 | 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".