Increased homeostatic cytokines and stability of HIV-infected memory CD4 T-cells identify individuals with suboptimal CD4 T-cell recovery on-ART
Bibliographic record
Abstract
Clinical outcomes are inferior for individuals with HIV having suboptimal CD4 T-cell recovery during antiretroviral therapy (ART). We investigated if the levels of infection and the response to homeostatic cytokines of CD4 T-cell subsets contributed to divergent CD4 T-cell recovery and HIV reservoir during ART by studying virologically-suppressed immunologic responders (IR, achieving a CD4 cell count >500 cells/μL on or before two years after ART initiation), and virologically-suppressed suboptimal responders (ISR, did not achieve a CD4 cell count >500 cells/μL in the first two years after ART initiation). Compared to IR, ISR demonstrated higher levels of HIV-DNA in naïve, central (CM), transitional (TM), and effector (EM) memory CD4 T-cells in blood, both pre- and on-ART, and specifically in CM CD4 T-cells in LN on-ART. Furthermore, ISR had higher pre-ART plasma levels of IL-7 and IL-15, cytokines regulating T-cell homeostasis. Notably, pre-ART PD-1 and TIGIT expression levels were higher in blood CM and TM CD4 T-cells for ISR; this was associated with a significantly lower fold-changes in HIV-DNA levels between pre- and on-ART time points exclusively on CM and TM T-cell subsets, but not naïve or EM T-cells. Finally, the frequency of CM CD4 T-cells expressing PD-1 or TIGIT pre-ART as well as plasma levels of IL-7 and IL-15 predicted HIV-DNA content on-ART. Our results establish the association between infection, T-cell homeostasis, and expression of PD-1 and TIGIT in long-lived CD4 T-cell subsets prior to ART with CD4 T-cell recovery and HIV persistence on-ART.
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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) | 0.000 | 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 | 0.000 | 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".