HIV Controllers Are Distinguished by Chemokine Expression Profile and HIV-Specific T-Cell Proliferative Potential
Bibliographic record
Abstract
BACKGROUND: HIV controllers demonstrate a natural ability to control HIV replication in the absence of antiretroviral therapy. We performed a comprehensive evaluation of inflammation and T-cell activation in a demographically unique cohort of HIV controllers and noncontrollers. METHODS: Plasma concentrations of 22 cytokines and chemokines were evaluated using a multiplex bead array approach. Multicolor flow cytometry was used to measure baseline levels of T-cell activation and regulatory T cells (Tregs) and HIV-specific T-cell cytokine (interferon γ, interleukin 2) and proliferation responses. RESULTS: HIV controllers were characterized by elevated macrophage inflammatory protein 1α and low levels of interferon γ-induced protein 10, monocyte chemotactic protein 1, and Transforming growth factor beta. Activated (CD38(+) HLA DR(+)) CD4(+) and CD8(+) T cells were reduced in HIV controllers relative to noncontrollers. HIV controllers and noncontrollers had comparable proportions of Tregs within the CD4(+) T-cell compartment, but absolute Treg counts were depleted in noncontrollers. Absolute Treg counts correlated inversely with T-cell activation. Proliferative CD4(+) and CD8(+) T-cell responses directed against HIV gag epitopes were found most frequently among HIV controllers with the lowest viral loads (elite controllers) and were rarely detected among noncontrollers, supporting a relationship between HIV-specific T-cell proliferation and viral control. CONCLUSIONS: Collectively, these data suggest a model in which HIV controllers maintain low levels of viral replication through robust HIV-specific T-cell responses in an environment of low inflammation and reduced availability of activated target cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".