Distinct biomarker signatures in HIV acute infection associate with viral dynamics and reservoir size
Bibliographic record
Abstract
Estimating the size of the viral reservoir is critical for HIV cure strategies. Biomarkers in peripheral circulation may give insights into the establishment of the viral reservoir in compartments not easily accessible. We therefore measured systemic levels of 84 soluble biomarkers belonging to a broad array of immune pathways in acute HIV infection in both antiretroviral therapy-naive (ART-naive) individuals as well as individuals who began ART upon early detection of HIV infection. These biomarkers were measured longitudinally during acute and chronic infection and their relationship to viral reservoir establishment and persistence was assessed. We observed several distinct biomarker pathways induced following HIV infection such as IFN-γ-signaled chemokines, proinflammatory markers, and TNF-α-family members. Levels of several of these factors directly correlated with contemporaneous viral loads and/or frequency of peripheral blood mononuclear cells harboring HIV DNA during acute HIV infection. MCP-1, MIP-3β, sTNFR-II, and IL-10 levels prior to ART associated with HIV DNA levels after 96 weeks of treatment, suggesting a link between early immune signaling events and the establishment and persistence of the viral reservoir during ART. Furthermore, they offer potentially novel tools for gaining insight into relative reservoir size in acutely infected individuals and the potential of associated risks of treatment interruption.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".