Rapid Galectin-9 release occurs following HIV-1 acquisition in acute HIV-1 infection (P6165)
Bibliographic record
Abstract
Abstract Despite successes with antiretroviral therapy, HIV infection continues to be a significant health burden with patients remaining at risk of non-AIDS defining events. The earliest immunological responses induced during HIV infection are key events in the control of HIV disease progression. Soluble biomarkers may aid in understanding HIV-immunopathogenesis, inflammation and prophylactic vaccine design. Galectin-9 (Gal-9) is a bidirectional immune regulator that promotes tissue inflammation and cell death but induces immune tolerance in part through engagement with the Tim-3 receptor. We measured circulating levels of Gal-9 in the plasma of 10 acute HIV-infected subjects at multiple time points over a 42-day period, before and after detectable HIV RNA. Plasma HIV RNA levels increased rapidly in all subjects. We found that Gal-9 levels rose with the first median elevation time of 5 days after detectable viremia and a median peak of 57.4 pg/ml. Overall Gal-9 levels generally tracked with viremia. In vitro, recombinant Gal-9 significantly increased IFN-γ production by CD8+ T cells compared to untreated cells (0.02% vs 1.2%) in cultures from early HIV-infected subjects and this was reversed by α-lactose blockade. Our findings reveal that Gal-9 is a novel plasma factor that is rapidly elevated during the first wave of the cytokine storm after HIV emergence and may have important roles in CD4+ T cell loss or the response to tissue damage.
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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".