Intravenous Immunoglobulin (IVIG) Treatment for Modulation of Immune Activation in Human Immunodeficiency Virus Type 1 Infected Therapy-Naive Individuals
Bibliographic record
Abstract
We evaluated the ability of intravenous immunoglobulin (IVIG) to diminish immune hyperactivation, which is considered a major cause of CD4+ T cell loss during chronic HIV-1 infection and whether this affected CD4+ T cell counts and plasma HIV-1 RNA (pVL). Therefore, we treated six chronically HIV-1-infected, antiretroviral-therapy-naive patients with IVIG (0.4 g/kg) at weeks 0 and 4, with a follow-up of 12 weeks after the second dosage during which pVL, T cell numbers, and T cell activation were measured. At baseline median CD4+ T cell counts were 300 (range 200-460) x 10(6)/liter and median pVL was 5.0 (range 3.2-5.2) log10 copies/ml. IgG plasma levels peaked during the first days after administration. We observed a decrease in the percentage of activated (CD38+ HLA-DR+) CD4+ and CD8+ T cells [3.5% (range 1-7%) and 5% (1-10%), respectively (p = 0.027)], but no effect on the fraction of proliferating CD4+ or CD8+ T cells as measured by Ki67 expression. CD4+ T cell counts were significantly increased on day 4 (median +55 cells, range 0-150, p = 0.043). pVL was significantly increased on day 1 after IVIG infusion (median +0.13 log10, range 0.01-0.55, p = 0.028). All these parameters returned to baseline levels within 1 week after infusion. In conclusion, administration of IVIG caused a temporary decrease in T cell activation and an increase in CD4+ T cell counts, despite an increase in pVL. Our results support the hypothesis that T cell activation, rather than direct HIV-1 infection, mediates the loss of CD4+ T cells and suggest that immunomodulating therapy in HIV-1 infection could indeed be effective.
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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) | 0.001 | 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 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".