Immunization of HIV-1-Infected Persons With Autologous Dendritic Cells Transfected With mRNA Encoding HIV-1 Gag and Nef
Bibliographic record
Abstract
BACKGROUND: HIV-1 eradication may require reactivation of latent virus along with stimulation of HIV-1-specific immune responses to clear infected cells. Immunization with autologous dendritic cells (DCs) transfected with viral mRNA is a promising strategy for eliciting HIV-1-specific immune responses. We performed a randomized controlled clinical trial to evaluate the immunogenicity of this approach in HIV-1-infected persons on antiretroviral therapy. METHODS: Fifteen participants were randomized 2:1 to receive intradermal immunization with HIV-1 Gag- and Nef-transfected DCs (vaccine) or mock-transfected DCs (placebo) at weeks 0, 2, 6, and 10. All participants also received DCs pulsed with keyhole limpet hemocyanin (KLH) to assess whether responses to a neo-antigen could be induced. RESULTS: After immunization, there were no differences in interferon-gamma enzyme-linked immunospot responses to HIV-1 Gag or Nef in the vaccine or placebo group. CD4 proliferative responses to KLH increased 2.4-fold (P = 0.026) and CD8 proliferative responses to KLH increased 2.5-fold (P = 0.053) after vaccination. There were increases in CD4 proliferative responses to HIV-1 Gag (2.5-fold vs. baseline, 3.4-fold vs. placebo, P = 0.054) and HIV-1 Nef (2.3-fold vs. baseline, 6.3-fold vs. placebo, P = 0.009) among vaccine recipients, but these responses were short-lived. CONCLUSION: Immunization with DCs transfected with mRNA encoding HIV-1 Gag and Nef did not induce significant interferon-gamma enzyme-linked immunospot responses. There were increases in proliferative responses to HIV-1 antigens and to a neo-antigen, KLH, but the effects were transient. Dendritic cell vaccination should be optimized to elicit stronger and long-lasting immune responses for this strategy to be effective as an HIV-1 therapeutic vaccine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".