Antiretroviral Pre-Exposure Prophylaxis Does Not Enhance Immune Responses to HIV in Exposed but Uninfected Persons
Bibliographic record
Abstract
BACKGROUND: Antiretroviral preexposure prophylaxis (PrEP), using daily oral combination tenofovir disoproxil fumarate plus emtricitabine, is an effective human immunodeficiency virus (HIV) prevention strategy for populations at high risk of HIV acquisition. Although the primary mode of action for the protective effect of PrEP is probably direct antiviral activity, nonhuman primate studies suggest that PrEP may also allow for development of HIV-specific immune responses, hypothesized to result from aborted HIV infections providing a source of immunologic priming. We sought to evaluate whether PrEP affects the development of HIV-specific immune response in humans. METHODS AND RESULTS: Within a PrEP clinical trial among high-risk heterosexual African men and women, we detected HIV-specific CD4(+) and CD8(+) peripheral blood T-cell responses in 10%-20% of 247 subjects evaluated. The response rate and magnitude of T-cell responses did not vary significantly between those assigned PrEP versus placebo, and no significant difference between those assigned PrEP and placebo was observed in measures of innate immune function. CONCLUSIONS: We found no evidence to support the hypothesis that PrEP alters either the frequency or magnitude of HIV-specific immune responses in HIV-1-exposed seronegative individuals. These results suggest that PrEP is unlikely to serve as an immunologic prime to aid protection by a putative HIV 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 | 0.003 |
| 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".