HIV-1 and AIDS: what are protective immune responses?
Bibliographic record
Abstract
HIV-1 is the leading cause of death in sub-Saharan Africa, responsible for one in five deaths in the region. Although potent antiretroviral therapy has had a huge impact on HIV-associated morbidity and mortality in economically advantaged countries, it is beyond the reach of most infected people in the world. The development of an effective HIV vaccine would be a huge step towards stopping the pandemic, but an important precondition for such a vaccine is that it must induce a host immune response that can protect the host from HIV acquisition or disease progression. This article reviews the evidence that protective host immune responses do exist, either in highly exposed, persistently seronegative (HEPS) subjects or in HIV-1-infected long-term non-progressors (LTNPs), as well as efforts to reproduce putative protective immunity in animal vaccine models. HIV-1-specific cellular responses are a key to viral control in infected subjects, but generally fail in the long term. This suggests that the goal of a preventive HIV-1 vaccine should be sterile immunity, rather than improved virus control after infection. Achieving this goal will at least require the induction of HIV-1-specific cellular immune responses at the site of initial viral contact (generally the genital tract), perhaps in combination with HIV-1-specific neutralising antibody.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".