New approaches to design HIV-1 T-cell vaccines
Bibliographic record
Abstract
PURPOSE OF REVIEW: Following the evidence that T-cell responses are crucial in the control of HIV-1 infection, vaccines targeting T-cell responses were tested in recent clinical trials. However, these vaccines showed a lack of efficacy. This review attempts to define the qualitative and quantitative features that are desirable for T-cell-induced responses by vaccines. We also describe strategies that could lead to achievement of this goal. RECENT FINDINGS: Using the yellow fever vaccine as a benchmark of an efficient vaccine, recent studies identified factors of immune protection and more importantly innate immune pathways needed for the establishment of long-term protective adaptive immunity. SUMMARY: To prevent or control HIV-1 infection, a vaccine must induce efficient and persistent antigen-specific T cells endowed with mucosal homing capacity. Such cells should have the capability to counteract HIV-1 diversity and its rapid spread from the initial site of infection. To achieve this goal, the activation of a diversified innate immune response is critical. New systems biology approaches will provide more precise correlates of immune protection that will pave the way for new approaches in T-cell-based vaccines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".