Immune interventions in <scp>HIV</scp> infection
Bibliographic record
Abstract
Immune-based therapy (IBT) interventions have found a window of opportunity within some limitations of the otherwise successful combined antiretroviral therapy (cART). Two major paradigms drove immunotherapeutic research to combat human immunodeficiency virus (HIV) infection. First, IBTs were proposed either to help restore CD4(+) T-cell counts in cases of therapeutic failures with cytokines, interleukin-2 (IL-2) or IL-7, or to better control HIV and disease progression during treatment interruptions with anti-HIV therapeutic candidate vaccines. The most widely used candidates were HIV-recombinant live vector-based alone or combined with other vaccine compounds and dendritic cell (DC) therapies. A more recent and current paradigm aims at achieving HIV cure by combining IBT with cART using either cytokines to reactivate virus production in latently infected cells and/or therapeutic immunization to boost HIV-specific immunity in a 'shock and kill' strategy. This review summarizes the rationale, hopes, and mechanisms of successes and failures of these cytokine-based and vaccine-based immune interventions. Results from these first series of IBTs have been so far somewhat disappointing in terms of clinical relevance, but have provided lessons that are discussed in light of the future combined strategies to be developed toward an HIV cure.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".