Eradication of HIV-1 latent reservoirs through therapeutic vaccination
Bibliographic record
Abstract
Despite the significant success of combination anti-retroviral therapy to reduce HIV viremia and save lives, HIV-1 infection remains a lifelong infection that must be appropriately managed. Advances in the understanding of the HIV infection process and insights from vaccine development in other biomedical fields such as cancer, imaging, and genetic engineering have fueled rapid advancements in HIV cure research. In the last few years, several studies have focused on the development of "Kick and Kill" therapies to reverse HIV latency and kick start viral translational activity. This has been done with the aim that concomitant anti-retroviral treatment and the elicited immune responses will prevent de novo infections while eradicating productively infected cells. In this review, we describe our perspective on HIV cure and the new approaches we are undertaking to eradicate the established pro-viral reservoir.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".