Progress and Prospects in HIV Vaccine Development: A Comprehensive Review
Bibliographic record
Abstract
HIV/AIDS has been a disease condition affecting the entire world since its discovery in the 1980s, with over 38 million people in the globe living with the virus. Several attempts, research and funds have been channelled towards discovering a cure for HIV/AIDS yet no cure has been discovered. Considering the burden associated with the disease condition, having a vaccine to help control the spread will be a great approach in the course of achieving universal control coverage of the disease condition and an end to the disease over time. In this review we examined the progress, challenges, and future directions in HIV vaccine development and various types of vaccine candidates currently under investigation, highlighting key drug candidates that failed to provide protection against HIV such as PrEPVacc, HVTN 505, Uhambo (HVTN 702), Imbokodo (HVTN 705) and Mosaico (HVTN 706) as well as RV144 that provided 31.2% protection against HIV in a modified intent-to-treat analysis and those whose investigations are still in progress. The search for HIV Vaccine continues despite the modest success of some vaccines in clinical trials due to diversity in genetic nature of HIV, its ability to mutate rapidly, and the difficulty of eliciting a sustained immune response. Hence we also reviewed current approaches to improve on the success rate recorded by RV144.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".