Development of an HIV vaccine using a vesicular stomatitis virus vector expressing designer HIV-1 envelope glycoproteins to enhance humoral responses
Bibliographic record
Abstract
Vesicular stomatitis virus (VSV), like many other Rhabdoviruses, have become the focus of intense research over the past couple of decades based on their suitability as vaccine vectors, transient gene delivery systems, and as oncolytic viruses for cancer therapy. VSV as a vaccine vector platform has multiple advantages over more traditional viral vectors including low level, non-pathogenic replication in diverse cell types, ability to induce both humoral and cell-mediate immune responses, and the remarkable expression of foreign proteins cloned into multiple intergenic sites in the VSV genome. The utility and safety of VSV as a vaccine vector was recently demonstrated near the end of the recent Ebola outbreak in West Africa where VSV pseudotyped with the Ebola virus (EBOV) glycoprotein was proven safe in humans and provided protective efficacy against EBOV in a human phase III clinical trial. A team of Canadian scientists, led by Dr. Gary Kobinger, is now working with International AIDS Vaccine Initiative (IAVI) in developing a VSV-based HIV vaccine that will combine unique Canadian research on the HIV-1 Env glycoprotein and on the VSV vaccine vector. The goal of this collaboration is to develop a vaccine with a robust and potent anti-HIV immune response with an emphasis on generating quality antibodies to protect against HIV challenges.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".