Killed whole-HIV vaccine; employing a well established strategy for antiviral vaccines
Bibliographic record
Abstract
The development of an efficient prophylactic HIV vaccine has been one of the major challenges in infectious disease research during the last three decades. Here, we present a mini review on strategies employed for the development of HIV vaccines with an emphasis on a well-established vaccine technology, the killed whole-virus vaccine approach. Recently, we reported an evaluation of the safety and the immunogenicity of a genetically modified and killed whole-HIV-1 vaccine designated as SAV001 [1]. HIV-1 Clade B NL4-3 was genetically modified by deleting the nef and vpu genes and substituting the coding sequence of the Env signal peptide with that of honeybee melittin to produce an avirulent and replication efficient HIV-1. This genetically modified virus (gmHIV-1 NL4-3 ) was propagated in a human T cell line followed by virus purification and inactivation by aldrithiol-2 and γ-irradiation. We found that SAV001 was well tolerated with no serious adverse events. HIV-1 NL4-3 -specific polymerase chain reaction showed no evidence of vaccine virus replication in participants receiving SAV001 and in human T cells infected in vitro. Furthermore, SAV001 with an adjuvant significantly increased the antibody response to HIV-1 structural proteins. Moreover, antibodies in the plasma from these vaccinations neutralized tier I and tier II of HIV-1 B, A, and D subtypes. These results indicated that the killed whole-HIV vaccine is safe and may trigger appropriate immune responses to prevent HIV infection. Utilization of this killed whole-HIV vaccine strategy may pave the way to develop an effective HIV vaccine.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".