Immunogenicity of a polyvalent HIV-1 candidate vaccine based on fourteen wild type gp120 proteins in golden hamsters
Bibliographic record
Abstract
BACKGROUND: One of the major obstacles in the design of an effective vaccine against HIV-1 is the hypervariability of the HIV-1 envelope glycoprotein. Most HIV-1 vaccine candidates have utilized envelope glycoprotein from a single virus isolate, but to date, none of them elicited broadly reactive humoral immunity. Herein, we hypothesised that a cocktail of HIV-1 gp120 proteins containing multiple epitopes may increase the breadth of immune responses against HIV-1. We compared and evaluated the immunogenicity of HIV-1 vaccines containing either gp120 protein alone or in combinations of four or fourteen gp120s from different primary HIV-1 isolates in immunized hamsters. RESULTS: We amplified and characterized 14 different gp120s from primary subtype B isolates with both syncytium and non-syncytium inducing properties, and expressed the proteins in Chinese Hamster Ovary (CHO) cell lines. Purified proteins were used either alone or in combinations of four or fourteen different gp120s to vaccinate golden hamsters. The polyvalent vaccine showed higher antibody titers to HIV-1 subtype B isolates MN and SF162 compared to the groups that received one or four gp120 proteins. However, the polyvalent vaccine was not able to show higher neutralizing antibody responses against HIV-1 primary isolates. Interestingly, the polyvalent vaccine group had the highest proliferative immune responses and showed a substantial proportion of cross-subtype CD4 reactivity to HIV-1 subtypes B, C, and A/E CONCLUSION: Although the polyvalent approach achieved only a modest increase in the breadth of humoral and cellular immunity, the qualitative change in the vaccine (14 vs. 1 gp120) resulted in a quantitative improvement in vaccine-induced immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".