A heterogeneous human immunodeficiency virus-like particle (VLP) formulation produced by a novel vector system
Bibliographic record
Abstract
Abstract First identified as the etiological agent behind Acquired Immunodeficiency Syndrome (AIDS) in the early 1980s, HIV-1 has continued to spread into a global pandemic and major public health concern. Despite the success of antiretroviral therapy at reducing HIV-1 viremia and preventing the dramatic CD4+ T-cell collapse, infected individuals remain HIV positive for life. Unfortunately, it is increasingly clear that natural immunity is not, and may never be, protective against this pathogen. Therefore, efficacious vaccine interventions, which can either prevent infection or eradicate the latent viral reservoir and effect cure, are a major medical priority. Here we describe the development of a safe vaccine platform, currently being utilized in on-going prophylactic and therapeutic preclinical studies and consisting of highly heterogeneous virus-like particle formulations that represent the virus diversity within infected individuals. These VLPs contain no 5′LTR, no functional integrase, and have a severely mutated stem loop 1—thereby preventing any potential reverse transcription, integration, and RNA packaging. Furthermore, we demonstrate that these VLPs are morphologically identical to wild-type virus with polyvalent Env in a functional form. Finally, we show that the VLPs are antigenic and capable of generating strong immune recall responses.
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 | 0.001 |
| 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".