Bibliographic record
Abstract
A large array of broadly neutralizing antibodies (bnAbs) against HIV have been isolated and described, particularly in the last decade. This continually expanding array of bnAbs has crucially led to the identification of novel epitopes on the HIV envelope protein via which antibodies can block a broad range of HIV strains. Moreover, these studies have produced high-resolution understanding of these sites of vulnerability on the envelope protein. They have also clarified the mechanisms of action of bnAbs and provided detailed descriptions of B cell ontogenies from which they arise. However, it is still not possible to predict which HIV-infected individuals will go onto develop breath nor is it possible to induce neutralization breadth by immunization in humans. This review aims to discuss the major insights gained so far and also to evaluate the requirement to continue isolating and characterizing new bnAbs. While new epitopes may remain to be uncovered, a clearer probable benefit of further bnAb characterization is a greater understanding of key decision points in bnAb development within the anti-HIV immune response. This in turn may lead to new insights into how to trigger bnAbs by immunization and more clearly define the challenges to using bnAbs as therapeutic agents.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".