Beyond Viral Neutralization
Bibliographic record
Abstract
It has been known for more than 30 years that HIV-1 infection drives a very potent B cell response resulting in the production of anti-HIV-1 antibodies targeting several viral proteins, particularly its envelope glycoproteins (Env). Env epitopes are exposed on the surfaces of viral particles and infected cells where they are targets of potentially protective antibodies. These antibodies can interdict infection by neutralization and there is strong evidence suggesting that Fc-mediated effector function can also contribute to protection. Current evidence suggests that Fc-mediated effector function plays a role in protection against infection by broadly neutralizing antibodies and it might be important for protection by non-neutralizing antibodies. Fc-mediated effector function includes diverse mechanisms such as antibody-dependent cellular cytotoxicity (ADCC), antibody-mediated complement activation, antibody-dependent cellular phagocytosis, antibody-dependent cell-mediated virus inhibition, antibody-mediated trancytosis inhibition, and antibody-mediated virus opsonization. All these functions could be beneficial in fighting viral infections, including HIV-1. In this perspective, we discuss the latest developments in ADCC research discussed at the HIVR4P satellite session on non-neutralizing antibodies, with emphasis on the mechanisms of ADCC resistance used by HIV-1, the structural basis of epitopes recognized by antibodies that mediate ADCC, natural killer-cell education and ADCC, and murine models to study ADCC against HIV-1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".