NKG2D Acts as a Co-Receptor for Natural Killer Cell-Mediated Anti-HIV-1 Antibody-Dependent Cellular Cytotoxicity
Bibliographic record
Abstract
The utility of antibody-dependent cellular cytotoxicity (ADCC) for eliminating HIV-1-infected cells is of much interest for the design of both prophylactic vaccines for HIV-1 prevention and therapeutics to eliminate latently infected cells following reactivation. Significant research has been conducted to understand the antibody specificities involved in anti-HIV-1 ADCC responses. Perhaps equally important as the identity of the antibodies mediating these responses are factors regulating the ability of ADCC effector cells, in particular, natural killer (NK) cells, to respond to antibody-coated target cells. Indeed, a plethora of activating and inhibitory receptors expressed on the surface of NK cells might act in conjunction with CD16 to influence ADCC. As the expression of NKG2D and its ligands has been linked to HIV-1 disease progression, we evaluated if signals through NKG2D were involved in anti-HIV-1 ADCC. Utilizing assays measuring cytolysis, we provide the first data implicating NKG2D in antibody-dependent NK cell responses against a target cell line either pulsed with gp120 or infected with HIV-1. These observations are highly significant for understanding antibody-dependent NK cell responses against HIV-1 and might be useful for optimizing prophylactics and therapeutics aiming to utilize antibodies and optimally functional NK cells to control 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 | 0.000 | 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) | 0.002 | 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".