The Education of NK Cells Determines Their Responsiveness to Autologous HIV-Infected CD4 T Cells
Bibliographic record
Abstract
Epidemiological studies show that natural killer (NK) cells have anti-HIV activity: they are able to reduce the risk of HIV infection and/or slow HIV disease progression. How NK cells contribute to these outcomes is not fully characterized. We used primary NK cells and autologous HIV-infected cells to examine the role of education through four inhibitory killer immunoglobulin-like receptors (iKIRs) from persons with HLA types that are able to educate NK cells bearing one of these iKIRs. HIV-infected cells activated NK cells through missing-self mechanisms due to the downmodulation of cell surface HLA expression mediated by HIV Nef and Vpu. A higher frequency of educated than uneducated NK cells expressing each of these iKIRs responded to autologous HIV-infected cells by producing CCL4, IFN-γ, and CD107a. Since NK cells were from non-HIV-infected individuals, they model the consequences of healthy NK cell-HIV-infected cell interactions occurring in the HIV eclipse phase, when new infections are susceptible to extinction.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".