Phenotypic and functional characteristics of highly differentiated CD57+NKG2C+ NK cells in HIV-1-infected individuals
Bibliographic record
Abstract
Natural killer (NK) cells are important anti-viral effector cells. The function and phenotype of the NK cells that constitute an individual's NK cell repertoire can be influenced by ongoing or previous viral infections. Indeed, infection with human cytomegalovirus (HCMV) drives the expansion of a highly differentiated NK cell population characterized by expression of CD57 and the activating NKG2C receptor. This NK cell population has also been noted to occur in HIV-1-infected individuals. We evaluated the NK cells of HIV-1-infected and HIV-1-uninfected individuals to determine the relative frequency of highly differentiated CD57+NKG2C+ NK cells and characterize these cells for their receptor expression and responsiveness to diverse stimuli. Highly differentiated CD57+NKG2C+ NK cells occurred at higher frequencies in HCMV-infected donors relative to HCMV-uninfected donors and were dramatically expanded in HIV-1/HCMV co-infected donors. The expanded CD57+NKG2C+ NK cell population in HIV-1-infected donors remained stable following antiretroviral therapy. CD57+NKG2C+ NK cells derived from HIV-1-infected individuals were robustly activated by antibody-dependent stimuli that contained anti-HIV-1 antibodies or therapeutic anti-CD20 antibody, and these NK cells mediated cytolysis through NKG2C. Lastly, CD57+NKG2C+ NK cells from HIV-1-infected donors were characterized by reduced expression of the inhibitory NKG2A receptor. The abundance of highly functional CD57+NKG2C+ NK cells in HIV-1-infected individuals raises the possibility that these NK cells could play a role in HIV-1 pathogenesis or serve as effector cells for therapeutic/cure strategies.
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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 | 0.000 |
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".