Natural killer cell responses during SARS-CoV-2 infection and vaccination in people living with HIV-1
Bibliographic record
Abstract
Abstract Natural killer (NK) cell subsets with adaptive properties are emerging as regulators of vaccine-induced T and B cell responses and are specialized towards antibody-dependent functions contributing to SARS-CoV-2 control. Although HIV-1 infection is known to affect the NK cell pool, the additional impact of SARS-CoV-2 infection and/or vaccination on NK cell responses in people living with HIV (PLWH) has remained unexplored. Our data show that SARS-CoV-2 infection skews NK cells towards a more differentiated/adaptive CD57+FcεRIγ− phenotype in PLWH. A similar subset was induced following vaccination in SARS-CoV-2 naïve PLWH in addition to a CD56bright population with cytotoxic potential. Antibody-dependent NK cell function showed robust and durable responses to Spike up to 148 days post-infection, with responses enriched in adaptive NK cells. NK cell responses were further boosted by the first vaccine dose in SARS-CoV-2 exposed individuals and peaked after the second dose in SARS-CoV-2 naïve PLWH. The presence of adaptive NK cells associated with the magnitude of cellular and humoral responses. These data suggest that features of adaptive NK cells can be effectively engaged to complement and boost vaccine-induced adaptive immunity in potentially more vulnerable groups such as PLWH.
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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 | 0.001 |
| 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".