Antibody-mediated NK cell activation as a correlate of immunity against influenza infection
Bibliographic record
Abstract
Antibodies play a critical role in protection against influenza; yet titers and viral neutralization represent incomplete correlates of immunity. Instead, the ability of antibodies to leverage the antiviral power of the innate immune system has been implicated in protection from and clearance of influenza infection. Here, post-hoc analysis of the humoral immune response to influenza is comprehensively profiled in a cohort of vaccinated older adults (65 + ) monitored for influenza infection during the 2012/2013 season in the United States (NCT: 01427309). While robust humoral immune responses arose against the vaccine and circulating strains, influenza-specific antibody effector profiles differed in individuals that later became infected with influenza, who are deficient in NK cell activating antibodies to both hemagglutinin and neuraminidase, compared to individuals who remained uninfected. Furthermore, NK cell activation was strongly associated with the NK cell senescence marker CD57, arguing for the need for selective induction of influenza-specific afucosylated NK activating antibodies in older adults to achieve protection. High dose vaccination, currently used for older adults, was insufficient to generate this NK cell-activating humoral response. Next generation vaccines able to selectively bolster NK cell activating antibodies may be required to achieve protection in the setting of progressively senescent NK cells.
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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) | 0.001 | 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".