Influenza Vaccination Primes Human Myeloid Cell Cytokine Secretion and NK Cell Function
Bibliographic record
Abstract
Cytokine-induced memory-like (CIML) NK cells generated in response to proinflammatory cytokines in vitro and in vivo can also be generated by vaccination, exhibiting heightened responses to cytokine stimulation months after their initial induction. Our previous study demonstrated that in vitro human NK cell responses to inactivated influenza virus were also indirectly augmented by very low doses of IL-15, which increased induction of myeloid cell-derived cytokine secretion. These findings led us to hypothesize that IL-15 stimulation could reveal a similar effect for active influenza vaccination and influence CIML NK cell effector functions. In this study, 51 healthy adults were vaccinated with seasonal influenza vaccine, and PBMC were collected before and up to 30 d after vaccination. Myeloid and lymphoid cell cytokine secretion was measured after in vitro PBMC restimulation with low-dose IL-15, alone or in combination with inactivated H3N2 virus; the associated NK cell response was assessed by flow cytometry. PBMC collected 30 d postvaccination showed heightened cytokine production in response to IL-15 compared with PBMC collected at baseline; these responses were further enhanced when IL-15 was combined with H3N2. NK cell activation in response to IL-15 alone (CD25) and H3N2 plus IL-15 (CD25 and IFN-γ) was enhanced postvaccination. We also observed proliferation of less-differentiated NK cells with downregulation of cytokine receptors as early as 3 d after vaccination, suggesting cytokine stimulation in vivo. We conclude that vaccination-induced "training" of accessory cells combines with the generation of CIML NK cells to enhance the overall NK cell response postvaccination.
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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".