Specific human cytomegalovirus signature detected in NK cell metabolic changes post vaccination
Bibliographic record
Abstract
Effective vaccines for human immunodeficiency virus-1 (HIV-1) and hepatitis C virus (HCV) remain a significant challenge for these infectious diseases. Given that the innate immune response is key to controlling the scale and nature of developing adaptive immune responses, targeting natural killer (NK) cells that can promote a T-helper type 1 (Th1)-type immune response through the production of interferon-γ (IFNγ) remains an untapped strategic target for improved vaccination approaches. Here, we investigate metabolic and functional responses of NK cells to simian adenovirus prime and MVA boost vaccination in a cohort of healthy volunteers receiving a dual HCV-HIV-1 vaccine. Early and late timepoints demonstrated metabolic changes that contributed to the sustained proliferation of all NK cells. However, a strong impact of human cytomegalovirus (HCMV) on some metabolic and functional responses in NK cells was observed in HCMV seropositive participants. These changes were not restricted to molecularly defined adaptive NK cells; indeed, canonical NK cells that produced most IFNγ in response to vaccination were equally impacted in individuals with latent HCMV. In summary, NK cells undergo metabolic changes in response to vaccination, and understanding these in the context of HCMV is an important step towards rational vaccine design against a range of human viral pathogens.
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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".