Latent Infection with Cytomegalovirus Is Associated with Poor Memory CD4 Responses to Influenza A Core Proteins in the Elderly
Bibliographic record
Abstract
Influenza remains a major pathogen in older people. Infection with CMV and the accumulation of late-differentiated T cells associated with it have been implicated in poor Ab responsiveness to influenza vaccination in the elderly, most of whom are CMV positive. However, whether CMV infection also affects memory T cell responses to influenza remains unknown. To investigate this, we assessed T cell responses to influenza A matrix protein and nucleoprotein ex vivo in 166 Dutch individuals (mean age 62.2 y, range 42-82) and validated the results in a second cohort from North America (mean age 73.1 y, range 65-81, n = 28). We found that less than half of the CMV-infected older subjects mounted a CD4 T cell response to influenza Ags, whereas ∼80% of uninfected elderly did so. A similar proportion of younger subjects possessed influenza A virus-responsive CD4 T cells, and, interestingly, this was the case whether they were CMV-infected. Thus, the effect of CMV was only seen in the older donors, who may have been exposed to the virus for decades. The percentage of donors with CD8 responses to influenza A virus was lower than those with CD4; this was not influenced by whether the subjects were CMV seropositive or seronegative. CMV-seropositive responders had significantly higher frequencies of late-differentiated CD4 T-cells (CD45RA(+/-)CCR7(-)CD27(-)CD28(-)) compared with CMV-infected nonresponders. These data add to the accumulating evidence that infection with CMV has profound but heterogeneous effects on responses to the products of other viruses and have implications for the design of influenza vaccines, especially in the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".