CMV seropositivity in older adults changes the T cell repertoire, but does not prevent antibody or cellular responses to SARS-CoV-2 vaccination
Bibliographic record
Abstract
Abstract Chronic infection with human cytomegalovirus (CMV) may contribute to poor vaccine efficacy in older adults. We assessed effects of CMV serostatus on antibody quantity and quality, as well as cellular memory recall responses, after 2 and 3 SARS-CoV-2 mRNA vaccine doses, in older adults in assisted living facilities. CMV serostatus did not affect anti-Spike and anti-RBD IgG antibody levels, nor neutralization capacity against wildtype or beta variants of SARS-CoV-2 several months after vaccination. CMV seropositivity altered T cell expression of senescence-associated markers and increased T EMRA cell numbers, as has been previously reported; however, this did not impact Spike-specific CD4 + T cell memory recall responses. CMV seropositive individuals did not have a higher incidence of COVID-19, though prior infection influenced humoral immunity. Therefore, CMV seropositivity may alter T cell composition but does not impede the durability of humoral protection or cellular memory responses after SARS-CoV-2 mRNA vaccination in older adults. Key Points CMV seropositive older adults have more EMRA and terminally differentiated T cells CMV seropositivity does not prevent antibody maintenance after SARS-CoV-2 vaccination CMV seropositivity does not impede SARS-CoV-2 vaccine T cell memory recall responses
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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) | 0.003 | 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".