Impact of Aging and Cytomegalovirus on Immunological Response to Influenza Vaccination and Infection
Bibliographic record
Abstract
The number of people over the age of 60 is expected to double by 2050 according to the WHO. This emphasizes the need to ensure optimized resilience to health stressors in late life. In older adults, influenza is one of the leading causes of catastrophic disability (defined as the loss of independence in daily living and self-care activities). Influenza vaccination is generally perceived to be less protective in older adults, with some studies suggesting that the humoral immune response to the vaccine is further impaired in cytomegalovirus (CMV)-seropositive older people. CMV is a β-herpes virus infection that is generally asymptomatic in healthy individuals. The majority of older adults possess serum antibodies against the virus indicating latent infection. Age-related changes in T-cell-mediated immunity are augmented by CMV infection and may be associated with more serious complications of influenza infection. This review focuses on the impact of aging and CMV on immune cell function, the response to influenza infection and vaccination, and how the current understanding of aging and CMV can be used to design a more effective influenza vaccine for older adults. It is anticipated that efforts in this field will address the public health need for improved protection against influenza in older adults, particularly with regard to the serious complications leading to loss of independence.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".