The Polyfunctionality of Human Memory CD8+ T Cells Elicited by Acute and Chronic Virus Infections Is Not Influenced by Age
Bibliographic record
Abstract
As humans age, they experience a progressive loss of thymic function and a corresponding shift in the makeup of the circulating CD8+ T cell population from naïve to memory phenotype. These alterations are believed to result in impaired CD8+ T cell responses in older individuals; however, evidence that these global changes impact virus-specific CD8+ T cell immunity in the elderly is lacking. To gain further insight into the functionality of virus-specific CD8+ T cells in older individuals, we interrogated a cohort of individuals who were acutely infected with West Nile virus (WNV) and chronically infected with Epstein Barr virus (EBV) and Cytomegalovirus (CMV). The cohort was stratified into young (<40 yrs), middle-aged (41-59 yrs) and aged (>60 yrs) groups. In the aged cohort, the CD8+ T cell compartment displayed a marked reduction in the frequency of naïve CD8+ T cells and increased frequencies of CD8+ T cells that expressed CD57 and lacked CD28, as previously described. However, we did not observe an influence of age on either the frequency of virus-specific CD8+ T cells within the circulating pool nor their functionality (based on the production of IFNγ, TNFα, IL2, Granzyme B, Perforin and mobilization of CD107a). We did note that CD8+ T cells specific for WNV, CMV or EBV displayed distinct functional profiles, but these differences were unrelated to age. Collectively, these data fail to support the hypothesis that immunosenescence leads to defective CD8+ T cell immunity and suggest that it should be possible to develop CD8+ T cell vaccines to protect aged individuals from infections with novel emerging viruses.
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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".