Functional Changes of T-Cell Subsets with Age and CMV Infection
Bibliographic record
Abstract
Cytomegalovirus (CMV) latent infection and aging contribute to alterations in the function and phenotype of the T-cell pool. We have demonstrated that CMV-seropositivity is associated with the expansion of polyfunctional CD57+ T-cells in young and middle-aged individuals in response to different stimuli. Here, we expand our results on the effects of age and CMV infection on T-cell functionality in a cohort of healthy middle-aged and older individuals stratified by CMV serostatus. Specifically, we studied the polyfunctional responses (degranulation, IFN-γ and TNF-α production) of CD4+, CD8+, CD8+CD56+ (NKT-like), and CD4-CD8- (DN) T-cells according to CD57 expression in response to Staphylococcal Enterotoxin B (SEB). Our results show that CD57 expression by T-cells is not only a hallmark of CMV infection in young individuals but also at older ages. CD57+ T-cells are more polyfunctional than CD57- T-cells regardless of age. CMV-seronegative individuals have no or a very low percentages of cytotoxic CD4+ T-cells (CD1017a+) and CD4+CD57+ T-cells, supporting the notion that the expansion of these T-cells only occurs in the context of CMV infection. There was a functional shift in T-cells associated with CMV seropositivity, except in the NKT-like subset. Here, we show that the effect of CMV infection and age differ among T-cell subsets and that CMV is the major driving force for the expansion of highly polyfunctional CD57+ T-cells, emphasizing the necessity of considering CMV serology in any study of immunosenescence.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".