The Immune Response Against Human Cytomegalovirus Links Cellular to Systemic Senescence
Bibliographic record
Abstract
Aging reflects long-term decline in physiological function and integrity. Changes arise at a variable pace governed by time-dependent and -independent mechanisms that are themselves complex, interdependent and variable. Molecular decay produces inferior cells that eventually dominate over healthy counterparts in tissues they comprise. In a form of biological entropy, progression from molecular through cellular to tissue level degeneration culminates in organ disease or dysfunction, affecting systemic health. To better understand time-independent contributors and their potential modulation, common biophysical bases for key molecular and cellular changes underlying age-related physiological deterioration must be delineated. This review addresses the potential contribution of cytomegalovirus (CMV)-driven T cell proliferation to cellular senescence and immunosenescence. We first describe molecular processes imposing cell cycle arrest, the foundation of cellular senescence, then focus on the unique distribution, phenotype and function of CMV-specific CD8+ T cells in the context of cellular senescence and “inflammaging”. Their features position CMV infection as a pathogenic accelerant of immune cell proliferation underlying immune senescence. In human immunodeficiency virus (HIV) infection, where increased inflammation and exaggerated anti-CMV immune responses accelerate immune senescence, CMV infection has emerged as a major factor in unhealthy aging. Thus, we speculate on mechanistic links between CMV-specific CD8+ T-cell expansion, immune senescence and prevalence of age-related disorders in HIV infection.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".