Aging of the immune system: Focus on inflammation and vaccination
Bibliographic record
Abstract
Major advances in preventing, delaying, or curing individual pathologies are responsible for an increasingly long life span in the developed parts of our planet, and indeed reaching eight to nine decades of life is nowadays extremely frequent. However, medical and sanitary advances have not prevented or delayed the underlying cause of the disparate pathologies occurring in the elderly: aging itself. The identification of the basis of the aging processes that drives the multiple pathologies and loss of function typical of older individuals is a major challenge in current aging research. Among the possible causes, an impairment of the immune system plays a major role, and indeed numerous studies have described immunological changes which occur with age. Far from the intention of being exhaustive, this review will focus on recent advances and views on the role that modifications of cell signalling and remodelling of the immune response play during human aging and longevity, paying particular attention to phenomena which are linked to the so called inflammaging process, such as dysregulation of innate immunity, altered T-cell or B-cell maturation and differentiation, as well as to the implications of immune aging for vaccination strategies in the elderly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".