Bibliographic record
Abstract
Functional analyses of changes in the immune response indicate that aging is associated with a decline of adaptive immunity whereas innate immunity is ramped up. Gene expression studies also support age-dependent changes in immunity. Studies using a large panel of methodologies and multiple species show that some of the most dramatic transcriptional changes that occur during aging are associated with immunity. This observation leads to two fundamental questions: (1) Why is the immune response altered with age? (2) Is this a consequence of aging or does it contribute to it? The origin of these changes and the mechanistic relationship among them as well as with aging must be identified. In mammals, this task is complicated by the interdependence of the innate and adaptive immune systems. The value of invertebrates as model organisms to help answer these questions is presented. This includes a description of the immune response in invertebrate models and how it compares with vertebrates, focusing on conserved pathways. Finally, these questions are explored in light of recent reports and data from our laboratory. Experimental alterations of longevity indicate that the differential expression of immunity-related genes during aging is linked to the rate of aging. Long-lived nematodes are more resistant to pathogens and blocking the expression of immune-related genes can prevent lifespan extension. These observations suggest that the immune response has a positive effect on longevity, possibly by increasing fitness. By contrast, it has been reported that activation of the immune system can reduce longevity upon starvation. We also observed that deregulation of the immune response has drastic effects on viability and longevity in Drosophila. These data suggest that the immune response results in a trade-off between beneficial and detrimental effects that might profoundly affect the aging process. Given this, immunity may be an ally early in life, but turns out to be an enemy as we age.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".