Aging and Interferons: Impacts on Inflammation and Viral Disease Outcomes
Bibliographic record
Abstract
As highlighted by the COVID-19 global pandemic, elderly individuals comprise the majority of cases of severe viral infection outcomes and death. A combined inability to control viral replication and exacerbated inflammatory immune activation in elderly patients causes irreparable immune-mediated tissue pathology in response to infection. Key to these responses are type I, II, and III interferons (IFNs), which are involved in inducing an antiviral response, as well as controlling and suppressing inflammation and immunopathology. IFNs support monocyte/macrophage-stimulated immune responses that clear infection and promote their immunosuppressive functions that prevent excess inflammation and immune-mediated pathology. The timing and magnitude of IFN responses to infection are critical towards their immunoregulatory functions and ability to prevent immunopathology. Aging is associated with multiple defects in the ability of macrophages and dendritic cells to produce IFNs in response to viral infection, leading to a dysregulation of inflammatory immune responses. Understanding the implications of aging on IFN-regulated inflammation will give critical insights on how to treat and prevent severe infection in vulnerable individuals. In this review, we describe the causes of impaired IFN production in aging, and the evidence to suggest that these impairments impact the regulation of the innate and adaptive immune response to infection, thereby causing disease pathology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".