What goes up must come down: dynamics of type 1 interferon signaling across the lifespan
Bibliographic record
Abstract
Type 1 interferons (T1IFNs) are typically expressed in low concentrations under homeostatic conditions, but upon pathogenic insult or perturbation of the pathway, these critical immune signaling molecules can become either protectors from or drivers of pathology. While essential for initiating antiviral defense and modulating inflammation, dysregulation of T1IFN signaling can contribute to immunopathology, making it and its associated pathways prime targets for immune evasion and disruption by pathogens. This review focuses on the changes in T1IFN signaling across the lifespan, with particular emphasis on the role of the Stimulator of Interferon Genes (STING) pathway in autoimmune and infectious disease susceptibility, especially in the context of viral infections. Aging is associated with diminished T1IFN responsiveness, partially resulting from chronic stimulation of the STING pathway, which contributes to increased susceptibility and impaired viral clearance. Conversely, neonates and young children also show increased vulnerability to certain viral infections, but whether this is driven by T1IFN differences or another mechanism remains incompletely understood. Despite growing interest in T1IFN-based immunotherapies, pediatric and elderly populations remain underrepresented in clinical trials. Here, we advocate for a deeper molecular and systems understanding of how the interferon response evolves across the human lifespan, to inform age-tailored therapeutic approaches and more inclusive study designs, thereby improving outcomes in both the youngest and oldest patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".