Requirement for interleukin‐1 to drive brain inflammation reveals tissue‐specific mechanisms of innate immunity
Bibliographic record
Abstract
The immune system is implicated in a wide range of disorders affecting the brain and is, therefore, an attractive target for therapy. Interleukin-1 (IL-1) is a potent regulator of the innate immune system important for host defense but is also associated with injury and disease in the brain. Here, we show that IL-1 is a key mediator driving an innate immune response to inflammatory challenge in the mouse brain but is dispensable in extracerebral tissues including the lung and peritoneum. We also demonstrate that IL-1α is an important ligand contributing to the CNS dependence on IL-1 and that IL-1 derived from the CNS compartment (most likely microglia) is the major source driving this effect. These data reveal previously unknown tissue-specific requirements for IL-1 in driving innate immunity and suggest that IL-1-mediated inflammation in the brain could be selectively targeted without compromising systemic innate immune responses that are important for resistance to infection. This property could be exploited to mitigate injury- and disease-associated inflammation in the brain without increasing susceptibility to systemic infection, an important complication in several neurological disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".