Causal Links between Brain Cytokines and Experimental Febrile Convulsions in the Rat
Bibliographic record
Abstract
PURPOSE: Despite the prevalence of febrile convulsions (FCs), their pathophysiology has remained elusive. We tested the hypothesis that components of the immune response, particularly the proinflammatory cytokine interleukin-1beta (IL-1beta) and its naturally occurring antagonist interleukin-1 receptor antagonist (IL-1ra) may play a role in the genesis of FC. METHODS: Postnatal day 14 rats were treated with lipopolysaccharide (LPS; 200 microg/kg, i.p.) followed by a subconvulsant dose of kainic acid (1.75 mg/kg, i.p.). Brains were harvested at and 2 h after onset of FCs to measure brain levels of IL-1beta and IL-1ra. Separate groups of animals were given intracerebroventricular (ICV) injections of IL-1beta, or IL-1ra in an attempt to establish a causal relation between the IL-1beta/IL-1ra system and FCs. RESULTS: Animals with FCs showed increased IL-1beta in the hypothalamus and hippocampus but not in the cortex compared with noFC animals that also received LPS and kainic acid. This increase was first detected in the hippocampus at onset of FCs. No detectable difference in IL-1ra was found in brain regions examined in either group. When animals were treated with IL-1beta ICV, a dose-dependant increase was noted in the proportion of animals that experienced FCs, whereas increasing doses of IL-1ra, given to separate groups of animals, were anticonvulsant. CONCLUSIONS: Our results suggest that excessive amounts of IL-1beta may influence the genesis of FCs. This may occur by overproduction of IL-1beta, or by alteration in the IL-1beta/IL-1ra ratio in the brain after an immune challenge.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".