Lipopolysaccharide‐induced Febrile Convulsions in the Rat: Short‐term Sequelae
Bibliographic record
Abstract
PURPOSE: Febrile convulsions (FCs) occur in children as a result of fever. The mechanisms involved in the genesis of FCs and their long-term consequences on brain development remain unclear. We have developed a model of FC, by using fever as a parameter, to test the hypothesis that fever can lower seizure threshold and to examine the neurologic sequelae of FCs. METHODS: Fourteen-day-old rat pups equipped with body-temperature telemetry devices exhibited approximately 1.5 degrees C fevers after lipopolysaccharide (Escherichia coli, 200 microg/kg). During such fevers, concurrently administered doses of kainic acid that are normally subconvulsant were used to induce convulsions with fever. Animals were then killed at varying times for pathological and immunohistochemical studies. RESULTS: The pairing of lipopolysaccharide and subconvulsant kainic acid resulted in convulsions in approximately 50% of febrile animals, with very low mortality. To study the neural correlates of these FCs, we used fos immunohistochemistry and found that animals with FCs had fos-positive immunoreactivity in brain regions involved in seizures. After a period of 72 h, we also examined brains for pathologic changes and found no differences among our groups. CONCLUSIONS: Our data indicate that a neuroimmune challenge and its accompanying fever reduce the seizure threshold. Furthermore, the FCs induced by fever in this model do not have short-term adverse effects on the brain. In addition, this model, by incorporating physiologic fever, may be useful for examining the role of fever and its constituent mediators in the genesis of FCs.
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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 | 0.000 |
| 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".