Isoflurane prevents acquired epilepsy in rat models of temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: Acquired epilepsy is a devastating long-term risk of various brain insults, including trauma, stroke, infections, and status epilepticus (SE). There is no preventive treatment for patients at risk. Attributable to the complex alterations involved in epileptogenesis, it is likely that multitargeted approaches are required for epilepsy prevention. We report novel preclinical findings with isoflurane, which exerts various nonanesthetic effects that may be relevant for antiepileptogenesis. METHODS: The effects of isoflurane were investigated in two rat models of SE-induced epilepsy: intrahippocampal kainate and systemic administration of paraoxon. Isoflurane was either administered during (kainate) or after (paraoxon) induction of SE. Magnetic resonance imaging was used to assess blood-brain barrier (BBB) dysfunction. Positron emission tomography was used to visualize neuroinflammation. Long-term electrocorticographic recordings were used to monitor spontaneous recurrent seizures. Neuronal damage was assessed histologically. RESULTS: In the absence of isoflurane, spontaneous recurrent seizures were common in the majority of rats in both models. When isoflurane was administered during kainate injection, duration and severity of SE were not affected, but only few rats developed spontaneous recurrent seizures. A similar antiepileptogenic effect was found when paraoxon-treated rats were exposed to isoflurane after SE. Moreover, in the latter model, isoflurane prevented BBB dysfunction and neurodegeneration, whereas isoflurane reduced neuroinflammation in the kainate model. INTERPRETATION: Given that isoflurane is a widely used volatile anesthetic, and is used for inhalational long-term sedation in critically ill patients at risk to develop epilepsy, our findings hold a promising potential to be successfully translated into the clinic. Ann Neurol 2016;80:896-908.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".