Neuroinflammatory targets and treatments for epilepsy validated in experimental models
Bibliographic record
Abstract
A large body of evidence that has accumulated over the past decade strongly supports the role of inflammation in the pathophysiology of human epilepsy. Specific inflammatory molecules and pathways have been identified that influence various pathologic outcomes in different experimental models of epilepsy. Most importantly, the same inflammatory pathways have also been found in surgically resected brain tissue from patients with treatment-resistant epilepsy. New antiseizure therapies may be derived from these novel potential targets. An essential and crucial question is whether targeting these molecules and pathways may result in anti-ictogenesis, antiepileptogenesis, and/or disease-modification effects. Therefore, preclinical testing in models mimicking relevant aspects of epileptogenesis is needed to guide integrated experimental and clinical trial designs. We discuss the most recent preclinical proof-of-concept studies validating a number of therapeutic approaches against inflammatory mechanisms in animal models that could represent novel avenues for drug development in epilepsy. Finally, we suggest future directions to accelerate preclinical to clinical translation of these recent discoveries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".