The Endocannabinoid System in Glial Cells and Their Profitable Interactions to Treat Epilepsy: Evidence from Animal Models
Bibliographic record
Abstract
Epilepsy is one of the most common neurological conditions. Yearly, five million people are diagnosed with epileptic-related disorders. The neuroprotective and therapeutic effect of (endo)cannabinoid compounds has been extensively investigated in several models of epilepsy. Therefore, the study of specific cell-type-dependent mechanisms underlying cannabinoid effects is crucial to understanding epileptic disorders. It is estimated that about 100 billion neurons and a roughly equal number of glial cells co-exist in the human brain. The glial population is in charge of neuronal viability, and therefore, their participation in brain pathophysiology is crucial. Furthermore, glial malfunctioning occurs in a wide range of neurological disorders. However, little is known about the impact of the endocannabinoid system (ECS) regulation over glial cells, even less in pathological conditions such as epilepsy. In this review, we aim to compile the existing knowledge on the role of the ECS in different cell types, with a particular emphasis on glial cells and their impact on epilepsy. Thus, we propose that glial cells could be a novel target for cannabinoid agents for treating the etiology of epilepsy and managing seizure-like disorders.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".