2-Arachidonoylglycerol enrichment Reduced Epileptiform Activity of the Rat Hippocampus induced with Pentylenetetrazol
Bibliographic record
Abstract
Background & Objective: 2-arachidonoylglycerol (2-AG) and anandamide (AEA) are two major endocannabinoids.Using inhibitors of the enzymatic pathways involved in the elimination of 2-AG and AEA as well as synthetic 2-AG, we examined the effectiveness of these endocannabinoids on epileptiform activity induced in Wistar rats by pentylenetetrazol (PTZ). Materials & Methods:Adult male Wistar rats were used in this study.Epileptiform activity was induced in adult male Wistar rats by PTZ injection (20 mg/kg, i.p.).To inhibit 2-AG degradation WWL70 and JJKK048 (JJKK048: 1 mg/kg, WWL70: 5 mg/kg, i.p.) were used.To inhibit AEA elimination, URB597 and LY2183240 (URB597: 1 mg/kg, LY2183240: 2.5 mg/kg, i.p.) were used.Synthetic 2-AG was also examined (1 mg/kg, i.p.) before the PTZ injection.All drugs were dissolved in DMSO as vehicle and injected (i.p.) 15 minutes before the PTZ injection.Latency to onset and duration of the epileptiform activity were considered for statistical analysis.Results: Injection of (JJKK048+WWL70) before the PTZ significantly increased latency to onset of the epileptiform activity (P<0.01), while reduced duration of the epileptiform activity in comparison to the vehicle (P<0.05).In addition, 2-AG administration significantly increased latency to onset of the epileptiform activity (P<0.05) and reduced duration of the epileptiform activity in comparison to the vehicle (P<0.01).However, these indexes did not show significant changes when URB597+LY2183240 were injected before the PTZ (P>0.05). Conclusion:It seems increased level of 2-AG but not AEA, effectively decreases PTZ induced epileptiform activity of the hippocampus.
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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) | 0.002 | 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".