P.041 Magnesium and calcium reduce severity of spatial memory impairments in kainate mouse model of mesial temporal lobe epilepsy
Bibliographic record
Abstract
Background: Calcium (Ca) and magnesium (Mg) are crucial in metabolism, excitability and neuroglial plasticity. Our aim was to evaluate whether Mg (20 mg/kg) or Ca (100 mg/kg) could improve the memory prognosis in the kainic model of mesial temporal epilepsy. Methods: Seizures were induced by systemic injection of kainate (8mg/kg) and mice were then treated by ions every 48 hours. A placebo (physiological solution) replaced kainate or ions in specific groups. Six cohorts were studied for seven weeks: control group (G0: no kainate and no ion, only placebo); untreated reference group (GR: kainate and then placebo); G1 groups were treated from the third day (G1m, G1c: kainate and then Mg/Ca); G2 groups were treated from the third week (G2m, G2c: kainate and then Mg/Ca). Radial maze and a classic maze were used for cognition evaluation. Results: The memory (short/long term) was differently affected by kainate or improved by Mg/Ca. The treated groups performed better than GR mice, but Mg was more effective. In addition, Mg demonstrated an increasing therapeutic effect over time while Ca showed an acute and apparently decreasing action in the G1c group. Conclusions: Mg should be considered for a clinical evaluation of its effect on epileptic 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".