Dose-dependent anticonvulsive effect of ethanol on lindane-induced seizures in ratsThis article is one of a selection of papers published in the special issue Bridging the Gap: Where Progress in Cardiovascular and Neurophysiologic Research Meet.
Bibliographic record
Abstract
Chronic ethanol consumption is a major risk factor for epilepsy, and seizures frequently occur during the withdrawal period. The aim of our study was to investigate effects of ethanol on lindane-induced seizures in rats. Male Wistar rats were injected i.p. with one of the following 5 treatments: (i) saline, (ii) dimethylsulfoxide, (iii) lindane (8 mg/kg) (L), (iv) ethanol in doses of 0.5 g/kg (E(0.5)), 1 g/kg (E(1)), and 2 g/kg (E(2)), and (v) groups that received ethanol 30 min before lindane (LE(0.5), LE(1), and LE(2)). Behavioral changes were described by using a descriptive scale as follows: 0, no response; 1, head nodding, lower jaw twitching; 2, myoclonic body jerks, bilateral forelimb clonus; 3, generalized tonic-clonic convulsions; 4, status epilepticus. The incidence of convulsions in the LE(2) group was significantly lower than the incidence in the L (p < 0.01) and LE(0.5) groups (p < 0.05). The median grade of convulsive behavior was significantly lower in the LE(2) (p < 0.01) and LE(1) groups (p < 0.05) compared with the L group. Latencies to the first seizure response were not significantly different among groups. ED50 of ethanol was 1.40 (1.19-1.65). Our findings suggest that ethanol decreased severity and incidence of lindane-induced seizures in a dose-dependent manner.
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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) | 0.000 | 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 | 0.000 |
| 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".