Diverse effects of variant doses of dexamethasone in lithium–pilocarpine induced seizures in rats
Bibliographic record
Abstract
Corticosteroids are used in the management of several epileptic aliments; however, their effectiveness in combating seizures remains controversial, with pro- and anti-convulsive effects ascribed. The current study aimed to address the modulatory effect of dexamethasone (DEX) utilizing 3 dose levels (5, 10, and 20 mg/kg body mass of male Wistar rat) in the rat lithium-pilocarpine (Li-PIL) epilepsy model. Li-PIL induced seizures that were associated with neuronal cell loss in the CA3 region, and increased prostaglandin (PG)E(2), tumor necrosis factor (TNF)-α, interleukin (IL)-10, nitric oxide, and neutrophil infiltration in the hippocampus. However, Li-PIL compromised the oxidant-antioxidant balance of the hippocampus. Effective anticonvulsant activity was only observed with 10 mg DEX/kg body mass, which reduced seizure production and incidence, as well as neuronal cell loss in the CA3 region. At this anticonvulsant dose, enhancements in the antioxidant system and IL-10, as well as suppression of altered inflammatory markers were observed. Conversely, doubling the dose showed a tendency to shorten seizure latency, and neither affected seizure incidence nor CA3 neuronal cell loss. These effects were associated with an increase in levels of PGE(2) and TNF-α. The present study found a lack of protection at 5 mg DEX/kg body mass, an anticonvulsant effect at 10 mg/kg, and a loss of protection at 20 mg/kg in the Li-PIL epilepsy model, which indicates that there is an optimal dose of DEX for preventing the induction of seizures.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".