Herbal treatment following post‐seizure induction in rat by lithium pilocarpine: <i>Scutellaria lateriflora</i> (Skullcap), <i>Gelsemium sempervirens</i> (Gelsemium) and <i>Datura stramonium</i> (Jimson Weed) may prevent development of spontaneous seizures
Bibliographic record
Abstract
About 1 week after the induction of status epilepticus in male rats by a single systemic injection of lithium (3 mEq/kg) and pilocarpine (30 g/kg), rats were continuously administered one of three herbal treatments through the water supply for 30 days. A fourth group received colloidal minerals and diluted food grade hydrogen peroxide in tap water, while a fifth group of rats received only tap water (control). Herbal treatments were selected for their historical antiseizure activities and sedative actions on the nervous system. The numbers of spontaneous seizures per day during a 15 min observation interval were recorded for each rat during the treatment period and during an additional 30 days when only tap water was given. Rats that received a weak solution of the three herbal fluid extracts of Scutellaria lateri flora (Skullcap), Gelsemium sempervirens (Gelsemium) and Datura stramonium (Jimson Weed) displayed no seizures during treatment while all the other groups were not seizure-free. However, when this treatment was removed, the rats in this group displayed numbers of spontaneous seizures comparable to the controls. Although there is no proof that herbal remedies can control limbic or temporal lobe epilepsy, the results of this experiment strongly suggest that the appropriate combination of herbal compounds may be helpful as adjunctive interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".