Bibliographic record
Abstract
PURPOSE OF REVIEW: Neurosteroids are a family of compounds synthesized directly in the brain by transforming cholesterol into pregnenolone, which is then converted to compounds such as allopregnanolone and allotetrahydrodeoxycorticosterone. In view of their ability to modulate neurotransmission, neurosteroids may influence the clinical course of epileptic disorders. In this review, we highlight two emerging properties of neurosteroids, that is, their anticonvulsant and antiepileptogenic activities. RECENT FINDINGS: It has been shown that fluctuations in neurosteroid synthesis, such as those seen in response to stress or during the ovarian cycle, determine an increase in seizure threshold. Moreover, increased neurosteroid synthesis, presumably occurring in glial cells during epileptogenesis, delays the appearance of recurrent spontaneous seizures in an animal model of temporal lobe epilepsy; such an effect may be due to augmented tonic gamma-aminobutyric acid type A receptor-mediated inhibition. Finally, clinical trials with ganaxolone, an allopregnanolone analogue, have demonstrated beneficial effects in pharmacoresistant epileptic patients, whereas finasteride--which interferes with neurosteroid synthesis - facilitates seizures in catamenial epilepsy. SUMMARY: The overall evidence suggests that neurosteroids may represent a novel therapeutic strategy in epileptic disorders and a future perspective to control epileptogenicity.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".