Neurosteroids and Focal Epileptic Disorders
Bibliographic record
Abstract
Neurosteroids are a family of compounds that are synthesized in principal excitatory neurons and glial cells, and derive from the transformation of cholesterol into pregnenolone. The most studied neurosteroids—allopregnanolone and allotetrahydrodeoxycorticosterone (THDOC)—are known to modulate GABAA receptor-mediated transmission, thus playing a role in controlling neuronal network excitability. Given the role of GABAA signaling in epileptic disorders, neurosteroids have profound effects on seizure generation and play a role in the development of chronic epileptic conditions (i.e., epileptogenesis). We review here studies showing the effects induced by neurosteroids on epileptiform synchronization in in vitro brain slices, on epileptic activity in in vivo models, i.e., in animals that were made epileptic with chemoconvulsant treatment, and in epileptic patients. These studies reveal that neurosteroids can modulate ictogenesis and the occurrence of pathological network activity such as interictal spikes and high-frequency oscillations (80–500 Hz). Moreover, they can delay the onset of spontaneous seizures in animal models of mesial temporal lobe epilepsy. Overall, this evidence suggests that neurosteroids represent a new target for the treatment of focal epileptic disorders.
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 | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".