P.016 A novel de novo GABRA1 mutation linked to epileptic encephalopathy: pathophysiology and potential therapeutic options
Bibliographic record
Abstract
Background: Epileptic encephalopathy (EE) is a severe neurological disorder characterized by treatment-resistant seizures and poor neurodevelopmental outcomes. EE is associated with mutant genes, including those that encode for γ-aminobutyric acid type A (GABAA) receptor subunits. We identified a novel de novo GABRA1 mutation in a patient with EE, characterized its impact on GABAA receptor function, and sought potential therapeutic options. Methods: We described the clinical and electrophysiological features of a patient with a novel de novo GABRA1 (R214C) mutation; performed functional studies; and determined the effect of diazepam and insulin on wild type and mutant GABAA receptors. Results: The patient is a 10-year-old girl with EE, treatment-resistant seizures, intellectual disability and autism. Her GABRA1 (R214C) mutation dramatically decreased whole-cell GABA-evoked currents by reducing GABAA surface receptors, decreasing single channel open time, and altering channel kinetic properties. The combination of diazepam and insulin partially repaired these effects by enhancing channel activity and increasing the number of surface receptors, respectively. Conclusions: Diazepam and insulin partially mitigated a de novo GABRA1 (R214C) mutation’s effects on GABAA receptor number and function. Given the risks of insulin use, pharmacological agents with similar mechanisms of action but fewer side effects, such as IGF-1, should be studied and considered for clinical application.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".