Repeated episodes of postictal hypoxia are a mechanism for interictal cognitive impairments
Bibliographic record
Abstract
Comorbidities during the period between seizures present a significant challenge for individuals with epilepsy. Despite their clinical relevance, the pathophysiology of the interictal symptomatology is largely unknown. Postictal severe hypoxia (PIH) in those brain regions participating in the seizure has been indicated as a mechanism underlying several negative postictal manifestations. It is unknown how repeated episodes of PIH affect interictal symptoms in epilepsy. Using a rat model, we observed that repeated seizures consistently induced episodes of PIH that become increasingly severe with each seizure occurrence. Additionally, recurrent seizure activity led to decreased levels of oxygen in the hippocampus during the interictal period. However, these reductions were prevented when we repeatedly blocked PIH using either the COX-inhibitor acetaminophen or the L-type calcium channel antagonist nifedipine. Moreover, we found that interictal cognitive deficits caused by seizures were completely alleviated by repeated attenuation of PIH events. Lastly, mitochondrial dysfunction may contribute to the observed pathological outcomes during the interictal period. These findings provide evidence that seizure-induced hypoxia may play a crucial role in several aspects of epilepsy. Consequently, developing and implementing treatments that specifically target and prevent PIH could potentially offer significant benefits for individuals with refractory epilepsy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".