Caffeine Exacerbates Postictal Hypoxia
Bibliographic record
Abstract
A stroke-like event follows seizures which may be responsible for the postictal state and a contributing factor to the development of seizure-induced brain abnormalities and behavioral dysfunction associated with epilepsy. Caffeine is the world’s most popular drug with ∼85% of people in the USA consuming it daily. Thus, persons with epilepsy are likely to have caffeine in their body and brain during seizures. This preclinical study investigated the effects of acute caffeine on local hippocampal tissue oxygenation pre and post seizure. We continuously measured local oxygen levels in the CA1 region of the hippocampus and utilized the electrical kindling model in rats. Rats were acutely administered either caffeine, or one of its metabolites, or agonists and antagonists at adenosine sub-receptor types or ryanodine receptors prior to the elicitation of seizures. Acute caffeine administration caused a significant drop in pre-seizure hippocampal pO2. Following a seizure, caffeine, as well as two of its metabolites paraxanthine, and theophylline, increased the time below the severe hypoxic threshold (10 mmHg). Likewise, the specific A2A receptor antagonist, SCH-58261, mimicked caffeine by causing a significant drop in pre-seizure pO2 and the area and time below the severe hypoxic threshold. Moreover, the A2A receptor agonist, CGS-21680 was able to prevent the effect of both caffeine and SCH-58261 adding further evidence that caffeine is likely acting through the A2A receptor. Clinical tracking and investigations are needed to determine the effect of caffeine on postictal symptomology and blood flow in persons with 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".