Event-Associated Oxygen Consumption Rate Increases ca. Five-Fold When Interictal Activity Transforms into Seizure-Like Events In Vitro
Bibliographic record
Abstract
Neuronal injury due to seizures may result from a mismatch of energy demand and adenosine triphosphate (ATP) synthesis. However, ATP demand and oxygen consumption rates have not been accurately determined, yet, for different patterns of epileptic activity, such as interictal and ictal events. We studied interictal-like and seizure-like epileptiform activity induced by the GABAA antagonist bicuculline alone, and with co-application of the M-current blocker XE-991, in rat hippocampal slices. Metabolic changes were investigated based on recording partial oxygen pressure, extracellular potassium concentration, and intracellular flavine adenine dinucleotide (FAD) redox potential. Recorded data were used to calculate oxygen consumption and relative ATP consumption rates, cellular ATP depletion, and changes in FAD/FADH2 ratio by applying a reactive-diffusion and a two compartment metabolic model. Oxygen-consumption rates were ca. five times higher during seizure activity than interictal activity. Additionally, ATP consumption was higher during seizure activity (~94% above control) than interictal activity (~15% above control). Modeling of FAD transients based on partial pressure of oxygen recordings confirmed increased energy demand during both seizure and interictal activity and predicted actual FAD autofluorescence recordings, thereby validating the model. Quantifying metabolic alterations during epileptiform activity has translational relevance as it may help to understand the contribution of energy supply and demand mismatches to seizure-induced injury.
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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".