Barbiturates Induce Mitochondrial Depolarization and Potentiate Excitotoxic Neuronal Death
Bibliographic record
Abstract
Barbiturates are widely used as anesthetics, anticonvulsants, and neuroprotective agents. However, barbiturates may also inhibit mitochondrial respiration, and mitochondrial inhibitors are known to potentiate NMDA receptor-mediated neurotoxicity. Here we used rat cortical cultures to examine the effect of barbiturates on neuronal mitochondria and responses to NMDA receptor stimulation. The barbiturates tested, secobarbital, amobarbital, and thiamylal, each potentiated NMDA-induced neuron death at barbiturate concentrations relevant to clinical and experimental use (100-300 microm). By using rhodamine-123 under quenching conditions, barbiturates in this concentration range were shown to depolarize neuronal mitochondria and greatly amplify NMDA-induced mitochondrial depolarization. Barbiturate-induced mitochondrial depolarization was increased by the ATP synthase inhibitor oligomycin, indicating that barbiturates act by inhibiting electron transport sufficiently to cause ATP synthase reversal. Barbiturates similarly amplified the effects of NMDA on cytoplasmic free calcium concentrations. The cell-impermeant barbiturate N-glucoside amobarbital did not influence mitochondrial potential or potentiate NMDA neurotoxicity or calcium responses. However, all of the barbiturates attenuated NMDA-induced calcium elevations and cell death when present at millimolar concentrations. Whole-cell patch-clamp studies showed that these effects may be attributable to actions at the cell membrane, resulting in a block of NMDA-induced current flux at millimolar barbiturate concentrations. Together, these findings reconcile previous reports of opposing effects on barbiturates on NMDA neurotoxicity and show that barbiturate effects on neuronal mitochondria can be functionally significant. Effects of barbiturates on neuronal mitochondria should be considered in experimental and clinical application of these drugs.
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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) | 0.001 | 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 | 0.001 |
| 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".