Low Concentrations of Pentobarbital Enhance Excitability of Rat Hippocampal Neurons
Bibliographic record
Abstract
BACKGROUND: Although the excitation phase observed during anesthetic induction and emergence is familiar to anesthesiologists, the cellular mechanisms of this phenomenon are not well understood. At anesthetic concentrations approximately one-tenth those required for surgical anesthesia, subjects demonstrate increased responsiveness to noxious stimulation. We previously estimated that the decrease in nociceptive reflex threshold is maximal at pentobarbital concentrations of approximately 5 microM. Here we used the rat hippocampal slice preparation to examine whether 5 microM pentobarbital increases the excitability of neurons. METHODS: Intracellular recordings were obtained from CA1 neurons during stimulation of the Schaffer collateral pathway. We examined the effect of pentobarbital on resting intrinsic membrane properties and stimulus-response relationships. Excitability was evaluated with the relationship between the synaptic signal strength, as indicated by the excitatory postsynaptic potential slope, and the probability of spiking (E-S relationship). RESULTS: Pentobarbital increased the excitability of hippocampal neurons, as shown by an increased probability of spiking at any given synaptic signal strength (P = 0.002), an effect known as "E-S potentiation." Pentobarbital was associated with an increase in the input resistance of the neuron and a shift of the action potential threshold towards more negative values. Pentobarbital did not increase the excitatory postsynaptic potential slope at any given stimulus strength. CONCLUSIONS: At a 5 microM concentration, pentobarbital increased E-S coupling by enhancing the excitability of the postsynaptic neurons. Pentobarbital induced changes in intrinsic membrane properties that may contribute to increased excitability.
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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".