Propofol Facilitates Glutamatergic Transmission to Neurons of the Ventrolateral Preoptic Nucleus
Bibliographic record
Abstract
BACKGROUND: There is much evidence that the sedative component of anesthesia is mediated by gamma-aminobutyric acid type A (GABA(A)) receptors on hypothalamic neurons responsible for arousal, notably in the tuberomammillary nucleus. These GABA(A) receptors are targeted by gamma-aminobutyric acid-mediated (GABAergic) neurons in the ventrolateral preoptic area (VLPO): When these neurons become active, they inhibit the arousal-producing nuclei and induce sleep. According to recent studies, propofol induces sedation by enhancing VLPO-induced synaptic inhibition, making the target cells more responsive to GABA(A). The authors explored the possibility that propofol also promotes sedation less directly by facilitating excitatory inputs to the VLPO GABAergic neurons. METHODS: Spontaneous excitatory postsynaptic currents were recorded from VLPO cells-principally mechanically isolated, but also in slices from rats. RESULTS: In isolated VLPO GABAergic neurons, propofol increased the frequency of glutamatergic spontaneous excitatory postsynaptic currents without affecting their mean amplitude. The action of propofol was mimicked by muscimol and prevented by gabazine, respectively a specific agonist and antagonist at GABA(A) receptors. It was also suppressed by bumetanide, a blocker of Na-K-Cl cotransporter-mediated inward Cl transport. In slices, propofol also increased the frequency of spontaneous excitatory postsynaptic currents and, at low doses, accelerated firing of VLPO cells. CONCLUSION: Propofol induces sedation, at least in part, by increasing firing of GABAergic neurons in the VLPO, indirectly by activation of GABA(A) receptors on glutamatergic afferents: Because these axons/terminals have a relatively high internal Cl concentration, they are depolarized by GABAergic agents such as propofol, which thus enhance glutamate release.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".