Selective GABA‐receptor actions of amobarbital on thalamic neurons
Bibliographic record
Abstract
1. We studied amobarbital's effects on membrane properties and currents, and electrically evoked inhibitory postsynaptic currents (IPSCs) mediated by gamma-aminobutyric acid (GABA) in rat thalamic slices. Using concentration-response relationships, we compared amobarbital's effects in nociceptive nuclei and non-nociceptive nucleus reticularis thalami (nRT). 2. Amobarbital decreased input resistance by activating GABA(A) receptors. Amobarbital produced a larger decrease in ventrobasal than nRT neurons. 3. Amobarbital depressed burst and tonic firing. Depression of burst firing was more effective, particularly in ventrobasal and intralaminar neurons. Depression was reversed by GABA(A) antagonists, and surmountable by increasing current injection, implicating a receptor-mediated shunt mechanism. 4. Amobarbital did not affect the tetrodotoxin-isolated low threshold Ca(2+) spike during GABA(A) blockade. Amobarbital reduced excitability without altering outward leak, or hyperpolarisation-activated inward currents. 5. Amobarbital increased mean conductance and burst duration of single GABA(A) channels. Consistent with this, amobarbital increased amplitude and decay time of IPSCs with distinct EC(50)s, implicating actions at two GABA(A) receptor sites. 6. Activation of GABA(A) receptors by low concentrations, fast IPSC amplitude modulation, and failure to affect intrinsic currents distinguished amobarbital's mechanism of action from previously characterised barbiturates. The selective actions of amobarbital on GABA(A) receptor may have relevance in explaining anaesthetic and analgesic uses.
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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 | 0.000 |
| 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".