Mechanisms of general anesthesia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Anesthetics influence a wide variety of transmitter- and voltage-gated ion channels in the mammalian central nervous system. At the molecular level, the gamma-aminobutyric acid (GABA) subtype A receptor has emerged as a primary therapeutic target. This review highlights recent advances in our understanding of how anesthetics modify GABA(A) receptor function. RECENT FINDINGS: Anesthetics bind to discrete selective binding sites on GABA(A) receptors--a discovery that challenges lipid-based theories of anesthesia. Not all GABA(A) receptors are equally sensitive to anesthetics because positive allosteric modulation is critically dependent on receptor subunit composition. Moreover, GABA(A) receptors located in extrasynaptic regions of hippocampal neurons display a greater sensitivity to propofol and benzodiazepines than do receptors located in subsynaptic regions. Enhancement in GABAergic inhibition may not account for all of the behavioral end-points associated with the anesthetic state. In particular, the immobilizing properties of anesthetics may not be solely mediated by GABA(A) receptors. Finally, synthetic neurosteroids are being developed as improved general anesthetics. SUMMARY: Detailed insights into anesthetic-GABA(A) receptor interactions have resulted in intense efforts to develop safer drugs that selectively target subtypes of GABA(A) receptors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".