Modulating anesthetic emergence with pathway-selective dopamine signaling
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the recent preclinical findings investigating dopaminergic circuits for their involvement in reversing anesthetic-induced unconsciousness. RECENT FINDINGS: The release of dopamine from the ventral tegmental area onto dopamine D1 receptor-expressing neurons in the nucleus accumbens promotes emergence following general anesthesia. Two relevant targets of dopamine D1 receptor-expressing neurons in the nucleus accumbens include the lateral hypothalamus and ventral pallidum. Activating mesocortical dopaminergic projections from the ventral tegmental area to the prelimbic cortex has also been shown to hasten emergence from general anesthesia. In contrast, the nigrostriatal dopamine pathway is not involved in regulating anesthetic emergence. The role of the tuberoinfundibular endocrine dopamine pathway remains to be tested; however, recent studies have identified an important function of neuroendocrine signaling on modulating general anesthesia. SUMMARY: Potential avenues for accelerating anesthetic emergence may be found through targeting specific arousal-promoting pathways in the brain. Accumulating evidence from rodent studies manipulating cell type- and circuit-specific signaling pathways have identified dopamine as a potent modulator of general anesthesia. Specifically, dopamine signaling along the mesolimbic and mesocortical pathways plays a fundamental role in regulating consciousness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".