Alcohol‐withdrawn animals have a prolonged increase in dopamine D2<sup>High</sup>receptors, reversed by general anesthesia: Relation to relapse?
Bibliographic record
Abstract
The biochemical basis for alcohol addiction and relapse is not known. Although ethanol promotes the release of dopamine like other drugs of abuse, many unknown factors remain to be investigated concerning the biochemical abnormalities which persist after ethanol drinking and which contribute to alcohol relapse. Although ethanol withdrawal is associated with enhanced sensitivity to dopamine in animals and humans, only minor changes in the striatal density of dopamine D2 receptors have been found in humans, and animals show a small reduction in striatal D2 receptors. But how can dopamine-related functions be increased in ethanol withdrawal in the face of an unchanged or reduced density of dopamine D2 receptors? Considering that ethanol sensitizes rats to amphetamine, and that the high-affinity state of D2, or D2High, is markedly increased in striata from amphetamine-sensitized rats, we measured the density of D2High in striata from rats withdrawn from ethanol. These sites were elevated by 360% (7.2 pmol/g) for at least 8 days after stopping ethanol and returned to normal levels of 2 pmol/g after 2 weeks of ethanol withdrawal. In addition, 1 h of deep general anesthesia given 5 days into withdrawal resulted in a normal level of D2High within 24 h. Because the D2High states are the functional form of D2, their elevated density in ethanol withdrawal may be related to ethanol relapse in humans. General anesthesia may alleviate aspects of alcohol or amphetamine abuse or psychosis associated with elevated D2High.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".