<i>Monoamine oxidase-A</i> polymorphisms might modify the association between the <i>dopamine D</i><i><sub>2</sub></i><i> receptor</i> gene and alcohol dependence
Bibliographic record
Abstract
Objective: Low monoamine oxidase (MAO) activity and the neurotransmitter dopamine are 2 important factors in the development of alcohol dependence. MAO is an important enzyme associated with the metabolism of biogenic amines. Therefore, the present study investigates whether the association between the dopamine D2 receptor ( DRD2) gene and alcoholism is affected by different polymorphisms of the MAO type A ( MAOA) gene. Methods: A total of 427 Han Chinese men in Taiwan (201 control subjects and 226 with alcoholism) were recruited for the study. Of the subjects with alcoholism, 108 had pure alcohol dependence (ALC) and 118 had both alcohol dependence and anxiety, depression or both (ANX/DEP ALC). All subjects were assessed with the Chinese Version of the Modified Schedule of Affective Disorders and Schizophrenia-Lifetime. Alcohol dependence, anxiety and major depressive disorders were diagnosed according to Diagnostic and Statistical Manual of Mental Disorders, fourth edition criteria. Conclusion: The genetic variant of the DRD2 gene was only associated with the ANX/DEP ALC phenotype, and the genetic variant of the MAOA gene was associated with pure ALC. Subjects carrying the MAOA 3-repeat allele and genotype A1/A1 of the DRD2 were 3.48 times (95% confidence interval = 1.47–8.25) more likely to be ANX/DEP ALC than the subjects carrying the MAOA 3-repeat allele and DRD2 A2/A2 genotype. The MAOA gene may modify the association between the DRD2 gene and ANX/DEP ALC phenotype.
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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) | 0.000 | 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 | 0.000 |
| 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".