Opioid Receptors Gene Polymorphism and Heroin Dependence in Iran
Bibliographic record
Abstract
INTRODUCTION: Genes often have multiple polymorphisms that interact with each other and the environment in different individuals. Variability in the opioid receptors can influence opiate withdrawal and dependence. In humans, A118G Single Nucleotide Polymorphisms (SNP) on μ-Opioid Receptor (MOR), 36 G>T in κ-Opioid Receptor (KOR), and T921C in the δ-Opioid Receptor (DOR) have been found to associate with substance dependence. METHODS: To investigate the association between opioid receptors gene polymorphism and heroin addiction, 100 control subjects with no history of opioid use, and 100 heroin addicts (50% males and 50% females) in Tehran (capital of Iran), were evaluated. A118G, 36 G>T, and T921C SNPs on the MOR, KOR, DOR genes, respectively, were genotyped by sequencing. RESULTS: We found no differences in either allele or genotype frequency for MOR, KOR and DOR genes SNPs between controls and subjects addicted to heroin. CONCLUSION: The relationships among polymorphisms may be important in determining the risk profile for complex diseases such as addiction, but opioid addiction is a multifactorial syndrome which is partially hereditary and partially affected by the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".