Bibliographic record
Abstract
Methamphetamine (METH) addiction has been widely spread and caused severe problems both in society and public health in recent years, but there is a shortage of medication available. The naltrexone (NTX) as a non-selective opioid receptor antagonist has been widely applied to treat alcohol addiction and the relapse to opioid addiction after detoxification. In the present study, we investigated the potent pharmacotherapeutic effect of NTX in attenuating relapse to drug-seeking behavior in the METH self-administration and conditioned place preference (CPP) in rats. The results showed that acute intragastrical administration of NTX (40 mg/kg) significantly reduced cue-induced drug-seeking behavior after extinction training. The similar inhibition effect was observed in the CPP model, that the intragastrical administration of NTX (30 mg/kg) significantly disrupted the reactivation induced by intraperitoneal injection of METH (0.5 mg/kg) after the extinction training process. However, respective intragastrical administration of NTX (20 or 40 mg/kg) failed to alter the dose-response curve of METH under fixed ratio 2 program and intraperitoneal injection of METH (1.0 mg/kg)-induced reinstatement in rats self-administration. Overall, our findings suggest that NTX has the pharmacotherapeutic potential in reducing the relapse of METH addiction, which deserves further investigation as a promising medication for the treatment of METH addiction.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.647 | 0.251 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".