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Amphetamine-Related Disorders

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[Identification of Methamphetamine Abuse and Selegiline Use: Chiral Analysis of Methamphetamine and Amphetamine in Urine].

Fa yi xue za zhi
OBJECTIVES: To study the content variation of selegiline and its metabolites in urine, and based on actual cases, to explore the feasibility for the identification of methamphetamine abuse and selegiline use by chiral analysis.

Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.

Drug and alcohol dependence
BACKGROUND: Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited...

Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin labeling.

Addiction biology
Arterial spin labeling (ASL) magnetic resonance imaging has been widely applied to identify cerebral blood flow (CBF) abnormalities in a number of brain disorders. To evaluate its significance in detecting methamphetamine (MA) dependence, this study ...

Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study w...

Coupling analysis of diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) with abnormal cerebral blood flow in methamphetamine-dependent patients and its application in machine-learning-based classification.

Journal of affective disorders
BACKGROUND: Diffusion tensor imaging (DTI) analysis along the perivascular space (ALPS) index is currently widely employed to evaluate the neurophysiological activity in various neuropsychiatric disorders. However, there remains a scarcity of studies...

A deep learning model for characterizing altered gyro-sulcal functional connectivity in abstinent males with methamphetamine use disorder and associated emotional symptoms.

Cerebral cortex (New York, N.Y. : 1991)
Failure to manage emotional withdrawal symptoms can exacerbate relapse to methamphetamine use. Understanding the neuro-mechanisms underlying methamphetamine overuse and the associated emotional withdrawal symptoms is crucial for developing effective ...