Contents
Special Issue Topic

The Biological Basis of Substance Use Disorders

Guest Editor

Dr. Richard M. Sherva E-Mail

Boston University School of Medicine, Boston, USA

Research Keywords: Psychiatric genetics, substance use disorders, Alzheimer’s disease, statistical genetics

About the Special lssue

Substance use disorders, ranging from nicotine addiction to the current opiate epidemic, are a profound public health burden. Although human and animal studies have made advances towards understanding the biological systems driving substance use, misuse, addiction, and recovery, effective prevention and treatment strategies are still lacking. A better understanding of the psychological and neurological factors driving addiction risk, including its underlying genes and regulatory mechanisms, may lead to additional targets for and a more personalized approach to treatment.

Keywords: Substance use disorders, addiction, personalized medicine, psychiatric genetics, neurology, pharmacokinetics

Published Articles

Open Access Perspective
Diabetes and substance use: a perspective within drug rehabilitation
Diabetes mellitus has become increasingly more common and diagnosed within the global population. Coupled with the continued prevalence of substance use, there are some distinct considerations for u
Published: October 08, 2023 Explor Med. 2023;4:664–669
4535 37 0
Open Access Original Article
The brain activities of individuals with or without motivation to change: a preliminary study among cigarette smokers
Aim: Cigarette smoking is an addictive behavior that requires high motivation to change, a phenotype related to the functional activity of the brain. The study aims to examine motiv
Published: August 30, 2023 Explor Med. 2023;4:441–452
1176 12 0
Open Access Original Article
Associations between methamphetamine and alcohol use disorder, suicidal ideation, and lowered quality of life in methamphetamine users
Aim: There is a strong comorbidity between methamphetamine (MA) and alcohol use whereby MA use may contribute to increased alcohol consumption. This study aims to determine the associations betwe
Published: June 30, 2023 Explor Med. 2023;4:409–420
1777 51 0
Open Access Original Article
Genome-wide association study of phenotypes measuring progression from first cocaine or opioid use to dependence reveals novel risk genes
Aim: Substance use disorders (SUD) result in substantial morbidity and mortality worldwide. Opioids, and to a lesser extent cocaine, contribute to a large percentage of this health burden. Despite their high heritability, few genetic risk loci have been identified for either opioid or cocaine dependence (OD or CD, respectively). A genome-wide association study of OD and CD related phenotypes reflecting the time between first self-reported use of these substances and a first DSM-IV dependence diagnosis was conducted.
Published: February 28, 2021 Explor Med. 2021;2:60–73
4839 58 1
Open Access Original Article
A machine learning approach to identify correlates of current e-cigarette use in Canada
Aim: Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm.
Published: February 28, 2021 Explor Med. 2021;2:74–85
3583 45 0
Open Access Original Article
Placental OPRM1 DNA methylation and associations with neonatal opioid withdrawal syndrome, a pilot study
Aim: Epigenetic variation of DNA methylation of the mu-opioid receptor gene (OPRM1) has been identified in the blood and saliva of individuals with opioid use disorder (OUD) and infants with neonatal opioid withdrawal syndrome (NOWS). It is unknown whether epigenetic variation in OPRM1 exists within placental tissue in women with OUD and whether it is associated with NOWS outcomes.
Published: June 29, 2020 Explor Med. 2020;1:124–135
4539 50 9
Open Access Original Article
Identifying factors associated with opioid cessation in a biracial sample using machine learning
Aim: Racial disparities in opioid use disorder (OUD) management exist, however, and there is limited research on factors that influence opioid cessation in different population groups. Methods: We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD.
Published: February 29, 2020 Explor Med. 2020;1:27–41
5431 93 5