This pilot study simultaneously evaluated the effects of various factors, including genetic variations of CYP2B6, CYP2C19, and ABCB1, demographic characteristics, disease states, methadone-drug interactions (MDIs), and poly-substance use, on the treatment responses among non-HIV patients in the methadone maintenance treatment program (MMTP) in Taiwan.
ABCB1 has been implicated in substance use, and in post hoc tests we found that variation in ABCB1 was associated with DSM-IV alcohol and cocaine dependence criterion counts.
Adopting an intermediate phenotype approach, we investigated whether reward-related electrophysiological activity of ACC-a cortical region said to utilize dopamine reward signals to learn the value of extended, context-specific sequences of goal-directed behaviors-mediates the influence of multiple dopamine-related functional polymorphisms over substance use.
The data clearly show an increased prevalence of OUD, justifying the need for evidence-based substance use programs such as MAT in jail settings in the South.
These findings suggest that the MoCA and ACE-R are both valid and time-efficient screening tools to detect cognitive impairment in the context of substance use.
ACE modeling revealed that genetic, as well as shared and non-shared environmental factors explained the overall level of substance use and that these same factors also partly accounted for growth in substance use from age 13 to 17.
Effects of the serotonin transporter (5-HTTLPR) and α2A-adrenoceptor (C-1291G) genotypes on substance use in children and adolescents: a longitudinal study.
The Longitudinal Effects of Non-injection Substance Use on Sustained HIV Viral Load Undetectability Among MSM and Heterosexual Men in Brazil and Thailand: The Role of ART Adherence and Depressive Symptoms (HPTN 063).
Having 2 years and above duration on ART [AOR = 7, 95% CI (2.2, 22.6)], history of adverse effect [AOR = 6.9, 95% CI (1.4, 32.9)], substance use [AOR = 5.3, 95% CI (1.4, 20.0)], living with parents [AOR = 3.4, 95% CI (1.2, 10.3)], having depression symptom [AOR = 3.3, 95% CI (1.4, 7.5)], <350 cells/mm<sup>3</sup> cluster of differentiation 4 count [AOR = 3.2, 95% CI (1.8, 5.8)] and low dietary diversity [AOR = 2, 95% CI (1.1, 3.7)] were found significant determinants of non-adherence to antiretroviral drug.
Moreover, patients harboring the TNF308.2 allele and/or those with habits of substance use had low serum albumin concentration and platelet count (each p = 0.0001).
Contrary to the gateway model, we found no evidence that ALDH2 deficiency was associated with lower rates of nonalcohol substance use or antisociality.
We investigated the influence of individual-difference variables implicated as risk factors for Alzheimer's disease (AD) or known to be related to cognitive performance in normal aging (e.g., age, sex, years of education, previous and recent diseases, apolipoprotein E status, social network, and substance use) on rate of cognitive change from preclinical to clinical AD.
College students were administered self-report questionnaires (Zuckerman Sensation Seeking Scale [ZSS], Barratt Impulsiveness Scale [BIS-11], behavioral measures related to risk-taking and impulsivity (Balloon Analog Risk Task [BART], Experiential Discounting Task [EDT]), and the substance use module of a clinical interview (past-six-month alcohol and marijuana use).
The Longitudinal Effects of Non-injection Substance Use on Sustained HIV Viral Load Undetectability Among MSM and Heterosexual Men in Brazil and Thailand: The Role of ART Adherence and Depressive Symptoms (HPTN 063).
Having 2 years and above duration on ART [AOR = 7, 95% CI (2.2, 22.6)], history of adverse effect [AOR = 6.9, 95% CI (1.4, 32.9)], substance use [AOR = 5.3, 95% CI (1.4, 20.0)], living with parents [AOR = 3.4, 95% CI (1.2, 10.3)], having depression symptom [AOR = 3.3, 95% CI (1.4, 7.5)], <350 cells/mm<sup>3</sup> cluster of differentiation 4 count [AOR = 3.2, 95% CI (1.8, 5.8)] and low dietary diversity [AOR = 2, 95% CI (1.1, 3.7)] were found significant determinants of non-adherence to antiretroviral drug.
Screening uses validated measures (Tobacco, Alcohol, Prescription medication, and other Substance use [TAPS]; and the Adult Outcomes Questionnaire [AOQ], which includes the Patient Health Questionnaire [PHQ-9] and Generalized Anxiety Disorder [GAD-2]) delivered via three modalities (secure messaging, tablets in waiting rooms, and desktop computers in exam rooms).
Multiple subscales from both UPPS-P and BIS showed strong genetic correlations (>0.5) with Drug Experimentation and other substance use traits measured in independent cohorts, including smoking initiation, and lifetime cannabis use.
A total of 1806 adolescents (females = 48%, mean age = 17.14 years) completed BIS-15 questionnaire, strengths and difficulties questionnaire (SDQ) and a risk taking questionnaire that assessed adolescents level of involvement in two protypical risky behaviours- substance use and unsafe sexual behaviours.
Specific significant associations included links between CAS and alcohol use (b = 0.40), childhood sexual abuse and unstable housing (b = - 0.75), alcohol use and childhood sexual abuse (b = 0.40), and substance use and intimate partner violence (b = 0.43).
We used meta-analysis of an efficient empirical-Bayes estimator to detect potential multiplicative interactions between each of the SNPs [rs16892766 at 8q23.3 (EIF3H/UTP23), rs6983267 at 8q24 (MYC), rs10795668 at 10p14 (FLJ3802842), rs3802842 at 11q23 (LOC120376), rs4444235 at 14q22.2 (BMP4), rs4779584 at 15q13 (GREM1), rs9929218 at 16q22.1 (CDH1), rs4939827 at 18q21 (SMAD7), rs10411210 at 19q13.1 (RHPN2), and rs961253 at 20p12.3 (BMP2)] and select major CRC risk factors (sex, body mass index, height, smoking status, aspirin/nonsteroidal anti-inflammatory drug use, alcohol use, and dietary intake of calcium, folate, red meat, processed meat, vegetables, fruit, and fiber).
We used meta-analysis of an efficient empirical-Bayes estimator to detect potential multiplicative interactions between each of the SNPs [rs16892766 at 8q23.3 (EIF3H/UTP23), rs6983267 at 8q24 (MYC), rs10795668 at 10p14 (FLJ3802842), rs3802842 at 11q23 (LOC120376), rs4444235 at 14q22.2 (BMP4), rs4779584 at 15q13 (GREM1), rs9929218 at 16q22.1 (CDH1), rs4939827 at 18q21 (SMAD7), rs10411210 at 19q13.1 (RHPN2), and rs961253 at 20p12.3 (BMP2)] and select major CRC risk factors (sex, body mass index, height, smoking status, aspirin/nonsteroidal anti-inflammatory drug use, alcohol use, and dietary intake of calcium, folate, red meat, processed meat, vegetables, fruit, and fiber).