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Background: Detoxification is frequently recommended as a treatment for moderate to severe Cocaine Use Disorder (CUD). However, the response to detoxification varies among patients, and previous studies have focused mostly on patterns of drug use behavior to test associations with treatment outcomes, overlooking the potential impact of psychosocial factors, other clinical variables, and individual life experiences. In this study we comprehensively examined several variables aiming to find the most relevant predictors to classify patients with severe versus non-severe cocaine withdrawal symptoms at the end of detoxification., Methods: Data from 284 women with CUD who enrolled in a 3-week detoxification program was used in this longitudinal study. Psychosocial, clinical, and drug use behavior characteristics were evaluated, generating a dataset with 256 potential predictors. We tested six different machine learning classification algorithms., Results: The best classification algorithm achieved an average accuracy and ROC-AUC of approximately 70%. The 16 features selected as best predictors were the severity of psychiatric, family, and social problems and the level of exposure to childhood maltreatment. Features associated with drug-use behavior included days consuming drugs and having craving symptoms in the last month before treatment, number of previous drug/alcohol-related treatments, and a composite score of addiction severity. The level of cocaine withdrawal syndrome at the beginning of detoxification was also a key feature for classification. A network analysis revealed the pattern of association between predictors., Conclusion: These variables can be assessed in real-world clinical settings, potentially helping clinicians to identify individuals with severe cocaine withdrawal that is likely to be sustained over the course of detoxification., Competing Interests: Declaration of competing interest The authors declare there is no conflict of interest. The study was approved by the institutional review boards of included institutions, and it was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: There is growing emphasis in the field of psychiatry on the need to identify candidate biomarkers to aid in diagnosis and clinical management of addictive disorders. MicroRNAs (miRNAs) are small nucleotide sequences with the ability to regulate gene expression at the transcriptomic level. However, the role of miRNAs as potential biomarkers for addiction is still underexplored. Based on translational and clinical findings, we compared the expression levels of microRNA-124 (miR-124), microRNA-181 (miR-181), and microRNA-212 (miR-212) between a group of females with cocaine use disorder (CUD; n = 30) and a group of healthy female controls (HC; n = 20)., Methods: Blood expression levels of miR-124, miR-181, and miR-212 in the HC and CUD group were determined by qPCR, using two miRNAs as endogenous controls (miR-24 and miR-126). Substance use behavior was assessed by self-report using the Addiction Severity Index (ASI-6) and depressive symptoms severity was measured using the Beck Depressive Inventory (BDI-II). Urine screen test was performed to detect cocaine metabolites., Results: Mir-124 and miR-181 were upregulated in the CUD group (p > 0.01). Furthermore, increased cognitive/affective depression symptoms were identified among a CUD subgroup with the higher miR-181 expression levels (p > 0.05). No significant difference in expression levels was found for miR-212., Conclusions: MiR-124 and miR-181 show promise as biomarkers for CUD when assessed in the peripheral blood. Further investigation is needed to elucidate the molecular mechanisms underlying these associations and to validate target genes regulated by these miRNAs., (Copyright © 2019 Elsevier Ltd. All rights reserved.)