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Al-Ziadat MA. Do social support and self- efficacy play a significant role in substance use relapse? Health Psychol Res 2024; 12:94576. [PMID: 38533344 PMCID: PMC10963257 DOI: 10.52965/001c.94576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/05/2024] [Indexed: 03/28/2024] Open
Abstract
This study aims to investigate if social support and self-efficacy play a significant role in substance use relapse. To this end, 197 substance users responded to the modified measures of social support and self-efficacy questionnaire. The participants reported moderate levels of social support and self-efficacy. In addition, the results indicated that there are gender differences in social support level in favour of males and there were differences in social support level in the duration of substance use between less than one year and one year- less than two years also between one year- less than two years and ten years and more in favour of one year- less than two years. Furthermore, the results revealed differences in self-efficacy levels in accordance with substance use status in favour of those without relapse. But there was no difference in self-efficacy level with regard to gender or duration of substance use. Moreover, the findings indicate that self-efficacy and duration of substance use play a significant role in substance use relapse but this is not the case with social support. It was concluded that giving more attention to female social support and to the self-efficacy among substance users are needed.
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Jang WJ, Lee S, Jeong CH. Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review. Front Psychiatry 2024; 14:1302994. [PMID: 38260797 PMCID: PMC10800441 DOI: 10.3389/fpsyt.2023.1302994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction. Methods We performed a literature search for this review using the Boolean search in the PubMed database. Results This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues. Discussion Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.
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Affiliation(s)
| | | | - Chul-Ho Jeong
- College of Pharmacy, Keimyung University, Daegu, Republic of Korea
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Li X, Cong J, Liu K, Wang P, Sun M, Wei B. Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19565-19583. [PMID: 38052615 DOI: 10.3934/mbe.2023867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.
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Affiliation(s)
- Xiang Li
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Jinyu Cong
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Kunmeng Liu
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Pingping Wang
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Min Sun
- Shandong Detoxification Monitoring and Treatment Institute, Zibo 255311, China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
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Chen YH, Yang J, Wu H, Beier KT, Sawan M. Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction. Front Psychiatry 2023; 14:1085036. [PMID: 36911117 PMCID: PMC9995819 DOI: 10.3389/fpsyt.2023.1085036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.
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Affiliation(s)
- Yun-Hsuan Chen
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jie Yang
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kevin T Beier
- Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, United States.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
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