Background: Mental health disorders can affect physical and psychological behaviors. The people of the Greater Mekong Subregion (GMS) have a high risk of mental health disorders, such as depression, stress, and substance abuse be-cause the people in this region are trafficked for forced sex work and various forms of forced labor. In these situations, vic-tims often endure violence and abuse from trafficking recruiters, employers, and other individuals. The purposes of this study were to identify the elements characterizing mental health disorders, especially in terms of depression, stress, and sub-stance abuse, and to identify the treatment modalities for mental health disorders in the GMS.
Methods: The researcher undertook a comparative analysis of the literature, reviews of epidemiological studies and mental disorder therapies, and overviews of previous research studies, were used to generate a synthesis of the existing knowledge of the mental disorder therapeutic modalities. Regarding the search methods, the data from the electronic databases PubMed, PsycINFO, Dynamed and ScienceDirect were supplemented with a manual reference search covering relevant studies from 2005 to 2016.
Results: Thirty-one papers were included in the review of elements characterizing mental health disorders, especially in terms of depression, stress, and substance abuse, and to identify the treatment modalities for mental health disorders in the GMS. Nine papers defined characterizing mental health disorders, in terms of depression, stress, and substance abuse. Twenty-two papers showed the treatment modalities for mental health disorders that the treatment was effective, these in-cluded pharmacological treatments and psychological treatments, such as mindfulness-based cognitive therapy, biofeedback, and music therapy. Useful guidance can be provided for the prevention and treatment of mental health disorders, and for the care of people in the Greater Mekong Subregion.
Conclusion: The finding of this review confirms the therapeutic modalities can provide useful guidance for the prevention and treatment of mental health disorders and the care of the people in the Greater Mekong Sub-region. In addition, the effective interventions should be tested regarding their suitability for the socio-cultural context in the Greater Mekong Subregion.
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http://dx.doi.org/10.2174/1573400513666170721102543 | DOI Listing |
Neuromodulation
January 2025
Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Objectives: Biphasic sinusoidal repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation treatment that has been approved by the US Food and Drug Administration for treatment-resistant depression (TRD). Recent advances suggest that standard rTMS may be improved by altering the pulse shape; however, there is a paucity of research investigating pulse shape, owing primarily to the technologic limitations of currently available devices. This pilot study examined the feasibility, tolerability, and preliminary efficacy of biphasic and monophasic rectangular rTMS for TRD.
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View Article and Find Full Text PDFViruses
November 2024
Department of Toxicology, Drug Industry, Management and Legislation, Faculty of Pharmacy, "Victor Babeş" University of Medicine and Pharmacy, 2nd Eftimie Murgu Sq., 300041 Timişoara, Romania.
The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.
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