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Both metabolic syndrome (MetS) and subclinical hypothyroidism (SCH) are prevalent in major depressive disorder (MDD) patients. However, their relationship in this population remains unknown. The study assessed the association between SCH and MetS in 1706 first-episode drug-naïve (FEDN) MDD patients. We also compared the relationship between MetS and clinical symptoms in patients with and without comorbid SCH. The Positive and Negative Syndrome Scale positive subscale, the Hamilton Anxiety Rating Scale, and the Hamilton Depression Rating Scale were used to detect clinical symptoms. Serum levels of free triiodothyronine, free thyroxine, thyroid stimulating hormone (TSH), anti-thyroglobulin, thyroid peroxidases antibody, cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and fasting glucose were measured. The Area Under the Curve (AUC) was used to test the performance of serum TSH in identifying MetS patients. The prevalence of MetS and SCH was 34.5% (n = 585) and 61% (n = 1034), respectively. The presence of SCH increased the risk of MetS, hyperglycemia, hypertension, obesity, and low HDL-C by 4.91, 3.51, 3.54, 2.02, and 2.34 times, respectively. Serum TSH had a nice ability to distinguish MetS patients from non-MetS patients (AUC value = 0.77). MetS and its components exhibited a positive association with clinical profiles only in SCH patients, but not in non-SCH patients. Taken together, our study suggested SCH was closely related to MetS and might play a vital role in the relationship between MetS and clinical symptoms. Regular thyroid function checks might help early detect MetS. [ABSTRACT FROM AUTHOR]

Several studies have identified the effects of methamphetamine (MA) on central dopaminergic neurons, but its effects on enteric dopaminergic neurons (EDNs) are unclear. The aim of this study was to investigate the effects of MA on EDNs and intestinal motility. Male Sprague–Dawley rats were randomly divided into MA group and saline group. The MA group received the multiple high-dose MA treatment paradigm, while the controls received the same saline treatment. After enteric motility was assessed, different intestinal segments (i.e., duodenum, jejunum, ileum, and colon) were taken for histopathological, molecular biological, and immunological analysis. The EDNs were assessed by measuring the expression of two dopaminergic neuronal markers, dopamine transporter (DAT) and tyrosine hydroxylase (TH), at the transcriptional and protein levels. We also used c-Fos protein, a marker of neural activity, to detect the activation of EDNs. MA resulted in a significant reduction in TH and DAT mRNA expression as well as in the number of EDNs in the duodenum and jejunum (p < 0.05). MA caused a dramatic increase in c-Fos expression of EDNs in the ileum (p < 0.001). The positional variability of MA effects on EDNs paralleled the positional variability of its effect on intestinal motility, as evidenced by the marked inhibitory effect of MA on small intestinal motility (p < 0.0001). This study found significant effects of MA on EDNs with locational variability, which might be relevant to locational variability in the potential effects of MA on intestinal functions, such as motility. [ABSTRACT FROM AUTHOR]

Purpose: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.Methods: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.Results: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.Conclusion: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma. [ABSTRACT FROM AUTHOR]