Alpha psychiatry最新文献

Background: Evolutionary psychiatry is a growing field that emphasizes the value of evolutionary explanations for traits that make individuals vulnerable to mental disorders. Some articles that apply evolutionary theory in psychiatry make errors, such as viewing a disease as an adaptation. We assessed article quality and error quantity in the most cited articles on evolutionary psychiatry and examined the relationship of these measures to citation number.

Methods: PubMed, Web of Science, and Google Scholar were searched in 2023 and again in 2025 using specific terms related to "evolution" and "psychiatry", to find the most highly cited articles in the field. Based on the work of Nesse, we developed a measure for assessing overall article quality and error quantity in evolutionary psychiatry articles. We applied the measure to the 20 most highly cited articles, and calculated the correlations of article quality and error quantity with number of citations.

Results: Twenty highly cited articles, with a mean citation count of 413.30 and publication year from 1964 to 2011, were rated. While the most highly cited articles had good quality on average, they also made important errors. There was no significant correlation of article quality or error quantity with citation count.

Conclusion: Highly cited articles in evolutionary psychiatry demonstrated strengths but also weaknesses. The lack of a relationship of article quality and error quantity to citation rates suggests that other factors influence such citations. Future research should focus on achieving consensus on how best to assess the quality of evolutionary psychiatry articles and what errors should be avoided.

Background: The objective of this study is to develop an easily applicable scale to measure the course of treatment and the level of recovery for mental problems in various dimensions, which can be used in clinical practice and research.

Methods: The validity and reliability test of Moodist Outcome Inventory (MOI) were conducted with 293 participants. Criterion-related validity was investigated by assessment with the Brief Psychiatric Rating Scale (BPRS), Disability Assessment Schedule (WHO-DAS-II), and Psychological Distress Scale (K10-PDS). Factor analysis was investigated by assessment with clinical and non-clinical samples. The sample was followed for six clinical assessments and evaluated by repetitive analysis of Variance (ANOVA) measurement.

Results: The Cronbach's alpha coefficient of the total scale was noted to be 0.89 in the reliability analysis. In the exploratory factor analysis, the single factor explaining 75.64% of the total variance was attained, and all items were included in this factor. Forty cases completed six clinical assessments, and the change between the MOI scores during the time interval was noted to be statistically significant. The correlation of the MOI scale with the K-10, WHO-DAS-II, and BPRS scales was noted to be 0.62, 0.73, and 0.65, respectively. In six consecutive assessments, the mean scores of all scales dropped significantly. The cut-off point of the scale was recorded as 7.27, and the reliable change index (RCI) was noted as 2.5.

Conclusion: MOI was assessed as a valid and reliable scale for evaluating the course of treatment. The strengths of the scale are that it assesses both symptoms and well-being, is short, and can be implemented in clinical practice.

Alcohol Use Disorder (AUD) is a global health challenge, affecting 10-15% of the population, with significant social, health, and economic consequences. Although pharmacotherapies such as disulfiram, naltrexone, and acamprosate are available, their effectiveness is limited and patient adherence is often poor. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation technique that targets neural circuits implicated in addiction. Emerging evidence suggests that rTMS may reduce alcohol craving and consumption, although results have been mixed. This review examines the neural mechanisms by which rTMS may influence AUD, summarizes current clinical evidence of its efficacy, and discusses future directions.

Background: Chronic hepatitis B (CHB) represents a significant global public health challenge. In China, the disease remains prevalent despite recent reductions in incidence. In addition to its impact on physical health, CHB adversely affects patients' mental health, particularly in the form of anxiety and depression. However, limited research has been conducted on the psychological status of CHB inpatients, especially in metropolitan settings. This study aimed to evaluate the prevalence of anxiety and depression among hospitalized CHB patients in Shenzhen, China, and to investigate factors associated with these mental health conditions.

Methods: A cross-sectional study was conducted involving 649 inpatients with chronic hepatitis B at Shenzhen Third People's Hospital. The Hospital Anxiety and Depression Scale (HADS) was used to assess levels of anxiety and depression. Logistic regression analysis was performed to identify factors associated with mental health outcomes.

Results: The study revealed that 34.05% of patients experienced anxiety, while 71.65% exhibited symptoms of depression. Depression was more prevalent among older patients and those with multiple hospitalizations. Factors such as lack of health insurance and prolonged hospitalizations were significantly associated with depression. Female patients showed a higher propensity for experiencing anxiety.

Conclusion: The high prevalence of anxiety and depression among CHB inpatients highlights the need for integrated mental health screening and intervention strategies within hospital settings. Tailored healthcare approaches are essential to address both the physical and psychological needs of CHB patients, particularly in rapidly urbanizing areas such as Shenzhen.

Background: This systematic review aimed to investigate the effectiveness of mindfulness-based interventions (MBIs) in treating depression, enhancing interoceptive awareness (IA), and whether IA mediates this relationship.

Methods: In August 2024, a comprehensive literature search was conducted in web-based medical and psychological databases, including PsycINFO, MEDLINE, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were included if they were quantitative, peer-reviewed, in English, used MBIs derived from Mindfulness-based Stress Reduction (MBSR), Mindfulness-based Cognitive Therapy (MBCT), or Mindfulness-integrated Cognitive Behavioural Therapy (MiCBT), included a control/comparison group, pre- and post-intervention measures, assessed depressive symptoms and IA in adults over 18, and had at least 20 participants. Exclusion criteria included non-English publications, dissertations, case studies, qualitative research, therapies not derived from the specified MBIs, and studies with under 20 participants or individuals under 18. Methodological quality and risk of bias were assessed.

Results: Six studies involving 646 participants met the inclusion criteria. All MBIs (MBCT, MBSR, MiCBT, Mindfulness-based Cancer Recovery, and Mindful Awareness in Body-Oriented Therapy) significantly reduced depressive symptomology and improved IA across varying effect sizes, with IA identified as a partial mediator.

Conclusions: MBIs appear to alleviate depressive symptoms and improve IA, with one study finding IA as a mediator. Limitations included limited literature, search term specificity, heterogeneity and mixed evidence quality. Future research should explore IA's mediating role, develop a standardised IA measure, and integrate IA into broader treatment modalities to enhance outcomes.

The prospero registration: CRD42023457300, https://www.crd.york.ac.uk/PROSPERO/view/CRD42023457300.

Adolescent depression is a serious public health issue affecting the mental health and quality of life of adolescents worldwide. Mental time travel (MTT), an individual's capacity to recall the past or look to the future, plays an important role in emotion regulation and mental health. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature search was conducted. Due to considerable heterogeneity among the included studies, a narrative synthesis approach was adopted. A total of 22 articles retrieved from PubMed, Web of Science, PsycINFO, and EBSCO (up to October 31, 2024) were included to elucidate the mechanisms underlying MTT impairments and related interventions in depressed adolescents. The main findings indicated that depressed adolescents exhibit overgeneralization of autobiographical memories, impoverished future simulations, and negative bias in MTT constructs. Neuroimaging studies have revealed aberrant activation within the autobiographical memory network, hyperengagement of the self-referential network during MTT tasks, and alterations in emotion regulation circuits. Furthermore, the efficacy of cognitive therapy and memory/imagery-specific training in ameliorating temporal cognitive biases and fostering positive future expectations was demonstrated. These findings underscore the importance of examining adolescent depression through the lens of MTT, offering a promising framework for understanding its cognitive and neural mechanisms and the development of novel intervention strategies.

Objective: This study compared addiction severity, psychotic symptoms, suicide risk, and craving in patients with heroin use disorder, with and without methamphetamine use. We also investigated the reasons for methamphetamine use in these patients, and assessed 3-month clinical follow-up and treatment compliance.

Methods: This cross-sectional study included 166 inpatients diagnosed with heroin use disorder (DSM-5). Patients were divided into two groups: heroin use only (H), and heroin use + methamphetamine use (H+M). Clinical assessments included the Addiction Profile Index-Clinical Form (API-C), Brief Psychiatric Rating Scale (BPRS), and Suicide Probability Scale (SPS). Statistical analyses were conducted with Statistical Package for the Social Sciences (SPSS) and included descriptive statistics, Kolmogorov-Smirnov test, Chi-square test, Mann-Whitney U test, and logistic regression. Three-month follow-up results and treatment compliance were compared between the two groups.

Results: The H and H+M groups included 80 and 86 participants, respectively. The H+M group had higher BPRS total scores, API-C subscale scores (craving, risky behaviors, excitement-seeking, impulsiveness, depression), addiction severity, additional substance use, anxiety, depressive symptoms, suicidal ideation, and 3-month lapse rate. Craving and excitement-seeking were independent predictors of methamphetamine use.

Conclusion: The H+M group showed more severe addiction, novelty-seeking personal characteristics, and suicidal ideation compared to the H group. Craving scores were higher in the H+M group and should not be overlooked, along with a greater risk of early lapse. Our study found that craving, risky behaviors, depressive and psychotic symptoms, and suicidal thoughts are the most critical issues to be addressed in the treatment and follow-up of the H+M patient group.

Objective: To explore the molecular mechanisms underlying clozapine-induced metabolic syndrome (MetS) in schizophrenia patients, providing scientific evidence for clinicians to prevent and manage metabolic syndrome during the treatment of psychiatric disorders.

Methods: Ten schizophrenia patients with MetS and ten matched controls were recruited from Shanghai Mental Health Center according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for schizophrenia and the 2016 Chinese Adult Dyslipidemia Prevention and Treatment Guidelines for MetS. Peripheral blood RNA sequencing was performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to pinpoint hub genes. Mendelian randomization (MR) was conducted to validate causal relationship between serum brain-derived neurotrophic factor (BDNF) levels and MetS components.

Results: A total of 1019 DEGs were identified, grouped into eight mRNA modules through WGCNA. Key hub genes included RP11-611O2.6, acid phosphatase-like 2 (ACPL2), T cell receptor alpha variable 12-2 (TRAV12-2), matrix metallopeptidase 8 (MMP8), piggyBac transposable element derived 4 pseudogene 1 (PGBD4P1), transmembrane protein 261 (TMEM261), and BDNF, with BDNF and MMP8 further validated by PPI network analysis. MR analysis confirmed a causal association between BDNF levels and MetS risk, reinforcing its role in metabolic dysregulation. Gene Ontology (GO) annotation and pathway enrichment analysis highlighted immune response, morphological changes, and metabolic processes as key biological processes, with pathways such as biological oxidation and defensins significantly enriched.

Conclusion: Significant differences in gene expression are observed between schizophrenia patients with and without MetS. Individual variability in clozapine-induced MetS may be linked to DEGs.

Background: The pathophysiological mechanisms underlying alcohol use disorder (AUD) remain unclear, and its clinical evaluation largely depends on subjective assessments lacking objective biomarkers. This study employed a case-control design incorporating resting-state electroencephalography (EEG) with power spectral analysis (PSA) and dynamic functional connectivity (dFC) to explore potential biomarkers for AUD.

Methods: Resting-state EEG data were collected from individuals diagnosed with AUD and demographically matched healthy controls (HCs), alongside comprehensive neuropsychological and behavioral evaluations. PSA quantified energy distribution across specific frequency bands, with receiver operating characteristic analysis determining its discriminatory capacity. dFC was examined using a sliding window approach and the weighted phase-lag index, followed by K-means clustering to extract dominant connectivity states across frequency bands.

Results: After excluding cases with suboptimal EEG data, the final analytic sample comprised 25 individuals with AUD and 26 HCs. Compared to HCs, the AUD group exhibited elevated low-beta power at F1, FCz, FC1, and C3 electrode sites (10-20 EEG system), with respective area under the curve values of 0.795, 0.794, 0.806, and 0.769, indicating reliable group differentiation. Temporal profiling of functional connectivity revealed three distinct brain states: S1 (60.81%), S2 (21.05%), and S3 (18.15%). Correlations between these connectivity patterns and clinical indices were observed in the AUD cohort.

Conclusion: Individuals with AUD showed increased brain activity in the medial frontal gyrus and left central gyrus at rest, as well as significant low-beta frequency changes in dFC analysis. Resting EEG scans with PSA and dFC analysis could serve as potential biomarkers for detecting AUD.

Background: Individuals suffer from depression at a high rate on university campuses and current assessment methods primarily rely on subjective questionnaires. Therefore, there is a pressing need to develop objective measures for the automatic detection of depression. This study aimed to investigate the functional near-infrared spectroscopy (fNIRS) changes associated with depression and assess the potential of fNIRS signals in detecting depression among university students.

Methods: A total of 192 participants were recruited for psychological assessment. A 48-channel fNIRS system was employed to measure cerebral blood oxygenation signals during the verbal fluency task (VFT). Two-sample t-tests were used to detect group differences. The association between fNIRS data and depression was identified using Pearson correlation analysis. We applied five machine learning classifiers to differentiate depression using fNIRS signals. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. A ten-fold cross-validation incorporating the recursive feature elimination algorithm was utilized.

Results: Significant hemodynamic alterations were observed in the depression group at channels 4, 16, 21, 26, 32, 43, 44, and 47, in comparison with the control group. The bilateral medial prefrontal cortices (MPFC), left dorsolateral prefrontal cortex, and left temporal lobe, represented by channels 4, 16, 43, and 44, were associated with depression. Among the five machine learning algorithms, K-Nearest-Neighbors (KNN) exhibited superior classification performance (AUC = 66.51%). The left MPFC was the most significant contributor to the classification efficacy of the KNN model.

Conclusion: fNIRS-VFT may serve as an objective tool for evaluating depressive symptoms in university students. The findings underscore the central role of the left MPFC in the neural mechanisms underlying depression. This work developed an fNIRS-based identification system for depression in university students.