Causal role of immune cells in major depressive disorder and bipolar disorder: Mendelian randomization (MR) study.

BACKGROUND: Major depressive disorder (MDD) and bipolar disorder (BD) are prevalent psychiatric conditions linked to inflammatory processes. However, it is unclear whether associations of immune cells with these disorders are likely to be causal. METHODS: We used two-sample Mendelian randomization (MR) approach to investigate the relationship between 731 immune cells and the risk of MDD and BD. Rigorous sensitivity analyses are conducted to assess the reliability, heterogeneity, and horizontal pleiotropy of the findings. RESULTS: Genetically-predicted CD27 on IgD+ CD38- unswitched memory B cell (inverse variance weighting (IVW): odds ratio (OR) [95 %]: 1.017 [1.007 to 1.027], p = 0.001), CD27 on IgD+ CD24+ B cell (IVW: OR [95 %]: 1.021 [1.011 to 1.031], p = 4.821E-05) and other 12 immune cells were associated with increased risk of MDD in MR, while HLA DR++ monocyte %leukocyte (IVW: OR [95 %]: 0.973 [0.948 to 0.998], p = 0.038), CD4 on Central Memory CD4+ T cell (IVW: OR [95 %]: 0.979 [0.963 to 0.995], p = 0.011) and other 13 immune cells were associated with decreased risk of MDD in MR. Additionally, CD33+ HLA DR+ Absolute Count (IVW: OR [95 %]: 1.022[1.007 to 1.036], p = 0.007), CD28+ CD45RA- CD8+ T cell %T cell (IVW: OR [95 %]: 1.024 [1.008 to 1.041], p = 0.004) and other 18 immune cells were associated with increased risk of BD in MR, while CD62L on CD62L+ myeloid Dendritic Cell (IVW: OR [95 %]: 0.926 [0.871 to 0.985], p = 0.014), IgD- CD27- B cell %lymphocyte (IVW: OR [95 %]: 0.918 [0.880 to 0.956], p = 4.654E-05) and other 13 immune cells were associated with decreased risk of BD in MR. CONCLUSIONS: This MR study provides robust evidence supporting a causal relationship between immune cells and the susceptibility to MDD and BD, offering valuable insights for future clinical investigations. Experimental studies are also required to further examine causality, mechanisms, and treatment potential for these immune cells for MDD and BD.

Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex.

Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical assessment, are subjective and rely on healthcare professionals' expertise. Recently, there's growing interest in using Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to objectively understand MDD's neurobiology, complementing traditional diagnostics. The posterior cingulate cortex (PCC) is a pivotal brain region implicated in MDD which could be used to identify MDD from healthy controls. Thus, this study presents an intelligent approach based on rs-fMRI data to enhance the classification of MDD. Original rs-fMRI data were collected from a cohort of 430 participants, comprising 197 patients and 233 healthy controls. Subsequently, the data underwent preprocessing using DPARSF, and the amplitudes of low-frequency fluctuation values were computed to reduce data dimensionality and feature count. Then data associated with the PCC were extracted. After eliminating redundant features, various types of Support Vector Machines (SVMs) were employed as classifiers for intelligent categorization. Ultimately, we compared the performance of each algorithm, along with its respective optimal classifier, based on classification accuracy, true positive rate, and the area under the receiver operating characteristic curve (AUC-ROC). Upon analyzing the comparison results, we determined that the Random Forest (RF) algorithm, in conjunction with a sophisticated Gaussian SVM classifier, demonstrated the highest performance. Remarkably, this combination achieved a classification accuracy of 81.9 % and a true positive rate of 92.9 %. In conclusion, our study improves the classification of MDD by supplementing traditional methods with rs-fMRI and machine learning techniques, offering deeper neurobiological insights and aiding accuracy, while emphasizing its role as an adjunct to clinical assessment.

Association between Shift Work and Health Outcomes in the General Population in China: A Cross-Sectional Study.

Shift work may adversely affect individuals' health, thus, the current study aimed to investigate the association between shift work and health outcomes in the general population. A total of 41,061 participants were included in this online cross-sectional survey, among which 9612 (23.4%) individuals engaged in shift work and 31,449 (76.6%) individuals engaged in non-shift work. Multiple logistic regression analyses were conducted to explore the association between shift work and health outcomes (psychiatric disorders, mental health symptoms, and physical disorders). In addition, associations between the duration (≤1 year, 1-3 years, 3-5 years, 5-10 years, ≥10 years) and frequency of shift work (<1 or ≥1 night/week) and health outcomes were also explored. The results showed that compared to non-shift workers, shift workers had a higher likelihood of any psychiatric disorders (odds ratios [OR] = 1.80, 95% CI = 1.56-2.09, p < 0.001), mental health symptoms (OR = 1.76, 95% CI = 1.68-1.85, p < 0.001), and physical disorders (OR = 1.48, 95% CI = 1.39-1.57, p < 0.001). In addition, inverted U-shaped associations were observed between the duration of shift work and health outcomes. These results indicated that shift work was closely related to potential links with poor health outcomes. The findings highlighted the importance of paying attention to the health conditions of shift workers and the necessity of implementing comprehensive protective measures for shift workers to reduce the impact of shift work.

Role and mechanism of PVN-sympathetic-adipose circuit in depression and insulin resistance induced by chronic stress.

Chronic stress induces depression and insulin resistance, between which there is a bidirectional relationship. However, the mechanisms underlying this comorbidity remain unclear. White adipose tissue (WAT), innervated by sympathetic nerves, serves as a central node in the interorgan crosstalk through adipokines. Abnormal secretion of adipokines is involved in mood disorders and metabolic morbidities. We describe here a brain-sympathetic nerve-adipose circuit originating in the hypothalamic paraventricular nucleus (PVN) with a role in depression and insulin resistance induced by chronic stress. PVN neurons are labelled after inoculation of pseudorabies virus (PRV) into WAT and are activated under restraint stress. Chemogenetic manipulations suggest a role for the PVN in depression and insulin resistance. Chronic stress increases the sympathetic innervation of WAT and downregulates several antidepressant and insulin-sensitizing adipokines, including leptin, adiponectin, Angptl4 and Sfrp5. Chronic activation of the PVN has similar effects. ß-adrenergic receptors translate sympathetic tone into an adipose response, inducing downregulation of those adipokines and depressive-like behaviours and insulin resistance. We finally show that AP-1 has a role in the regulation of adipokine expression under chronic stress.

Artificial intelligence in psychiatry research, diagnosis, and therapy.

Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.

Frontoparietal network homogeneity as a biomarker for mania and remitted bipolar disorder and a predictor of early treatment response in bipolar mania patient.

OBJECTIVE: Previous studies have revealed the frontoparietal network (FPN) plays a key role in the imaging pathophysiology of bipolar disorder (BD). However, network homogeneity (NH) in the FPN among bipolar mania (BipM), remitted bipolar disorder (rBD), and healthy controls (HCs) remains unknown. The present study aimed to explore whether NH within the FPN can be used as an imaging biomarker to differentiate BipM from rBD and to predict treatment efficacy for patients with BipM. METHODS: Sixty-six patients with BD (38 BipM and 28 rBD) and 60 HCs participated in resting-state functional magnetic resonance imaging and neuropsychological tests. Independent component analysis and NH analysis were applied to analyze the imaging data. RESULTS: Relative to HCs, BipM patients displayed increased NH in the left middle frontal gyrus (MFG), and rBD patients displayed increased NH in the right inferior parietal lobule (IPL). Compared to rBD patients, BipM patients displayed reduced NH in the right IPL. Furthermore, support vector machine results exhibited that NH values in the right IPL could distinguish BipM patients from rBD patients with 69.70 %, 57.89 %, and 91.67 % for accuracy, sensitivity, and specificity, respectively, and support vector regression results exhibited a significant association between predicted and actual symptomatic improvement based on the reduction ratio of the Young` Mania Rating Scale total scores (r = 0.466, p < 0.01). CONCLUSION: The study demonstrated distinct NH values in the FPN could serve as a valuable neuroimaging biomarker capable of differentiating patients with BipM and rBD, and NH values of the left MFG as a potential predictor of early treatment response in patients with BipM.

Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis.

Objective: Mild cognitive impairment (MCI) is a heterogeneous syndrome characterized by cognitive impairment on neurocognitive tests but accompanied by relatively intact daily activities. Due to high variation and no objective methods for diagnosing and treating MCI, guidance on neuroimaging is needed. The study has explored the neuroimaging biomarkers using the support vector machine (SVM) method to predict MCI. Methods: In total, 53 patients with MCI and 68 healthy controls were involved in scanning resting-state functional magnetic resonance imaging (rs-fMRI). Neurocognitive testing and Structured Clinical Interview, such as Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) test, Activity of Daily Living (ADL) Scale, Hachinski Ischemic Score (HIS), Clinical Dementia Rating (CDR), Montreal Cognitive Assessment (MoCA), and Hamilton Rating Scale for Depression (HRSD), were utilized to assess participants' cognitive state. Neuroimaging data were analyzed with the regional homogeneity (ReHo) and SVM methods. Results: Compared with healthy comparisons (HCs), ReHo of patients with MCI was decreased in the right caudate. In addition, the SVM classification achieved an overall accuracy of 68.6%, sensitivity of 62.26%, and specificity of 58.82%. Conclusion: The results suggest that abnormal neural activity in the right cerebrum may play a vital role in the pathophysiological process of MCI. Moreover, the ReHo in the right caudate may serve as a neuroimaging biomarker for MCI, which can provide objective guidance on diagnosing and managing MCI in the future.

Cryptotanshinone ameliorates CUS-induced depressive-like behaviors in mice.

OBJECTIVES: Cryptotanshinone (CPT), a natural quinoid diterpene, isolated from Salvia miltiorrhiza, has shown various pharmacological properties. However, its effect on chronic unpredictable stress (CUS)-induced depression phenotypes and the underlying mechanism remain unclear. Therefore, the aim of this study was to investigate whether CPT could exert an antidepressant effect. METHODS: We investigated the effects of CPT in a CUS-induced depression model and explored whether these effects were related to the anti-inflammatory and neurogenesis promoting properties by investigating the expression levels of various signaling molecules at the mRNA and protein levels. RESULTS: Administration of CPT improved depression-like behaviors in CUS-induced mice. CPT administration increased the levels of doublecortin-positive cells and reversed the decrease in the expression levels of brain-derived neurotrophic factor (BDNF)/tyrosine kinase receptor B (TrkB) signaling transduction, as well as the downstream functional proteins, phosphorylated extracellular regulated protein kinases (p-ERK), and cyclic adenosine monophosphate (cAMP)-response element-binding protein levels (p-CREB) in hippocampus. CPT treatment also inhibited the activation of microglia and suppressed M1 microglial polarization, while promoting M2 microglial polarization by monitoring the expression levels of arginase 1 (Arg-1) and inducible nitric oxide synthase (iNOS), and further inhibited the expression of proinflammatory cytokines, including interleukin (IL)-1, IL-6, and tumor necrosis factor-α (TNF-α), and increased the expression of the anti-inflammatory cytokine IL-10 by regulating nuclear factor-κB (NF-κB) activation. CONCLUSIONS: CPT relieves the depressive-like state in CUS-induced mice by enhancing neurogenesis and inhibiting inflammation through the BDNF/TrkB and NF-κB pathways and could therefore serve as a promising candidate for the treatment of depression.

The Efficacy of Fecal Microbiota Transplantation in Experimental Autoimmune Encephalomyelitis: Transcriptome and Gut Microbiota Profiling.

OBJECTIVE: To study the protective effect of fecal microbiota transplantation (FMT) on experimental autoimmune encephalomyelitis (EAE) and reveal its potential intestinal microflora-dependent mechanism through analyses of the intestinal microbiota and spinal cord transcriptome in mice. METHOD: We measured the severity of disease by clinical EAE scores and H&E staining. Gut microbiota alteration in the gut and differentially expressed genes (DEGs) in the spinal cord were analyzed through 16S rRNA and transcriptome sequencing. Finally, we analyzed associations between the relative abundance of intestinal microbiota constituents and DEGs. RESULTS: We observed that clinical EAE scores were lower in the EAE+FMT group than in the EAE group. Meanwhile, mice in the EAE+FMT group also had a lower number of infiltrating cells. The results of 16S rRNA sequence analysis showed that FMT increased the relative abundance of Firmicutes and Proteobacteria and reduced the abundance of Bacteroides and Actinobacteria. Meanwhile, FMT could modulate gut microbiota balance, especially via increasing the relative abundance of g_Adlercreutzia, g_Sutterella, g_Prevotella_9, and g_Tyzzerella_3 and decreasing the relative abundance of g_Turicibacter. Next, we analyzed the transcriptome of mouse spinal cord tissue and found that 1476 genes were differentially expressed between the EAE and FMT groups. The analysis of these genes showed that FMT mainly participated in the inflammatory response. Correlation analysis between gut microbes and transcriptome revealed that the relative abundance of Adlercreutzia was correlated with the expression of inflammation-related genes negatively, including Casp6, IL1RL2 (IL-36R), IL-17RA, TNF, CCL3, CCR5, and CCL8, and correlated with the expression of neuroprotection-related genes positively, including Snap25, Edil3, Nrn1, Cpeb3, and Gpr37. CONCLUSION: Altogether, FMT may selectively regulate gene expression to improve inflammation and maintain the stability of the intestinal environment in a gut microbiota-dependent manner.

Psychological Influence of Coronovirus Disease 2019 (COVID-19) Pandemic on the General Public, Medical Workers, and Patients With Mental Disorders and its Countermeasures.

BACKGROUND: Coronovirus disease 2019 (COVID-19) first broke out in Wuhan, Hubei Province, China, in 2019, and now it spreads in more than 100 countries around the world. On January 30th, the World Health Organization (WHO) declared COVID-19 a public health emergency of international concern. It was classified as a pandemic by the WHO on March 11, 2020. With the increase in the number of cases reported by various countries every day, the COVID-19 pandemic has attracted more and more attention around the world. At the same time, this public health emergency has caused a variety of psychological problems, such as panic disorder, anxiety, and depression. In addition, the Wuhan Mental Health Center's analysis of 2144 calls from the psychological hotline from February 4 to February 20, 2020, showed that the general public accounted for 70%, medical workers accounted for 2.2%, patients with mental disorders accounted for 19.5%, and other personnel accounted for 8.3% (https://mp.weixin.qq.com/s/kmff1vnaLsT2d9xQkK5pwg). CONCLUSION: Therefore, while controlling the pandemic, the government should also pay attention to the mental health of the general public, medical workers, and patients with mental disorders. Community mental health service systems, online mental health services, telemedicine, and other measures for patients with mental disorders may play a vital role during the pandemic.