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Abstract Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

ABSTRACTIt is very important to construct digital twin scenes, which can accurately describe the dynamically changing geographical environment and improve the level of refined management in bridge construction. This article proposes a feature constrained automatic diagnostic analysis method for geometric deformation of bridge digital twins. The geometric deformation feature library of bridge twins was first created to accurately describe structural relationships and behavior characteristics. Secondly, line surface feature constraints were used to extract geometric deformation information from bridge digital twins. Then, a geometric deformation diagnosis algorithm was designed based on an improved Hausdorff method. Finally, a case study was conducted to implement experimental analysis. The experimental results show that the method proposed in this paper can automatically extract the geometric morphology and rapidly calculate line and surface deformations for point cloud bridge digital twins. It achieves an efficiency improvement above 90% and with millimeter-level accuracy, which effectively enhances the diagnostic analysis capabilities for geographical digital twin models.

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare.

We employed a Bayesian network meta-analysis for comparison of the efficacy and tolerability of US Food and Drug Administration (FDA)-approved atypical antipsychotics (AAPs) for the treatment of bipolar patients with depressive episodes. Sixteen randomized controlled trials with 7234 patients treated by one of the five AAPs (cariprazine, lumateperone, lurasidone, olanzapine, and quetiapine) were included. For the response rate (defined as an improvement of ≥50% from baseline on the Montgomery-Åsberg Depression Rating Scale [MADRS]), all AAPs were more efficacious than placebo. For the remission rate (defined as the endpoint of MADRS ≤12 or ≤ 10), cariprazine, lurasidone, olanzapine, and quetiapine had higher remission rates than placebo. In terms of tolerability, olanzapine was unexpectedly associated with lower odds of all-cause discontinuation in comparison with placebo, whereas quetiapine was associated with higher odds of discontinuation due to adverse events than placebo. Compared with placebo, lumateperone, olanzapine, and quetiapine showed higher odds of somnolence. Lumateperone had a lower rate of ≥ weight gain of 7% than placebo and other treatments. Olanzapine was associated with a significant increase from baseline in total cholesterol and triglycerides than placebo. These findings inform individualized prescriptions of AAPs for treating bipolar depression in clinical practice.

Individuals with bipolar disorder (BD) have a greater suicide risk than the general population. In this study, we employed latent profile analysis (LPA) to explore whether Chinese individuals with different phases of BD differed at the levels of suicidal ideation. We recruited 517 patients. Depressive symptoms were measured using the 24-item Hamilton Depression Rating Scale (HAMD-24), and manic symptoms were evaluated using the Young Mania Rating Scale (YMRS). The extent of suicidal thoughts was determined through the Beck Scale for Suicide Ideation (BSSI). The scores of HAMD and YMRS were used to perform LPA. LPA categorized participants into three classes: one exhibiting severe depressive and mild manic symptomatology, another showing severe depressive and severe manic symptomatology, and the third one displaying severe depressive and intermediate manic symptomatology. Suicidal ideation levels were found to be remarkably elevated across all three classes. Additionally, the three classes showed no significant differences in terms of suicidal ideation. Our research confirms the link between depressive symptoms and suicide, independent of the manic symptoms. These findings carry meaning as they provide insight into the suicide risk profiles within different phases of BD.

Abstract Autoimmune diseases (AID) cause inflammatory changes in the peripheral blood, which might be a predisposing factor for the development of comorbid bipolar disorder (BD). The levels of peripheral inflammatory indicators and cytokines may also serve as potential biomarkers for predicting BD susceptibility and the efficacy of antipsychotics in patients with AID. Herein, we present the case of a 43-year-old female who has suffered from AID for over 16 years and was recently diagnosed with “bipolar and related disorder due to another medical condition”.

It is essential to establish a digital twin scene, which helps to depict the dynamically changing geographical environment accurately. Digital twins could improve the refined management level of intelligent tunnel construction; however, research on geographical twin models primarily focuses on modeling and visual description, which has low analysis efficiency. This paper proposes a knowledge-guided intelligent analysis method for the geometric deformation of tunnel excavation profile twins. Firstly, a dynamic data-driven knowledge graph of tunnel excavation twin scenes was constructed to describe tunnel excavation profile twin scenes accurately. Secondly, an intelligent diagnosis algorithm for geometric deformation of tunnel excavation contour twins was designed by knowledge guidance. Thirdly, multiple visual variables were jointly used to support scene fusion visualization of tunnel excavation profile twin scenes. Finally, a case was selected to implement the experimental analysis. The experimental results demonstrate that the method in this article can achieve an accurate description of objects and their relationships in tunnel excavation twin scenes, which supports rapid geometric deformation analysis of the tunnel excavation profile twin. The speed of geometric deformation diagnosis is increased by more than 90% and the cognitive efficiency is improved by 70%. The complexity and difficulty of the deformation analysis operation are reduced, and the diagnostic analysis ability and standardization of the geographic digital twin model are effectively improved.

Abstract Objective Individuals with mental illnesses are exposed to an increased risk of suicide. In this study, we aimed to investigate the clinical characteristics and outcome of psychiatric patients who attempted suicide by drug overdose and required emergency care. Methods A retrospective study was carried out in the Department of Emergency, the First Affiliated Hospital, Zhejiang University School of Medicine. Electronic medical records of psychiatric patients who were hospitalized due to suicide attempts from March 2019 to February 2022, with a discharge diagnosis of drug overdose were reviewed. Suicide‐related data of patients were collected, including suicide month, time from suicide to admission, type of drugs, the number of tablets taken, as well as demographic and clinical profiles (e.g., gender, age, marital status, profession, physical comorbidities, and diagnosis of mental illness). Results In the results, half of the patients were young people, female patients accounted for a higher proportion (72.5%), and the incidence of suicide was higher in winter than other seasons. Among the 109 psychiatric patients, 60 patients (55.0%) had a history of major depressive disorder, and 86 patients (78.9%) committed suicide with various psychotropic drugs, among which anxiolytics were the most commonly used drugs. Thirty‐seven patients (33.9%) experienced severe physical complications caused by drug overdose, with lung infections being the most common. The clinical outcome of most patients was favorable following emergent treatment, while 2 patients (1.8%) older than 80 failed to survive. Conclusion A better understanding of psychiatric patients referred to emergency care due to suicide by drug overdose helps to improve the clinical management and prognosis of patients.

BackgroundOrexins are polypeptides regulating appetite, sleep-wake cycle, and cognition functions, which are commonly disrupted in patients with schizophrenia. Patients with schizophrenia show a decreased connectivity between the prefrontal cortex and midline-anterior thalamus, and orexin can directly activate the axon terminal of cells within the prefrontal cortex and selectively depolarize neurons in the midline intralaminar nuclei of the thalamus. To address the relationship between orexin and schizophrenia, this study performed a meta-analysis on the alteration of plasma orexin-A levels in patients with schizophrenia.MethodWe searched eligible studies in PubMed, Embase, Cochrane, and China National Knowledge Infrastructure (CNKI) from 1998 to September 3, 2021. A total of 8 case-control studies were included in the meta-analyses, providing data on 597 patients with schizophrenia and 370 healthy controls. The Stata version 16.0 software was used to calculate the Hedges's adjusted g with 95% confidence intervals (CI).ResultsThe plasma orexin-A levels were not altered in subjects with schizophrenia (n = 597) when compared to healthy controls (n = 370). Subgroup analyses of gender (male and female vs. only male), country (China vs. other countries), medication (medication vs. non-medication), and the measurement of plasma orexin-A (Enzyme-linked immunosorbent assay vs. radioimmunoassay) revealed heterogeneity ranging from 30.15 to 98.15%, but none showed a significant alteration of plasma orexin-A levels in patients with schizophrenia. Heterogeneity was lower in the other countries and radioimmunoassay subgroup, while other subgroups remained to be highly heterogeneous. No significant evidence of publication bias was found either in Begg's test or the Egger's test.ConclusionThe present meta-analysis indicated that patients with schizophrenia did not show abnormal plasma levels of orexin-A.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021283455, identifier: CRD42021283455.

Background: Non-suicidal self-injury (NSSI) refers to the intentional and repeated physical trauma of an individual without explicit suicidal intent, which has negative effects on the physical and mental well-being of an individual, especially for adolescents. Timely and accessible nursing care may play an important role in the survival and rehabilitation process of NSSI. Methods: In this review, we systematically discuss the nursing care of NSSI behavior and provide recommendations based on an integrated nursing model for NSSI management. Results: As reported in previous studies, a variety of factors can contribute to NSSI behavior, such as personality traits, current psychological status, history of mental illnesses, as well as family and social factors. In-hospital care is the most necessary and effective care during acute episodes of NSSI behavior. To effectively manage inpatients with NSSI behaviors, nurses should first understand the motivation of NSSI, and provide comprehensive and multi-level care through nurse-patient communication, individualized psychological care, and multidisciplinary cooperation with other professionals. While the purpose of out-of-hospital nursing is to reduce the frequency of NSSI behaviors by screening high-risk individuals, providing psychological support, promoting health education, and improving self-awareness. Conclusions: An integrative model of in-hospital and out-of-hospital nursing care can help improve the clinical management and long-term prognosis of patients with NSSI and minimize the risk of suicidal ideation or suicidal behavior.

Effective pharmacotherapy of bipolar depression with mixed features defined by DSM-5 remains unclear in clinical treatment guidelines. Quetiapine (QTP) and valproate have potential treatment utility but are often inadequate as monotherapy. Meanwhile, the efficacy of combination therapies of QTP plus valproate or lithium have yet to be verified. Hence, we conducted a randomized controlled pilot study to evaluate the efficacy of QTP monotherapy in patients with bipolar depression with mixed features defined by DSM-5 and compared the combination therapy of QTP plus valproate (QTP + V) versus QTP plus lithium (QTP + L) for those patients who responded insufficiently to QTP monotherapy. Data was analyzed according to the intent-to-treat population. Generalized linear mixed model was performed by using “nlme” package in R software. A total 56 patients were enrolled, among which, 35 patients responded to QTP alone, and 11 and 10 patients were randomly assigned to QTP + V and QTP + L group, respectively. Nearly 60% enrolled patients responded to QTP monotherapy at the first two weeks treatment. No statistically significant difference in efficacy between QTP + V and QTP + L was observed. In conclusion, QTP monotherapy appeared to be efficacious in patients with bipolar depression with mixed features, and for those who responded insufficiently to QTP, combining with either valproate or lithium appeared to have positive effects.

Bipolar disorder (BD) is one of the major psychiatric disorders that is characterized by recurrent episodes of depression and mania (or hypomania), leading to seriously adverse outcomes with unclear pathogenesis. There is an underlying relationship between bacterial communities residing in the gut and brain function, which together form the gut-brain axis (GBA). Recent studies have shown that changes in the gut microbiota have been observed in a large number of BD patients, so the axis may play a role in the pathogenesis of BD. This review summarizes briefly the relationship between the GBA and brain function, the composition and changes of gut microbiota in patients with BD, and further explores the potential role of GBA-related pathway in the pathogenesis of BD as well as the limitations in this field at present in order to provide new ideas for the future etiology research and drug development.

Tetratricopeptide repeat and ankyrin repeat containing 1 (TRANK1) is a robust risk gene of bipolar disorder (BD). However, little is known on the role of TRANK1 in the pathogenesis of BD and whether the gut microbiota is capable of regulating TRANK1 expression. In this study, we first investigated the serum mRNA level of TRANK1 in medication-free patients with a depressive episode of BD, then a mice model was constructed by fecal microbiota transplantation (FMT) to explore the effects of gut microbiota on brain TRANK1 expression and neuroinflammation, which was further verified by in vitro Lipopolysaccharide (LPS) treatment in BV-2 microglial cells and neurons. 22 patients with a depressive episode and 28 healthy individuals were recruited. Serum level of TRANK1 mRNA was higher in depressed patients than that of healthy controls. Mice harboring ‘BD microbiota’ following FMT presented depression-like phenotype. mRNA levels of inflammatory cytokines and TRANK1 were elevated in mice hippocampus and prefrontal cortex. In vitro, LPS treatment activated the secretion of pro-inflammatory factors in BV-2 cells, which was capable of upregulating the neuronal expression of TRANK1 mRNA. Moreover, primary cortical neurons transfected with plasmid Cytomegalovirus DNA (pcDNA3.1(+)) vector encoding human TRANK1 showed decreased dendritic spine density. Together, these findings add new evidence to the microbiota-gut-brain regulation in BD, indicating that microbiota is possibly involved in the neuropathogenesis of BD by modulating the expression of TRANK1.

The clinical efficacy of lurasidone and quetiapine, two commonly prescribed atypical antipsychotics for bipolar depression, has been inadequately studied in young patients. In this randomized and controlled study, we aimed to compare the effects of these two drugs on cognitive function, emotional status, and metabolic profiles in children and adolescents with bipolar depression. We recruited young participants (aged 10–17 years old) with a DSM-5 diagnosis of bipolar disorder during a depressive episode, who were then randomly assigned to two groups and treated with flexible doses of lurasidone (60 to 120 mg/day) or quetiapine (300 to 600 mg/day) for consecutive 8 weeks, respectively. All the participants were clinically evaluated on cognitive function using the THINC-it instrument at baseline and week 8, and emotional status was assessed at baseline and the end of week 2, 4, and 8. Additionally, the changes in weight and serum metabolic profiles (triglyceride, cholesterol, and fasting blood glucose) during the trial were also analyzed. In results, a total of 71 patients were randomly assigned to the lurasidone group (n = 35) or the quetiapine group (n = 36), of which 31 patients completed the whole treatment course. After an 8-week follow-up, participants in the lurasidone group showed better performance in the Symbol Check Reaction and Accuracy Tests, when compared to those in the quetiapine group. No inter-group difference was observed in the depression scores, response rate, or remission rate throughout the trial. In addition, there was no significant difference in serum metabolic profiles between the lurasidone group and the quetiapine group, including triglyceride level, cholesterol level, and fasting blood glucose level. However, the quetiapine group presented a more apparent change in body weight than the lurasidone group. In conclusion, the present study provided preliminary evidence that quetiapine and lurasidone had an equivalent anti-depressive effect, and lurasidone appeared to be superior to quetiapine in improving the cognitive function of young patients with bipolar depression.

Bipolar disorder (BD) is a severe affective disorder, mainly characterized by alternative depressive and manic or hypomanic episodes, yet the pathogenesis of BD has not been fully elucidated. Recent researches have implicated the altered kynurenine (KYN) metabolism involved in the neurobiology of BD. Excessive activation of the immune system also occurs in patients with BD, which further accelerates the KYN pathway for tryptophan metabolism. Changes of the KYN metabolites have effects on neuronal receptors and are involved in neuroendocrine transmissions. Interactions between KYN metabolism and the immune system may contribute to the neuropathogenesis of BD. Various studies have shown that alterations of the KYN metabolites were associated with mood, psychotic symptoms, and cognitive functions in patients with BD. In this review, we briefly introduce the KYN pathway and describe the immune dysregulation in BD as well as their interactions. We then focus on the research advances on the KYN metabolism in BD, which hold promise for identifying novel treatment targets in patients stricken with this disorder.

The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing methods have shortcomings, such as a long and costly deposit volume acquisition cycle, lack of knowledge and guidance, complex operations for scene modeling expression, and low scene rendering efficiency. Therefore, this paper focuses on the study of a three-dimensional visualization and optimization method for landslide disaster scenes guided by knowledge, and discusses key technologies such as the rapid acquisition of landslide deposit volume information based on three-dimensional reconstruction, the knowledge-guided dynamic modeling visualization of disaster scenes, and scene optimization considering visual significance. The prototype systems are developed and used in a case experiment and analysis. The experimental results show that the proposed method can quickly obtain the deposit volume, and the results are equivalent to ContextCapture, Metashape, and Pix4Dmapper software. The method realizes the dynamic visualization of the whole disaster process, provides rich information, achieves high readability, and improves the efficiency of scene rendering, with a stable average rendering frame rate of more than 80 frames/second.

With the advent of big data, the use of network data to characterize travel has gradually become a trend. Tencent Migration big data can fully, dynamically, immediately, and visually record the trajectories of population migrations with location-based service technology. Here, the daily population flow data of 346 cities during the Spring Festival travel rush in China were combined with different travel modes to measure the spatial structure and spatial patterns of an intercity trip network of Chinese residents. These data were then used for a comprehensive depiction of the complex relationships between the population flows of cities. The results showed that there were obvious differences in the characteristics of urban networks from the perspective of different modes of travel. The intercity flow of aviation trips showed a core-periphery structure with national hub cities as the core distribution. Trips by train showed a core-periphery structure with cities along the national railway artery as the core. This gradually decreased toward hinterland cities. Moreover, the intercity flow of highway trips indicated a spatial pattern of strong local aggregation that matched the population scale.

Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines K-core decomposition and Louvain algorithms. In this method, author keywords are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined as K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. The results show that the run time of the proposed method is significantly less than that of the traditional method.

Abstract Trillions of microorganisms inhabiting in the human gut play an essential role in maintaining physical and mental health. The connections between gut microbiome and neuropsychiatric diseases have been recently identified. The pathogenesis of bipolar disorder, a spectrum of diseases manifesting with mood and energy fluctuations, also seems to be involved in the bidirectional modulation of the microbiome‐gut‐brain (MGB) axis. In this review, we briefly introduce the concept of MGB axis, and then focus on the previous findings in human studies associated with bipolar disorder. These studies provided preliminary evidences on the gut microbial alterations in bipolar disorder. Limitations in these studies and future directions in this research field, such as fecal microbiome transplantation and microbiome‐targeted therapy, were discussed. A research framework linking gut microbiome to determinants and health‐related outcomes in BD was also proposed. Better characterizing and understanding of gut microbial biosignatures in bipolar patients contribute to clarify the etiology of this intractable disease and pave the new way for treatment innovation.

Abstract Discriminating depressive episodes of bipolar disorder (BD) from major depressive disorder (MDD) is a major clinical challenge. Recently, gut microbiome alterations are implicated in these two mood disorders; however, little is known about the shared and distinct microbial characteristics in MDD versus BD. Here, using 16S ribosomal RNA (rRNA) gene sequencing, the microbial compositions of 165 subjects with MDD are compared with 217 BD, and 217 healthy controls (HCs). It is found that the microbial compositions are different between the three groups. Compared to HCs, MDD is characterized by altered covarying operational taxonomic units (OTUs) assigned to the Bacteroidaceae family, and BD shows disturbed covarying OTUs belonging to Lachnospiraceae, Prevotellaceae, and Ruminococcaceae families. Furthermore, a signature of 26 OTUs is identified that can distinguish patients with MDD from those with BD or HCs, with area under the curve (AUC) values ranging from 0.961 to 0.986 in discovery sets, and 0.702 to 0.741 in validation sets. Moreover, 4 of 26 microbial markers correlate with disease severity in MDD or BD. Together, distinct gut microbial compositions are identified in MDD compared to BD and HCs, and a novel marker panel is provided for distinguishing MDD from BD based on gut microbiome signatures.

The mechanism of bipolar disorder is unclear. Growing evidence indicates that gut microbiota plays a pivotal role in mental disorders. This study aimed to find out changes in the gut microbiota in bipolar depression (BD) subjects following treatment with quetiapine and evaluate their correlations with the brain and immune function. Totally 36 subjects with BD and 27 healthy controls (HCs) were recruited. The severity of depression was evaluated with the Montgomery-Asberg depression rating scale (MADRS). At baseline, fecal samples were collected and analyzed by quantitative polymerase chain reaction (qPCR). T lymphocyte subsets were measured to examine immune function. Near-infrared spectroscopy (NIRS) was used to assess brain function. All BD subjects received quetiapine treatment (300 mg/d) for four weeks, following which the fecal microbiota and immune profiles were reexamined. Here, we first put forward the new concept of brain-gut coefficient of balance (B-GCB), which referred to the ratio of [oxygenated hemoglobin]/(Bifidobacteria to Enterobacteriaceae ratio), to analyze the linkage between the gut microbiota and brain function. At baseline, the CD3+ T cell proportion was positively correlated with log10 Enterobacter spp count, whereas the correlativity between the other bacteria and immune profiles were negative. Log10 B-GCB was positively correlated with CD3+ T cell proportion. In subjects with BD, counts of Faecalibacterium prausnitzii, Bacteroides–Prevotella group, Atopobium Cluster, Enterobacter spp, and Clostridium Cluster IV were higher, whereas the log10 (B/E) were lower than HCs (B/E refers to Bifidobacteria to Enterobacteriaceae ratio and represents microbial colonization resistance). After treatment, MADRS scores were reduced, whereas the levels of Eubacterium rectale, Bifidobacteria, and B/E increased. The composition of the gut microbiota and its relationship to brain function were altered in BD subjects. Quetiapine treatment was effective for depression and influenced the composition of gut microbiota in patients.Clinical Trial Registration:http://www.chictr.org.cn/index.aspx, identifier ChiCTR-COC-17011401, URL: http://www.chictr.org.cn/listbycreater.aspx.

Abstract This study aims to characterize the gut microbiota in depressed patients with bipolar disorder (BD) compared with healthy controls (HCs), to examine the effects of quetiapine treatment on the microbiota, and to explore the potential of microbiota as a biomarker for BD diagnosis and treatment outcome. Analysis of 16S‐ribosomal RNA gene sequences reveals that gut microbial composition and diversity are significantly different between BD patients and HCs. Phylum Bacteroidetes and Firmicutes are the predominant bacterial communities in BD patients and HCs, respectively. Lower levels of butyrate‐producing bacteria are observed in untreated patients. Microbial composition changes following quetiapine treatment in BD patients. Notably, 30 microbial markers are identified on a random forest model and achieve an area under the curve (AUC) of 0.81 between untreated patients and HCs. Ten microbial markers are identified with the AUC of 0.93 between responder and nonresponder patients. This study characterizes the gut microbiota in BD and is the first to evaluate microbial changes following quetiapine monotherapy. Gut microbiota‐based biomarkers may be helpful in BD diagnosis and predicting treatment outcome, which need further validations.