Objective The literature on large-scale studies of Chinese patients with adolescent-onset bipolar disorder (adolescent-onset BD) was limited. Based on the analysis of the National Bipolar Mania Pathway Survey (BIPAS) Phase II data, we examined the demographic and clinical characteristics of adults with adolescent-onset BD. Methods Among 899 participants diagnosed with BD from 20 mental health services, demographics and clinical data were collected at screening. Comparisons were made using chi-square (or Fisher's exact) tests and ANOVA. Multivariate logistic regression identified independent factors for adolescent-onset BD, and a CHAID decision tree analysis (SPSS) was constructed to detect risk factors. Results In the sample, 360 (40%) had adolescent-onset BD and 539 (60%) adult-onset BD. Significant differences between the two groups were observed in current age, number of episodes, years of education, gender, age of onset, education level, marital status, occupation, comorbid chronic physical illness, and first episode type. Stratified analysis also revealed significant differences between adolescent-onset BD I and BD II. Multivariate logistic regression identified younger onset age, more frequent episodes, lower education level, marital status, occupation, first episode type, and prior hospitalization as independent factors for adolescent-onset BD. The decision tree model selected current age as the first splitting variable, followed by occupation and marital status as the second, and years of education and prior hospitalization as the third. Conclusions Adolescent-onset BD exhibits distinct demographic and clinical features compared to adult-onset BD. Early recognition and tailored treatment strategies may improve prognosis and outcomes in this population.

IntroductionIndividuals at clinical high risk for bipolar disorder (CHR-BD) experienced insufficient recognition. Little is known regarding the association between exosome microRNA (miRNA) profile and bipolar disorder (BD) risk.Materials and methodsTwenty youth at CHR-BD, 21 patients with BD, and 24 healthy controls were recruited in this study. Exosomal small RNA sequencing was undertaken in the plasma sample of the participants. Using machine-learning algorithms, target miRNAs were selected from differentially expressed candidates. Predictive models were built and tested on validation set.ResultsThe study identified two miRNAs that showed significantly differential expression between the CHR-BD group and the HC group: hsa-miR-184 (log2FC = 4.22, P = 1.49E-04) and hsa-miR-196a-5p (log2FC = 4.75, P = 3.56E-04). Random forest (RF) and eXtreme Gradient XGBoost jointly selected two overlapping miRNAs: hsa-miR-1908-3p and hsa-miR-412-5p. XGBoost outperformed the RF model with higher AUCs (BD group: 0.71 vs 0.71, CHR-BD group: 0.74 vs 0.72, HC group: 0.60 vs 0.57).ConclusionThe study identified four target miRNAs involved in neuroimmunity and neuronal plasticity, supported by literature linking these miRNAs to neuropsychiatric diseases, suggesting their potential as biomarkers for early BD. Future research should integrate additional biomarkers for improved discriminative performance.

There is a high prevalence of suicidal ideation (SI), suicide attempts (SA), and non-suicidal self-injury (NSSI) in bipolar disorder (BD). Understanding the nature of suicidality and NSSI in BD is an important way to inform optimal intervention for reducing suicide risk. We aimed to investigate the prevalence and correlates of SI, SA, and NSSI in patients with BD using data from a national survey. We used network analysis to explore the associations among suicidality, NSSI, addictive features of NSSI, and symptoms of BD. Participants with BD were recruited from 20 research centers in China. Suicidality, NSSI, addictive features, and symptoms of BD were measured via a standardized electronic case report form. We used logistic regression and network analysis for data analysis. Of the 1,055 participants recruited, over 50

This study employs artificial intelligence methods to predict mood phases in patients with bipolar disorder, addressing the issue of poor prognosis caused by recurrent episodes and uncertainty in mood phases. To explore the patterns of mood transitions in patients with bipolar disorder, we developed a mood phase transition model using a Transformer model to investigate whether predicting mood changes can improve prognosis. We conducted cohort follow-up assessments of patients with bipolar disorder. At each visit, patients were evaluated using the Hamilton Depression Rating Scale (HAMD) and the Young Mania Rating Scale (YMRS) as clinician-rated assessments, along with the BDCC self-rating scale. We then input these data into several different AI models for training and validated the models’ performance using the data. The study was conducted through online medical platforms and offline follow-up evaluations. The study included 812 patients diagnosed with bipolar disorder according to DSM-5 criteria, who had at least one BDCC assessment result and at least one depressive episode meeting HAMD criteria and one manic/hypomanic episode meeting YMRS criteria. In the experiment utilizing current self-assessment scales for rapid identification of affective states, the best performance was observed with the Transformer and RF models, with AUCs for affective state identification of 0.83 (95 http://ClinicalTrials.gov under the identifier NCT02015143

OBJECTIVE:We collected maintenance treatment medication information from Chinese multicenter bipolar disorder patients, exploring the characteristics of medication use and related factors. We analyzed medication guideline adherence and compared trends in medication use with a study from 2014. METHODS:323 patients receiving maintenance therapy for bipolar disorder across 20 hospitals have been recruited. Their prescription information was collected to compare with the recommended drug protocols outlined by CANMAT to assess consistency. Additionally, descriptive statistics were conducted to document the quantities and percentages of different classes of medications used. Regression analysis was employed to explore factors influencing medication choices. RESULT:The rate of medication inconsistency in this study was 12.38 %, characterized by inappropriate use of antidepressants and mood stabilizers. The rate of using additional drugs was 67.80 %, with antipsychotics being the most common adjunctive treatment in the Bipolar I group and antidepressants in the Bipolar II group. Factors influencing the occurrence of adjunctive therapy included previous hospitalization, most recent episode type, and gender. The rate of polypharmacy was 47.10 %, with previous hospitalization and most recent episode type being the main influencing factors. CONCLUSION:There has been a noticeable improvement in guideline concordance in medication use compared to 2014; however, improper use of antidepressants remains a significant clinical issue. The increasing prevalence of adjunctive medication use underscores the need for more personalized medication recommendations in clinical guidelines.

Background: Bipolar disorder (BD) is a chronic, severe mental illness with a significant socio-economic burden. The early recognition of BD requires reliable and valid instruments. Objective: We evaluated the psychometric attributes of the Bipolar Prodrome Symptom Interview and Scale-Full Prospective (BPSS-FP)-Chinese version. Methods: Altogether, 129 participants, aged 16-35 years meeting DSM-5 criteria for major depressive disorder (MDD, n = 27) or BD (n = 76), and 26 healthy controls were recruited. Subjects were evaluated with the BPSS-FP Chinese version, Young Mania Rating Scale (YMRS), Montgomery-Asberg Depression Rating Scale (MADRS), and Cyclothymic-Hypersensitive Temperament questionnaire (CHT). Internal consistency, interrater reliability, criterion validity, and discriminant validity of the BPSS-FP-Chinese version were assessed. Results: Cronbach alpha indices were 0.87, 0.86, and 0.66 for the manic, depressive, and general subscales of BPSS-FP-Chinese version, respectively. Interrater correlation was high, with total agreements of 0.94, 0.95, and 0.94 for the manic, depressive, and general index, respectively. Significant correlations (r >= 0.50) were noted between the BPSS-FP manic subscale and both the YMRS and CHT, the depressive subscale and the MADRS, and the general symptom subscale and the CHT. Moderate correlations were detected between the BPSS-FP general symptoms and both the YMRS and CHT (r = 0.40 similar to <0.50). Furthermore, the BPSS-FP mania index demonstrated a moderate correlation with the MADRS, while the depression index had a small correlation with the YMRS (r = 0.10 similar to <0.40). Conclusions: The BPSS-FP-Chinese version has good psychometric properties and can be applied in both clinical and research settings for screening subjects with a risk for BD.

Abstract Bipolar disorder is characterized by recurrent episodes of depression and mania, making diagnosis and treatment challenging, often leading to poor prognosis. Predicting future episodes based on the assessment of current and past emotional phases in patients can offer valuable insights for clinicians, aiding in the prevention of misdiagnosis and relapse, and in intervention strategies. This study included 812 patients with bipolar disorder from 9 centers, who were assessed online or offline using BDCC, YMRS, and HAMD tools. We employed four AI methodologies to learn from the assessment results and time series, ultimately predicting future emotional phases of the patients. By learning from current assessment results to predict the next emotional phase, the AUCs of the four models were 0.81, 0.70, 0.84, and 0.81, respectively. External validation on 30-day and 90-day emotional phase follow-ups of patients showed prediction accuracies of 0.9, 0.65, 0.86, and 0.62 for each model. While predictive efficacy decreased over time, the average AUC remained above 0.75 at 180 days. Our findings suggest that utilizing big data and AI methods can effectively learn the characteristics of emotional phase transitions in bipolar patients, achieving high accuracy. This model holds significant value for clinical diagnosis and medication guidance for clinicians

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

IntroductionDepressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).ResultsOur results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%.DiscussionThese findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.

Objective:This study investigates the difference in the detection rate and symptomatology between ICD-10 and DSM-5 diagnostic criteria for bipolar disorder with mixed states.Methods:Based on the Phase Ⅰ (2012) and Phase Ⅱ (2021) databases of National Bipolar Mania Pathway Survey (BIPAS), patients with bipolar disorder were included. General demographic data, clinical characteristics, symptomatic phenotypes, and mixed characteristics were retrieved. The detection rates and symptomatic performances of patients with or without mixed states in Phase Ⅰ and Ⅱ were compared using the chi-square test.Results:For patients with mixed states, the detection rate during Phase Ⅱ (2021) using DSM-5 (18.79%, 199/1 059) criteria was significantly higher than that during Phase Ⅰ (2012) using ICD-10 (6.78%, 199/2 934; χ 2=125.05, P<0.001). Whether using ICD-10 or DSM-5 criteria, patients with mixed states had a significantly higher frequency of multiple symptomatic manifestations. Conclusion:The DSM-5 diagnostic criteria generate a high detection rate for bipolar disorder with mixed states. The clinical phenotypes of bipolar disorder with mixed states vary significantly using different diagnostic tools.

Introduction Processed betel quid product chewing is a public health problem in areca non-plant areas in China. However, there is no valid instrument to screen for betel quid use disorder (BQUD) in mainland China. We developed a self-administered screening test for betel quid use disorders (SST-BQUD) and tested its reliability and validity in a sample of betel quid chewers (BQCers) in Hunan, China. Methods Items of SST-BQUD were selected from the test results of an item pool, which includes 52 questions related to the psycho-social and behavioral presentations of BQUD. All participants, in a self-administered manner, completed the item pool. A subsample completed the re-test one week later. Two psychiatrists interviewed all participants to ascertain the presence of BQUD. The receiver Operating Characteristic curve was used to determine the best cut-off value to discriminate BQUD. Results One hundred and twelve BQCers were recruited. Based on the statistical analysis of receiver operating characteristic (ROC) curves, 14 yes/no questions were selected for SST-BQUD. As indicated by Cronbach’s α coefficient, the internal consistency was 0.876. The area under the curve of SST-BQUD was 0.881, representing a satisfactory diagnostic value. The one-week re-test reliability test was 0.771 (P<0.001), suggesting good stability over time. The optimal cut-off score for BQUD screening was six, with a sensitivity of 0.921 and a specificity of 0.716, implying the satisfactory accuracy of SST-BQUD to screen for BQUD. Conclusion The standard version of SST-BQUD consists of 14 items. The total score of SST-BQUD was the sum of affirmative answers, with higher scores denoting a more severe BQUD symptom. If one answered six or more times “yes” to these 14 questions, they can be classified with BQUD. The SST-BQUD is a valid screening method for BQUD among BQCers in betel quid processed area.

Objective: This study aimed to investigate the Chinese norms for the Symptom Checklist 90 (SCL-90) scale and its application. Methods: In total, 7,489 adults from Tianjin and Qingdao in China were included. Their data were compared with the norm data of 1,388 people published by Jin et al., the combined norms published by Tang et al., the data of 2,808 adults published by Chen and Li, and the data of 1,890 adults from Tong in China. Results: In five different periods, notable changes were observed in each factor of the SCL-90 that significantly differed from the previous norms. The scores of each factor showed an increasing annual trend. Compulsion consistently obtained the highest scores, and phobia consistently obtained the lowest scores. The scores tended to decrease from compulsion to anxiety, and psychosis scored lower than paranoia. There was a significant difference in the detection rate between the critical screening value of two points and the standard score. Using the standard score as the critical value, the detection rate ranged between 13 and 16% and was relatively concentrated. Using two points as the critical value, the detection rate ranged between 38 and 50%. Conclusion: The usual model in China is not consistent with social development. Using two points as the critical value is no longer suitable for the SCL-90. New Chinese norms and measurement standards should be developed. The mean value plus one standard deviation could be used as the new measurement standard.

Shift workers are mostly suffered from the disruption of circadian rhythm and health problems. In this study, we designed proper light environment to maintain stable circadian rhythm, cognitive performance, and mood status of shift workers. We used five-channel light-emitting diodes to build up the dynamic daylight-like light environment. The illuminance, correlated color temperature, and circadian action factor of light were tunable in the ranges of 226 to 678 lx, 2680 to 7314 K, and 0.32 to 0.96 throughout the day (5:30 to 19:40). During the nighttime, these parameters maintained about 200 lx, 2700 K, and 0.32, respectively. In this light environment, three subjects had engaged in shift work for 38 consecutive days. We measured plasma melatonin, activity counts, continuous performance tests, and visual analogue scale on mood to assess the rhythm, cognitive performance, and mood of subjects. After 38-day shift work, the subjects' peak melatonin concentration increased significantly. Their physiological and behavioral rhythms maintained stable. Their cognitive performance improved significantly after night work, compared with that before night work. Their mood status had no significant change during the 38-day shift work. These results indicated that the light environment was beneficial to maintain circadian rhythm, cognitive performance and mood status during long-term shift work in closed environment.

Empathy is the ability to generate emotional responses (i.e., cognitive empathy) and to make cognitive inferences (i.e., affective empathy) to other people’s emotions. Empirical evidence suggests that patients with bipolar disorder (BD) exhibit impairment in cognitive empathy, but findings on affective empathy are inconsistent. Few studies have examined the neural mechanisms of cognitive and affective empathy in patients with BD. In this study, we examined the empathy-related resting-state functional connectivity (rsFC) in BD patients. Thirty-seven patients with BD and 42 healthy controls completed the self-report Questionnaires of Cognitive and Affective Empathy (QCAE), the Yoni behavioural task, and resting-sate fMRI brain scans. Group comparison of empathic ability was conducted. The interactions between group and empathic ability on seed-based whole brain rsFC were examined. BD patients scored lower on the Online Simulation subscale of the QCAE and showed positive correlations between cognitive empathy and the rsFC of the dorsal Medial Prefrontal Cortex (dmPFC) with the lingual gyrus. The correlations between cognitive empathy and the rsFC of the temporal–parietal junction (TPJ) with the fusiform gyrus, the cerebellum and the parahippocampus were weaker in BD patients than that in healthy controls. These findings highlight the underlying neural mechanisms of empathy impairments in BD patients.

Hubs in the brain network are the regions with high centrality and are crucial in the network communication and information integration. Patients with schizophrenia (SCZ) exhibit wide range of abnormality in the hub regions and their connected functional connectivity (FC) at the whole-brain network level. Study of the hubs in the brain networks supporting complex social behavior (social brain network, SBN) would contribute to understand the social dysfunction in patients with SCZ. Forty-nine patients with SCZ and 27 healthy controls (HC) were recruited to undertake the resting-state magnetic resonance imaging scanning and completed a social network (SN) questionnaire. The resting-state SBN was constructed based on the automatic analysis results from the NeuroSynth. Our results showed that the left temporal lobe was the only hub of SBN, and its connected FCs strength was higher than the remaining FCs in both two groups. SCZ patients showed the lower association between the hub-connected FCs (compared to the FCs not connected to the hub regions) with the real-life SN characteristics. These results were replicated in another independent sample (30 SCZ and 28 HC). These preliminary findings suggested that the hub-connected FCs of SBN in SCZ patients exhibit the abnormality in predicting real-life SN characteristics.

Social behaviour requires the brain to efficiently integrate multiple social processes, but it is not clear what neural substrates underlie general social behaviour. While psychosis patients and individuals with subclinical symptoms are characterized by social dysfunction, the neural mechanisms underlying social dysfunctions in schizophrenia spectrum disorders remains unclear. We first constructed a general social brain network (SBN) using resting-state functional connectivity (FC) with regions of interest based on the automatic meta-analysis results from NeuroSynth. We then examined the general SBN and its relationship with social network (SN) characteristics in 30 individuals with schizophrenia (SCZ) and 33 individuals with social anhedonia (SA). We found that patients with SCZ exhibited deficits in their SN, while SA individuals did not. SCZ patients showed decreased segregation and functional connectivity in their SBN, while SA individuals showed a reversed pattern with increased segregation and functional connectivity of their SBN. Sparse canonical correlation analysis showed that both SCZ patients and SA individuals exhibited reduced correlation between SBN and SN characteristics compared with their corresponding healthy control groups. These preliminary findings suggest that both SCZ and SA participants exhibit abnormality in segregation and functional connectivity within the general SBN and reduced correlation with SN characteristics. These findings could guide the development of non-pharmacological interventions for social dysfunction in SCZ spectrum disorders.

目的 针对体检人群心理健康测评场景,修订症状自评量表(SCL-90)中文版常模.方法 抽取2019年青岛市区体检中心成人体检样本1826例,并使用1986年金华等的常模资料、1999年唐秋萍等的合并常模资料、陈树林等杭州2808人数据资料以及2006年童辉杰全国范围内1890人的数据资料与之进行比较分析.结果 2019年样本的SCL-90各因子分均高于金华等、唐秋萍等、陈树林等以及童辉杰的数据资料(P<0.001).样本各个因子上的得分呈逐年上升的趋势.所有的数据集都是强迫因子得分最高,恐怖因子得分最低.使用传统的2分的因子粗分作为体检人群心理健康筛查的临界值与参照本研究提出的常模的检出率比较,除恐怖因子外,其余各因子检出率差异均存在统计学意义(P<0.001).结论 以2分作为临界值不适应体检人群SCL-90的筛查需要,本研究提出的根据现实人群修订的常模数据作为衡量标准更适用.

Introduction Alteration of empathy is common in patients with psychiatric disorders. Reliable and valid assessment tools for measuring empathy of clinical samples is needed. The Questionnaire of Cognitive and Affective Empathy (QCAE) is a newly-developed instrument to capture cognitive and affective components of empathy. This study aimed to validate the QCAE and compared self-reported empathy between clinical groups with varied psychiatric diagnoses and healthy sample. Methods The present study performed factor analysis for the QCAE on clinical samples in the Chinese setting (n = 534), including patients with schizophrenia (n = 158), bipolar disorder (n = 213) and major depressive disorder (n = 163). Internal consistency, internal correlation and convergent validity was examined in the subsample (n = 361). Group comparison among patients with schizophrenia, bipolar disorder, major depressive disorder and healthy controls (n = 107) was conducted to assess the discriminant validity. Results Our results indicated acceptable factor model, good reliability and validity of the QCAE. Impaired cognitive empathy was found in clinical samples, especially in patients with schizophrenia, while higher affective empathy was found in patients with bipolar disorder and major depressive disorder. Conclusion The QCAE is a useful tool in assessing empathy in patients with varied psychiatric diagnoses.

<span id="ChDivSummary" name="ChDivSummary" class="abstract-text">&lt;正&gt;亲爱的精神障碍病友和家属朋友们,新型冠状病毒感染肺炎疫情不断升级,政府出台大量措施防控,我们了解到有些病友因恐慌出现了情绪问题和身体不舒服,甚至个别病友精神症状发生了波动。在此,我们给大家几点建议。1 坚持规律服药断药和不规律服药是精神疾病复发的最主要原因之一。大家可用手机或闹钟设置定时服药提醒。另外,现在的疫情防控措施不方便我们出行,有些朋友因手头儿备的药所剩不多而出现焦虑情绪。朋友们可以通过拨打自己通常就诊医院的咨询或者值班</span>