BackgroundThe evaluation of childhood trauma is essential for the treatment of schizophrenia. The short form of Childhood Trauma Questionnaire (CTQ-SF) is a widely used measure of the experience of childhood trauma in the general population. Nevertheless, data regarding the psychometric property of CTQ-SF for assessing childhood trauma of patients with schizophrenia are very limited.MethodsTwo hundred Chinese inpatients with schizophrenia completed the Chinese CTQ-SF, the Child Psychological Maltreatment Scale (CPMS), the Impact of Events Scale-Revised (IES-R), and the Dissociative Experiences Scale-II (DES-II). To assess test-retest reliability of the CTQ-SF, all patients completed the CTQ-SF again two weeks later. Concurrent and convergent validity was assessed by analyzing Pearson bivariate correlation coefficients between CTQ-SF and CPMS, IES-R, and DES-II.ResultsThe Cronbach’s α coefficient of the Chinese CTQ-SF was 0.81, and the two-week re-test reliability was 0.81 (P<0.01). The criterion-related validity coefficients of CTQ-SF with the CMPS, IES-R and DES-II were 0.61, 0.41, and 0.51, respectively.ConclusionThe Chinese CTQ-SF has satisfactory psychometric properties to measure childhood abuse or neglect in Chinese inpatients with schizophrenia.

Schizophrenia (SZ) detection enables effective treatment to improve the clinical outcome, but objective and reliable SZ diagnostics are still limited. An ideal diagnosis of SZ suited for robust clinical screening must address detection throughput, low invasiveness, and diagnosis accuracy. Herein, we built a multi‐shelled hollow Cr2O3 spheres (MHCSs) assisted laser desorption/ionization mass spectrometry (LDI MS) platform for the direct metabolic profiling of biofluids towards SZ diagnostics. The MHCSs displayed strong light absorption for enhanced ionization and microscale surface roughness with stability for the effective LDI of metabolites. We profiled urine and serum metabolites (≈1 μL) with the enhanced LDI efficacy in seconds. We discriminated SZ patients (SZs) from healthy controls (HCs) with the highest area under the curve (AUC) value of 1.000 for the blind test. We identified four compounds with optimal diagnostic power as a simplified metabolite panel for SZ and demonstrated the metabolite quantification for clinic use. Our approach accelerates the growth of new platforms toward a precision diagnosis in the near future.

This randomized, parallel-group, open study investigated the efficacy and safety of risperidone oral solution (RIS-OS) in combination with clonazepam and intramuscular haloperidol for the treatment of acute agitation in patients with schizophrenia, and the study explored the possibility of decreasing the efficacy of an acute 6-week treatment by switching intramuscular haloperidol injection to RIS-OS. Two hundred and five agitation-exhibiting schizophrenic inpatients at six hospitals were originally included in the study. The 47-day trial consisted of 5 days (session I) of receiving either oral treatment (RIS-OS plus clonazepam) or intramuscular treatment (intramuscular haloperidol) and a 42-day (session II) period of either withdrawing from clonazepam or shifting from intramuscular haloperidol to a RIS-OS period. The primary efficacy outcome was measured as the change in the Positive and Negative Syndrome Scale-Excited Component (PANSS-EC) in session I and the change in the PANSS in session II. Safety was assessed by the frequency of the adverse events. Mean PANSS-EC improvement was significant after 5 days of treatment in both groups (P>0.05) and was similar between the two treatment groups (P<0.01). Most patients' PANSS-EC scores improved or remained stable during the drawback/shift treatment period. Efficacy was not significantly different between the two treatment groups after the 6-week treatment (P>0.05). However, combination treatment exhibited greater efficacy, and adverse events, especially extrapyramidal symptoms, were lower with the oral treatment than with the intramuscular treatment in session I. These results show that RIS-OS in combination with clonazepam is an effective treatment, comparable with intramuscular haloperidol, and is well-tolerated for acute agitation in patients with schizophrenia.

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Aim: This study investigated the clinical characteristics of internet addiction using a cross-sectional survey and psychiatric interview. Methods:A structured questionnaire consisted of demographics, Symptom Checklist 90, Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Young's Internet Addiction Test (YIAT) was administered to students of two secondary schools in Wuhan, China. Students with a score of 5 or higher on the YIAT were classified as having Internet Addiction Disorder (IAD). Two psychiatrists interviewed students with IAD to confirm the diagnosis and evaluate their clinical characteristics.Results: Of a total of 1076 respondents (mean age 15.4 ± 1.7 years; 54.1% boys), 12.6% (n = 136) met the YIAT criteria for IAD. Clinical interviews ascertained the Internet addiction of 136 pupils and also identified 20 students (14.7% of IAD group) with comorbid psychiatric disorders. Results from multinomial logistic regression indicated that being male, in grade 7-9, poor relationship between parents and higher self-reported depression scores were significantly associated with the diagnosis of IAD. Conclusion:These results advance our understanding of the clinical characteristics of Internet addiction in Chinese secondary school students and may help clinicians, teachers, and other stakeholders better manage this increasingly serious mental condition.

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research. Although researchers hope that artificial intelligence can contribute to the diagnosis and treatment of depression, the traditional centralized machine learning needs to aggregate patient data, and the data privacy of patients with mental illness needs to be strictly confidential, which hinders machine learning algorithms clinical application.To solve the problem of privacy of the medical history of patients with depression, we implement federated learning to analyze and diagnose depression. First, we propose a general multi-view federated learning framework using multi-source data,which can extend any traditional machine learning model to support federated learning across different institutions or parties. Secondly, we adopt late fusion methods to solve the problem of inconsistent time series of multi-view data. Finally, we compare the federated framework with other cooperative learning frameworks in performance and discuss the related results.

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