OBJECTIVES:P2RX7 has been implicated in bipolar disorder, major depressive disorder, schizophrenia, anxiety disorders, Alzheimer's disease, and Parkinson's disease. However, the specificity and comparability of these associations remain unclear. This study aimed to systematically evaluate multiple neuropsychiatric disorders to identify those most robustly associated with P2RX7. METHODS:We analyzed 1861 imputed SNPs spanning the P2RX7 gene in 1,087,925 individuals from 72 independent cohorts across 18 neuropsychiatric disorders. SNP-disease associations were assessed within each cohort, followed by meta-analysis and false discovery rate (FDR) correction to identify significant disease-risk variants. P2RX7 mRNA and protein expression across tissues or cells was characterized. Functional analyses evaluated the regulatory effects of disease-associated SNPs on P2RX7 mRNA expression, subcortical gray matter volumes (GMVs), cortical surface area (SA), and cortical thickness (TH). RESULTS:Bipolar disorder showed the strongest association with P2RX7 variants in European Americans (EAs) (4.0 × 10-8 ≤ p ≤ 0.004; 3.8 × 10-5 ≤ q ≤ 0.05), followed by schizophrenia in EAs (8.9 × 10-6 ≤ p ≤ 2.6 × 10-4; 9.4 × 10-3 ≤ q ≤ 0.043) and Chinese populations (2.1 × 10-5 ≤ p ≤ 1.7 × 10-3; 6.8 × 10-3 ≤ q ≤ 0.049), and major depression in both EAs (p = 4.1 × 10-5; q = 0.030) and Chinese (4.3 × 10-5 ≤ p ≤ 0.009; 6.1 × 10-3 ≤ q ≤ 0.046). The significance of most associations and their relative ranking across disorders was maintained in the trans-ancestry meta-analysis. Expression analysis revealed that P2RX7 mRNA and protein expression were abundant in the brain, glial cells and macrophages. Approximately half of the disease-associated SNPs significantly influenced P2RX7 mRNA expression in nine brain regions (1.0 × 10-7 ≤ p ≤ 0.047) and altered GMV, SA, and TH of seven brain regions (1.9 × 10-4 ≤ p ≤ 3.4 × 10-3). CONCLUSION:P2RX7 is most consistently and specifically associated with bipolar disorder, schizophrenia, and major depression, supported by both statistical and biological evidence.

Left-behind children (LBC) have become a special population to be concerned due to the negative consequences of parental absence during their physical and psychological development in China. Expressive suppression (ES) is a response-focused emotion regulation and may be frequently used by LBC to suppress their emotions resulting in different forms of internalizing problems. The objective of the present study was to investigate the role of ES as an emotion regulation strategy on anxiety in Chinese left-behind children in middle school (LBC-MS) by considering the mediating role(s) of psychological resilience and self-esteem. 820 middle school students aged between 12 and 17 years from a middle school in Xiangtan, Hunan Province, participated in the study. Screen for Child Anxiety Related Emotional Disorders (SCARED), Emotion Regulation Questionnaire (ERQ), Resilience Scale for Chinese Adolescents (RSCA), and Rosenberg Self-Esteem Scale (SES) were administered. Variables measured using the above scales in left-behind children in middle school (LBC-MS) and non-left-behind children in middle school (non-LBC-MS) were compared, and descriptive statistics were used to present the overall characteristics. Then the PROCESS macro of SPSS was used to conduct regression-based statistical mediation for the data of 211 left-behind children. This study revealed that LBC-MS had higher anxiety and ES scores and lower psychological resilience and self-esteem scores than non-LBC-MS (Ps < 0.01). ES was found positively associated with anxiety in LBC-MS and negatively associated with psychological resilience and self-esteem (Ps < 0.05 − 0.01). Specifically, both psychological resilience and self-esteem significantly mediated the association between ES and anxiety, accounting for 7.50

目的 探讨留守初中生群体的心理韧性和表达抑制在自尊与焦虑情绪之间的双重中介效应.方法 采用儿童焦虑性情绪障碍筛查表(SCARED)、自尊量表(SES)、青少年心理韧性量表(RSCA)、情绪调节问卷(ERQ)对820名在校初中生进行问卷调查.结果 ①与非留守初中生相比,留守初中生的焦虑水平更高,自尊及心理韧性水平更低,更多使用表达抑制策略(Ps<0.01);②自尊、心理韧性与焦虑情绪呈显著负相关,表达抑制与焦虑情绪呈显著正相关(Ps<0.01);③心理韧性和表达抑制在留守初中生自尊与焦虑之间起双重中介作用,其中介效应量分别占总效应的26.81％和15.30％,且二者的中介效应差异不显著.结论 自尊既可以直接影响留守初中生的焦虑情绪,也可以通过心理韧性、表达抑制的双重中介间接影响焦虑情绪.提示通过增强留守初中生的自尊感和心理韧性水平,同时鼓励或教会他们充分表达自己的情感,将有益于减轻焦虑情绪,促进他们的心理健康.

Cortical and subcortical structural alteration has been extensively reported in schizophrenia, including the unusual expansion of gray matter volumes (GMVs) of basal ganglia (BG), especially putamen. Previous genome-wide association studies pinpointed kinectin 1 gene (KTN1) as the most significant gene regulating the GMV of putamen. In this study, the role of KTN1 variants in risk and pathogenesis of schizophrenia was explored. A dense set of SNPs (n = 849) covering entire KTN1 was analyzed in three independent European- or African-American samples (n = 6704) and one mixed European and Asian Psychiatric Genomics Consortium sample (n = 56,418 cases vs. 78,818 controls), to identify replicable SNP-schizophrenia associations. The regulatory effects of schizophrenia-associated variants on the KTN1 mRNA expression in 16 cortical or subcortical regions in two European cohorts (n = 138 and 210, respectively), the total intracranial volume (ICV) in 46 European cohorts (n = 18,713), the GMVs of seven subcortical structures in 50 European cohorts (n = 38,258), and the surface areas (SA) and thickness (TH) of whole cortex and 34 cortical regions in 50 European cohorts (n = 33,992) and eight non-European cohorts (n = 2944) were carefully explored. We found that across entire KTN1, only 26 SNPs within the same block (r2 > 0.85) were associated with schizophrenia across ≥ 2 independent samples (7.5 × 10–5 ≤ p ≤ 0.048). The schizophrenia-risk alleles, which increased significantly risk for schizophrenia in Europeans (q < 0.05), were all minor alleles (f < 0.5), consistently increased (1) the KTN1 mRNA expression in 12 brain regions significantly (5.9 × 10–12 ≤ p ≤ 0.050; q < 0.05), (2) the ICV significantly (6.1 × 10–4 ≤ p ≤ 0.008; q < 0.05), (3) the SA of whole (9.6 × 10–3 ≤ p ≤ 0.047) and two regional cortices potentially (2.5 × 10–3 ≤ p ≤ 0.042; q > 0.05), and (4) the TH of eight regional cortices potentially (0.006 ≤ p ≤ 0.050; q > 0.05), and consistently decreased (1) the BG GMVs significantly (1.8 × 10–19 ≤ p ≤ 0.050; q < 0.05), especially putamen GMV (1.8 × 10–19 ≤ p ≤ 1.0 × 10–4; q < 0.05, (2) the SA of four regional cortices potentially (0.010 ≤ p ≤ 0.048), and (3) the TH of four regional cortices potentially (0.015 ≤ p ≤ 0.049) in Europeans. We concluded that we identified a significant, functional, and robust risk variant block covering entire KTN1 that might play a critical role in the risk and pathogenesis of schizophrenia.

BACKGROUND Neuropsychiatric disorders are highly heritable and have overlapping genetic underpinnings. Single nucleotide polymorphisms (SNPs) in the gene CACNA1C have been associated with several neuropsychiatric disorders, across multiple genome-wide association studies. METHOD A total of 70,711 subjects from 37 independent cohorts with 13 different neuropsychiatric disorders were meta-analyzed to identify overlap of disorder-associated SNPs within CACNA1C. The differential expression of CACNA1C mRNA in five independent postmortem brain cohorts was examined. Finally, the associations of disease-sharing risk alleles with total intracranial volume (ICV), gray matter volumes (GMVs) of subcortical structures, cortical surface area (SA), and average cortical thickness (TH) were tested. RESULTS Eighteen SNPs within CACNA1C were nominally associated with more than one neuropsychiatric disorder (P < .05); the associations shared among schizophrenia, bipolar disorder, and alcohol use disorder survived false discovery rate correction (five SNPs with P < 7.3 × 10-4 and q < 0.05). CACNA1C mRNA was differentially expressed in brains from individuals with schizophrenia, bipolar disorder, and Parkinson's disease, relative to controls (three SNPs with P < .01). Risk alleles shared by schizophrenia, bipolar disorder, substance dependence, and Parkinson's disease were significantly associated with ICV, GMVs, SA, or TH (one SNP with P ≤ 7.1 × 10-3 and q < 0.05). CONCLUSION Integrating multiple levels of analyses, we identified CACNA1C variants associated with multiple psychiatric disorders, and schizophrenia and bipolar disorder were most strongly implicated. CACNA1C variants may contribute to shared risk and pathophysiology in these conditions.

Objectives Numerous genome-wide association studies have identified CACNA1C as one of the top risk genes for schizophrenia. As a necessary post-genome-wide association study (GWAS) follow-up, here, we focused on this risk gene, carefully investigated its novel risk variants for schizophrenia, and explored their potential functions. Methods We analyzed four independent samples (including three European and one African-American) comprising 5648 cases and 6936 healthy subjects to identify replicable single nucleotide polymorphism-schizophrenia associations. The potential regulatory effects of schizophrenia-risk alleles on CACNA1C mRNA expression in 16 brain regions (n = 348), gray matter volumes (GMVs) of five subcortical structures (n = 34 431), and surface areas and thickness of 34 cortical regions (n = 36 936) were also examined. Results A novel 17-variant block across introns 36-45 of CACNA1C was significantly associated with schizophrenia in the same effect direction across at least two independent samples (1.8 x 10(-4) <= P <= 0.049). Most risk variants within this block showed significant associations with CACNA1C mRNA expression (1.6 x 10(-3) <= P <= 0.050), GMVs of subcortical structures (0.016 <= P <= 0.048), cortical surface areas (0.010 <= P <= 0.050), and thickness (0.004 <= P <= 0.050) in multiple brain regions. Conclusion We have identified a novel and functional risk variant block at CACNA1C for schizophrenia, providing further evidence for the important role of this gene in the pathogenesis of schizophrenia. Psychiatr Genet 33: 182-190 Copyright (c) 2023 Wolters Kluwer Health, Inc. All rights reserved.

ObjectiveTo observe the effect of the enhanced external counterpulsation （EECP） combined with drug on social function and efficacy in patients with depressive episodes， so as to provide references for the treatment of depressive episodes.MethodsA total of 66 hospitalized patients who was in hospital at department of psychiatry of the Second Xiangya Hospital of Central South University， met the criteria of Diagnostic and Statistical Manual of Mental Disorders，fifth edition（DSM-5） diagnosis of depressive episode or bipolar disorder depressive episode from May 2019 to March 2020 were included by simple random sampling. The participants were divided into study group （n=36） and control group （n=30） according to the random number table method. Both groups received conventional drug treatment， and the study group recieved the EECP intervention at same time. The Depression symptoms and social function were assessed before and after treatment by using Hamilton Depression Scale-24 item （HAMD-24） and Sheehan Disability Scale （SDS）. Treatment efficacy of the two groups was compared.ResultsAfter the intervention， the HAMD-24 and SDS scores in both groups were lower than those before treatment， the differences were statistically significant （t=8.149， 5.791， 8.016， 3.488， P˂0.01）. And the SDS score of the study group was siginficantly lower than that of the control group （t=-3.008， P<0.05）. The total effective rate of treatment in the study group was higher than that of the control group， and the difference was statistically significant （90.63% vs. 63.33%， χ²=8.725， P˂0.05）.ConclusionEECP therapy combined with drug has better efficacy on the patients with depressive episodes， and it can improve social function effectively.

Objective: Deep learning algorithms were used to explore the relationship between extrinsic bodily and facial expressions and intrinsic psychological assimilation of a depressed patient during the course of cognitive behavior therapy. Methods: The video data of the whole course of 14 treatments of a patient with depressive disorder were collected.Deep learning algorithms were used to analyze the patient’s expressions and movements, and meanwhile Assimilation Analysis were used to analysis the textual data transcribed from the audio. Results: The reduction rate of the patient’s HAMD and HAMA were 90.91% and 91.43%, in respective. The expression entropy increased from the beginning to the end of therapy. The expression components changed from mainly sad expressions to various expressions, as the treatment progresses. The movement range gradually increased. The results of analysis based on textual data showed that the volume of speech gradually increased, and the patient’s awareness and integration ability of their own problems gradually improved. Conclusion: The changes in the facial and bodily expressions of depression based on deep learning algorithms are consistent with the changes in their internal psychological assimilation. Deep learning algorithm to facial expression recognition and posture estimation of patients with depression is feasible.

INTRODUCTION:At rest, the brain's higher cognitive systems engage in correlated activity patterns, forming networks. With mild cognitive impairment (MCI), it is essential to understand how functional connectivity within and between resting-state networks changes. This study used resting-state functional connectivity to identify significant differences within and between the cingulo-opercular network (CON) and default mode network (DMN).METHODS:We assessed cognitive function in patients using the Chinese version of the Alzheimer's disease assessment scale-Cognitive subscale (ADAS-Cog). A group of MCI subjects (ages 60-83 years, n = 45) was compared to age-matched healthy controls (n = 70). Resting-state functional connectivity was used to determine functional connectivity strength within and between the CON and DMN.RESULTS:Compared to healthy controls, the MCI group showed significantly lower functional connectivity within the CON (F = 10.76, df = 1, p = 0.001, FDR adjusted p = 0.003). Additionally, the MCI group displayed no distinct differences in functional connectivity within DMN (F = 0.162, df = 1, p = 0.688, FDR adjusted p = 0.688) and between CON and DMN (F = 2.270, df = 1, p = 0.135, FDR adjusted p = 0.262). Moreover, we found no correlation between ADAS-Cog and within- or between-connectivity metrics among subjects with MCI.CONCLUSIONS:Our findings indicate that specific patterns of hypoconnectivity within CON circuitry may characterize MCI relative to healthy controls. This work improves our understanding of network dysfunction underlying MCI and could inform more targeted treatment.

Attention deficit hyperactivity disorder (ADHD) is associated with reduction of cortical and subcortical gray matter volumes (GMVs). The kinectin 1 gene ( KTN1 ) has recently been reported to significantly regulate GMVs and ADHD risk. In this study, we aimed to identify sex-specific, replicable risk KTN1 alleles for ADHD and to explore their regulatory effects on mRNA expression and cortical and subcortical GMVs. We examined a total of 1020 KTN1 SNPs in one discovery sample (ABCD cohort: 5573 males and 5082 females) and three independent replication European samples (Samples #1 and #2 each with 802/122 and 472/141 male/female offspring with ADHD; and Sample #3 with 14,154/4945 ADHD and 17,948/16,246 healthy males/females) to identify replicable associations within each sex. We examined the regulatory effects of ADHD-risk alleles on the KTN1 mRNA expression in two European brain cohorts (n = 348), total intracranial volume (TIV) in 46 European cohorts (n = 18,713) and the ABCD cohort, as well as the GMVs of seven subcortical structures in 50 European cohorts (n = 38,258) and of 118 cortical and subcortical regions in the ABCD cohort. We found that four KTN1 variants significantly regulated the risk of ADHD with the same direction of effect in males across discovery and replication samples (0.003 ≤ p ≤ 0.041), but none in females. All four ADHD-risk alleles significantly decreased KTN1 mRNA expression in all brain regions examined (1.2 × 10 –5 ≤ p ≤ 0.039). The ADHD-risk alleles significantly increased basal ganglia (2.8 × 10 –22 ≤ p ≤ 0.040) and hippocampus (p = 0.010) GMVs but reduced amygdala GMV (p = 0.030) and TIV (0.010 < p ≤ 0.013). The ADHD-risk alleles also significantly reduced some cortical (right superior temporal pole, right rectus) and cerebellar but increased other cortical (0.007 ≤ p ≤ 0.050) GMVs. To conclude, we identified a set of replicable and functional risk KTN1 alleles for ADHD, specifically in males. KTN1 may play a critical role in the pathogenesis of ADHD, and the reduction of specific cortical and subcortical, including amygdalar but not basal ganglia or hippocampal, GMVs may serve as a neural marker of the genetic effects.

Introduction Real-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD. Methods We proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients’ facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients. Results We found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales. Discussion The BDD can effectively measure the current state of patients’ depression and its changing trend according to the patient’s expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.

Abstract Background The status of depression, anxiety and related factors in Chinese firstborn and second-born children have hitherto not been examined and remains unknown. With the changing of China’s fertility policy, it is of greater significance to know about children’s health growth. Methods A convenience sampling and cross-sectional survey method was used. Data was collected from school-based sample in one middle and one primary school located in Xinxiang City, Henan province in China during the period February 18th to March 22nd 2019. There was a total of 653 first-born and 772 second-born children in the surveyed data pool. Depression self-rating scale for children (DSRSC), Screen for Child Anxiety Related Emotional Disorders (SCARED), Perceived parental rearing patterns scale, Chinese Screen Questionnaire of Child Abuse were used. Results Second-born child was in higher risk to suffer from anxiety when compared with first-born child(P < 0.05). The depression score of female was significantly higher compared to males in the second-born child (P < 0.05). The anxiety score of female was significantly higher compared to males in both the firstborn and second-born children (Ps < 0.05 − 0.01), while the anxiety score of female was significantly higher in the second-born compared to whom in firstborn child (P < 0.05). People who with elder female siblings were of lower depression and anxiety scores than other with male siblings(Ps < 0.05 − 0.01). Age gap was weakly negatively correlated with the depression scores of the first-born children(P < 0.05). The mean scores of severe punishment, excessive interference and overprotection from father were higher in males in both firstborn and second-born children (Ps < 0.001), rejection/denial in firstborn from father were higher in males in second-born child. The mean scores of over interference and over protection from mother were also higher in males in both firstborn and second-born children (Ps < 0.05 − 0.001). Conclusions The second-born children are more likely to suffer from depression and anxiety, especially in female. The related factors including the age gap, the gender of elder siblings, the parenting styles and the child abuse. It suggested that under China’s fertility policy, more energy needs to be devoted to maintaining children's mental health.

Previous genome-wide association studies (GWAS) reported that the allele C of rs945270 of the kinectin 1 gene ( KTN1 ) most significantly increased the gray matter volume (GMV) of the putamen and modestly regulated the risk for attention deficit hyperactivity disorder (ADHD). On the other hand, ADHD is known to be associated with a reduction in subcortical and cortical GMVs. Here, we examined the interrelationships of the GMVs, rs945270 alleles, and ADHD symptom scores in the same cohort of children. With data of rs945270 genotypes, GMVs of 118 brain regions, and ADHD symptom scores of 3372 boys and 3129 girls of the Adolescent Brain Cognition Development project, we employed linear regression analyses to examine the pairwise correlations adjusted for the third of the three traits and other relevant covariates, and examine their mediation effects. We found that the major allele C of rs945270 modestly increased risk for ADHD in males only when controlling for the confounding effects of the GMV of any one of the 118 cerebral regions (0.026 ≤ p ≤ 0.059: Top two: left and right putamen). This allele also significantly increased putamen GMV in males alone (left p = 2.8 × 10 −5 , and right p = 9.4 × 10 −5 ; α = 2.1 × 10 −4 ) and modestly increased other subcortical and cortical GMVs in both sexes ( α < p < 0.05), whether or not adjusted for ADHD symptom scores. Both subcortical and cortical GMVs were significantly or suggestively reduced in ADHD when adjusted for rs945270 alleles, each more significantly in females (3.6 × 10 −7 ≤ p < α ; Top two: left pallidum and putamen) and males (3.5 × 10 −6 ≤ p < α ), respectively. Finally, the left and right putamen GMVs reduced 14.0% and 11.7% of the risk effects of allele C on ADHD, and allele C strengthened 4.5% (left) and 12.2% (right) of the protective effects of putamen GMVs on ADHD risk, respectively. We concluded that the rs945270-GMVs-ADHD relationships were sex-different. In males, the major allele C of rs945270 increased risk for ADHD, which was compromised by putamen GMVs; this allele also but only significantly increased putamen GMVs that then significantly protected against ADHD risk. In females, the top two GMVs significantly decreasing ADHD risk were left pallidum and putamen GMVs. Basal ganglia the left putamen in particular play the most critical role in the pathogenesis of ADHD.

Objectives: Genome-wide association studies have identified a significant risk gene, CACNA1C, for schizophrenia. In this study, we comprehensively investigated a large set of CACNA1C single-nucleotide polymorphisms (SNPs) to identify the replicable risk alleles for schizophrenia and explore their biological functions. Methods: One Jewish (1044 cases vs 2052 controls), one European (1350 cases vs 1378 controls) and one exploratory African American samples (98 cases vs 20 controls) were analyzed to identify replicable single-nucleotide polymorphism–schizophrenia associations. The regulatory effects of risk alleles on CACNA1C messenger RNA expression were examined. The most robust risk tagSNP (rs1006737) was meta-analyzed on 17 studies (74,122 cases vs 109,062 controls), and associated with the gray matter volumes of seven subcortical structures in 38,258 Europeans, and the surface areas and thickness of 34 cortical regions in 33,992 Europeans and 2944 non-Europeans. Results: Forty-seven replicable risk single-nucleotide polymorphisms, including a 20-single-nucleotide polymorphism haplotype block, were identified in our samples (1.8 × 10 −4 ⩽ p ⩽ 0.049). This variant block was consistently associated with schizophrenia across four independent Psychiatric Genomics Consortium cohorts (79,645 cases vs 109,590 controls; 2.5 × 10 –17 ⩽ p ⩽ 0.017). This block showed significant expression quantitative trait loci in three independent European brain cohorts (5.1 × 10 –12 ⩽ p ⩽ 8.3 × 10 –3 ) and could be tagged by the most significant risk single-nucleotide polymorphism rs1006737. The minor allele A of rs1006737 significantly increased risk for schizophrenia across the Jewish and European samples ( p = 0.029 and 0.004, respectively), and this association was highly significant in the meta-analysis ( p = 1.62 × 10 –42 ). This allele also significantly altered the CACNA1C messenger RNA expression in five brain regions (5.1 × 10 –12 ⩽ p ⩽ 0.05), decreased the gray matter volume of thalamus ( p = 0.010), the surface area of isthmus cingulate cortex ( p = 0.013) and the thickness of transverse temporal and superior temporal sulcus cortexes (0.005 ⩽ p ⩽ 0.043). Conclusion: We identified an independent, replicable, functional, and significant risk variant block at CACNA1C for schizophrenia, which could be tagged by the most robust risk marker rs1006737, suggesting an important role of CACNA1C in the pathogenesis of schizophrenia.

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

Blood pressure (BP) assessment and dynamic detection are of great significance for timely detection of the morbidity of hypertension, which is a major risk factor for most cardiovascular diseases (CVDs). It has been proved that the dynamic BP can be effectively predicted by using the combined input of photoplethysmogram (PPG) and electrocardiographic (ECG) signals. In this paper, we proposed a hybrid neural network architecture, which contains CNN-Sequential-Adapt layer, ResNet25_BP layer with squeeze and excitation (SE) block and fully connected layers, for BP estimation. The structure based on the convolutional network aims at the current inputs, which can effectively absorb the graph information of the inputted biological signals and make the model more stable and reliable. We evaluated the performance of two datasets including 1216 and 40 subjects, based on the criterions of British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). According to the BHS and AAMI standards, the outputs of the model achieved grade A on BHS and met the AAMI criteria. The mean absolute errors (MAE) of systolic BP and diastolic BP are 3.70 and 2.81 mmHg in the large dataset, and 1.37 and 0.93 mmHg in the small dataset, respectively.

In psychiatry, observation of the patients is often an important basis for making a diagnosis during clinical practice. However, changes in emotional facial expressions are often subtle and difficult to detect. For this reason, automated facial expression recognition can be used to assist in identifying mental disorders. Facial expression is one of the important ways of emotional expression, and strong similarities of basic human facial expression are not affected by cultural background or congenital blindness. With the development of computer science, facial expression recognition methods are also constantly improving. Among them, deep-learning-based facial expression recognition approaches, with their powerful information processing capabilities, highly reduce the dependence on face-physics-based models and other pre-processing techniques by using trainable feature extraction models to automatically learn representations from images and videos. This article focuses on the progress of facial expression recognition system in the diagnosis and treatment of schizophrenia, depression, borderline personality disorder, autism spectrum disorder and other diseases. This article also explores the application of facial expression recognition technology in the field of psychiatry and remote psychology intervention.

OBJECTIVE:To compare the clinical efficacy of penicillin and ceftriaxone sodium in the treatment of neurosyphilis with psychiatric symptoms.METHODS:50 neurosyphilis with mental symptoms patients were randomly divided into penicillin group (4 million units, Q4h) and ceftriaxone sodium group (1 g, Q12h). The total treatment time was 14 and 15 days respectively.The activity of daily living scale (ADL), brief psychiatric rating scale (BPRS) and mini-mental state examination (MMSE) were scored as the measurement of efficiency in living ability, mental symptoms and cognitive function.RESULT:There were no significant differences in ADL, MMSE and BPRS between the penicillin group and the ceftriaxone sodium treatment group (p > 0.05). After treatment, the score of BPRS and ADL decreased from baseline, while MMSE scores increased from baseline, having a main time effect (F=31.098,F=26.342,F= 79.916; p < 0.05).CONCLUSION:Penicillin or ceftriaxone sodium are both effective in the aspect of mental symptoms, cognitive function and life ability among neurosyphilis with psychiatric symptoms patients.

分离性身份障碍(dissociative identity disorder,DID)曾称多重人格障碍,其特点是至少有两个且相对持久的身份或互不联系的人格出现并交替控制个体行为,同时伴随对重要事件的记忆障碍[1].DID患病率尚无系统数据,meta分析发现其横断面患病率约2％～5％[2].DID 症状复杂多样,常共病其他精神疾病,治疗难度大.本文报告1 例具有四重人格的DID患者,以期提高对该疾病诊治的认识.