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A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing has improved the sensitivity of structural variant discovery. Here, we conduct the long-read sequencing-based structural variant analysis for 405 unrelated Chinese individuals, with 68 phenotypic and clinical measurements. We discover a landscape of 132,312 nonredundant structural variants, of which 45.2% are novel. The identified structural variants are of high-quality, with an estimated false discovery rate of 3.2%. The concatenated length of all the structural variants is approximately 13.2% of the human reference genome. We annotate 1,929 loss-of-function structural variants affecting the coding sequence of 1,681 genes. We discover rare deletions in HBA1 / HBA2/HBB associated with anemia. Furthermore, we identify structural variants related to immunity which differentiate the northern and southern Chinese populations. Our study describes the landscape of structural variants in the Chinese population and their contribution to phenotypes and disease. Although many studies have characterized genetic variation in human populations, few have investigated structural variation and few have been in non-European populations. Here, the authors have performed long read sequencing on 405 Chinese individuals to identify structural variants and link them to phenotypes.

Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.

[...]the structural-functional connectivity (SC-FC) relationship is strengthened with age, which is consistent with the finding that significant correlations along intra-hemispheric tracts are observed between structural connectivity and functional connectivity in adults but not in children. [...]the maturation of some functional connections in the default-mode network precedes that of structural connectivity. Han Chinese participants aged 16 to 55 years who met the criteria of MDD as specified in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) were recruited from the Mental Health Center of West China Hospital of Sichuan University. The Benjamini–Hochberg procedure was used to control false discovery rates (FDRs) of multiple tests in comparing SC-FC coupling and correlation coefficients between patients with MDD and HCs. There was a statistically significant difference in correlation coefficients between SC-FC coupling of SMA_R and DMS_MCL_S in patients with MDD and HCs (P <0.01, q = 0.02) after correction for multiple testing [Table 1].

Rapid industrialization and urbanization in China have resulted in labor migrants leaving children behind. For left-behind children (LBC), disrupted parental attachment may increase the risk of psychiatric morbidity in adulthood. To investigate psychopathological consequences for university students who were LBC and to estimate the effects of one or both parents being migrants, the duration of left-behind experience, and parental absence during critical periods of growth on psychiatric morbidity. We conducted an annual survey of all freshmen at a Chinese university from 2014 to 2018. The questionnaire collected information on left-behind experiences and psychiatric morbidity using standardized self-report instruments. Regression coefficients derived from logistic regression were used to measure the associations among total time left behind, absence of one parent or both parents, age when left behind and psychopathological consequences. A total of 42,505 students were included. Students who were LBC had more psychopathology, including depression, anxiety, somatoform disorder, obsessive–compulsive disorder, self-reported suicide attempts and deliberate self-harm, than those who were not. Students for whom one or both parents were migrants showed a greater risk of psychiatric morbidity. The risk of psychiatric morbidity increased with the length of parental absence. Left-behind experience during childhood represents sustained impacts for university students into early adulthood. The higher prevalence of psychiatric morbidity in young adults who experienced the absence of one or both of their parents, especially in their early childhood, suggests that other factors besides attachment, such as protection from other risks, are important and that further research is necessary.

Abstract Recent neuroanatomical pattern recognition studies have shown some promises for developing an objective neuroimaging-based classification related to schizophrenia. This study explored the feasibility of reliably identifying schizophrenia using single and multimodal multivariate neuroimaging features. Multiple brain measures including regional gray matter (GM) volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) were extracted using fully automated procedures. We used Gradient Boosting Decision Tree to identify the most frequently selected features of each set of neuroanatomical metric and fused multimodal measures. The current classification model was trained and validated based on 98 patients with first-episode schizophrenia (FES) and 106 matched healthy controls (HCs). The classification model was trained and tested in an independent dataset of 54 patients with FES and 48 HCs using imaging data acquired on a different magnetic resonance imaging scanner. Using the most frequently selected features from fused structural and diffusion tensor imaging metrics, a classification accuracy of 75.05% was achieved, which was higher than accuracy derived from a single imaging metric. Most prominent discriminative features included cortical thickness of left transverse temporal gyrus and right parahippocampal gyrus, the FA of left corticospinal tract and right external capsule. In the independent cohort, average accuracy was 76.54%, derived from combined features selected from cortical thickness, gyrification, FA, and MD. These features characterized by GM abnormalities and white matter disruptions have discriminative power with respect to the underlying pathological changes in the brain of individuals having schizophrenia. Our results further highlight the potential advantage of multimodal data fusion for identifying schizophrenia.

Background Childhood trauma is strongly linked to anxiety and depression, significantly increasing the risk of negative outcomes in adulthood. This study employed network analysis to investigate the complex interplay of anxiety and depression symptoms among Chinese college students, focusing on identifying the core symptoms most directly affected by childhood trauma and those exerting the greatest influence on others. Methods Data were collected from December 2020 to January 2021 from 2,266 college students at 16 institutions in southwestern and eastern coastal China. Depression, anxiety, and childhood trauma were assessed using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Childhood Trauma Questionnaire-28, respectively. Separate symptom networks were constructed for participants with and without childhood trauma experiences. Central indices were employed to identify the central symptom within each network. The accuracy and stability of the networks were then evaluated. Finally, a network comparison test was used to analyze differences in network properties between the trauma and non-trauma groups. Results Loss of Energy and Worry too much were the central symptoms in the non-trauma group, while anhedonia and nervousness were the central symptoms in the trauma group. There was a significant difference in the global strength of the network between the trauma group and the non-trauma group ( p FDR < 0.01), but no significant difference in the distribution of edge weights between the two networks ( p FDR =0.14). Anhedonia, Suicide ideation and Feeling afraid in the trauma group showed increased network centrality compared with the non-trauma group. Conclusions This study demonstrates the profound impact of childhood trauma on the central symptoms of anxiety and depression in college students. Further research is warranted to investigate the specific pathways through which these symptoms develop, with the goal of developing targeted interventions for this vulnerable population. Clinical trial number Not applicable.

The risk of suicide in patients with major depressive disorder (MDD) poses a major concern, with studies suggesting that genetics may be a contributing factor. Although there are many transcriptomic studies on postmortem brain tissue related to suicidal behavior, the blood transcriptional mechanisms of suicidal ideation (SI) remain unknown. This study utilized a weighted gene coexpression network analysis (WGCNA) approach to investigate the associations between gene coexpression modules and SI in individuals with MDD using peripheral blood RNA-seq data from 75 MDD patients with SI (MDD_SI), 82 MDD patients without SI (MDD_nSI), and 149 healthy controls (HC). An ANCOVA was conducted to assess differences in gene coexpression modules among groups, with age and sex included as covariates. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases were used to annotate module functions. Results indicated that the magenta module (associated with RNA splicing processes) differentiated MDD_SI from MDD_nSI ( p  = 0.021), while the green module (related to immune and inflammatory responses) distinguished MDD_SI from HC ( p  = 0.004). Additionally, three modules showed differences between MDD_nSI and HC: magenta ( p  = 0.009), brown (related to innate immunity and mitochondrial metabolism; p  = 0.001), and turquoise (associated with energy metabolism and neurodegeneration; p  = 0.005). Our findings highlight that gene expression regulation, immune response, and inflammation may be linked to SI in patients with MDD, while pathways associated with innate immunity, energy metabolism, mitochondrial function, and neurodegeneration appear to be more broadly related to MDD.

Background Neurocognitive impairment is one of the prominent manifestations of major depressive disorder (MDD). Childhood trauma enhances vulnerability to developing MDD and contributes to neurocognitive dysfunctions. However, the distinct impacts of different types of childhood trauma on neurocognitive processes in MDD remain unclear. Methods This study comprised 186 individuals diagnosed with MDD and 268 healthy controls. Childhood trauma was evaluated using the 28-item Childhood Trauma Questionnaire-Short Form. Neurocognitive abilities, encompassing sustained attention, vigilance, visual memory, and executive functioning, were measured by the Cambridge Neuropsychological Testing Automated Battery. Results Multivariable linear regressions revealed that childhood trauma and MDD diagnosis were independently associated with neurocognitive impairment. Physical neglect was associated with impaired visual memory and working memory. MDD diagnosis is associated with working memory and planning. Interactive analysis revealed that physical/sexual abuse was associated with a high level of vigilance and that emotional neglect was linked with better performance on cognitive flexibility in MDD patients. Furthermore, childhood emotional abuse, physical abuse, and emotional neglect were revealed to be risk factors for developing early-onset, chronic depressive episodes. Conclusion Thus, specific associations between various childhood traumas and cognitive development in depression are complex phenomena that need further study.

This study was to explore the sex differences in clinical characteristics and brain gray matter volume (GMV) alterations in 29 male patients with major depressive disorder (MDDm), 53 female patients with MDD (MDDf), and in 29 male and 53 female matched healthy controls. Maps of GMV were constructed using magnetic resonance imaging data and compared between groups. We evaluated clinical symptoms using the Hamilton Rating Scale for Depression and obtained a total score and five syndrome scores. A two-factor ANCOVA model was specified using SPM8, with sex and diagnosis as the between-subject factors. We found that: (1) significant GMV increase in the left cerebellum and GMV reduction in the bilateral middle temporal gyrus and left ventral medial prefrontal gyrus occurred selectively in male patients, while the GMV reduction in the left lingual gyrus and dorsal medial prefrontal gyrus occurred selectively in female patients; (2) MDDf may have experienced more severe sleep disturbance than MDDm; and (3) the severity of sleep symptom could be predicted by the sex specific brain structural alterations in depressions. These findings suggest that sex specific anatomical alterations existed in MDD, and these alterations were associated with the clinical symptoms.

Animal feed (including forage and silage) can be contaminated with mycotoxins. Here, 200 maize silage samples from around China were collected in 2019 and analyzed for regulated mycotoxins, masked mycotoxins (deoxynivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol, and deoxynivalenol-3-glucoside), and emerging mycotoxins (beauvericin, enniatins, moniliformin, and alternariol). Deoxynivalenol and zearalenone were detected in 99.5% and 79.5% of the samples, respectively. Other regulated mycotoxins were detected in fewer samples. The highest deoxynivalenol and zearalenone concentrations were 3600 and 830 μg/kg, respectively. The most commonly detected masked mycotoxin was 15-acetyldeoxynivalenol, which was detected in 68.5% of the samples and had median and maximum concentrations of 61.3 and 410 μg/kg, respectively. The emerging mycotoxins beauvericin, alternariol, enniatin A, enniatin B1, and moniliformin were detected in 99.5%, 85%, 80.5%, 72.5%, and 44.5%, respectively, of the samples but at low concentrations (medians <25 μg/kg). The samples tended to contain multiple mycotoxins, e.g., the correlation coefficients for the relationships between the concentrations of beauvericin and deoxynivalenol, deoxynivalenol and zearalenone, and zearalenone and beauvericin were 1.0, 0.995, and 0.995, respectively. The results indicated that there needs to be more awareness of the presence of one or more masked and emerging mycotoxins in maize silage in China.