The impact of land use on eco-environment in the Dianchi Basin.

(1) Studying the dynamic correlation between land use and the eco-environment in the Dianchi Basin is important for improving the basin's spatial layout and enhancing ecological development and conservation; (2) Through dynamic analysis and comprehensive evaluation of land use, the introduction of ecological and environmental quality index, and the use of FLUS models, the impacts on eco-environments in the Dianchi Basin for the recent 20 years were analyzed; (3) The past two decades witnessed a constant increase in the construction land in the Dianchi Basin and a decline in the farmland at an average annual rate of 0.93 %; The utilization level of land in the Dianchi Basin presented a negative correlation with the quality of the area's eco-environment, which reduces first and then increases; When natural production becomes a priority, both the construction land and farmland have witnessed growth. However, when ecological protection becomes a priority, it is projected that by 2035, the Dianchi Basin will achieve its highest eco-environmental quality index; (4) Studying how the change of land use types affects eco-environment is crucial for optimizing the current allocation of land resources and promoting sustainable development in the basin.

Evaluating and diagnosing ecosystem health of the "three-lake" watershed in Yuxi, Yunnan, China from 2010 to 2020 by PSR-KDE.

To carry out the diagnosis and evaluation of the ecosystem health in Yuxi three-lake watershed, this paper presents the changing trend of its health state, and predicts the future development. This also provides ideas for maintaining the regional ecosystem health, and then gradually improves the ecological environment quality. Taking Fuxian Lake, Qilu Lake and Xingyun Lake (the three-lake watershed) in Yuxi City, Yunnan Province, Southwest China as the research object, a model combining pressure-state-response and kernel density estimation (PSR-KDE) adopts to diagnose and evaluate the ecosystem health of the "three lake" watershed from 2010 to 2020, and the distribution map of ecosystem health index has obtained by the evaluation indexes integration based on GIS spatial analysis. Hence, the evaluation results have visualized on the map. The results show that: The distribution of ecosystem health index in the study area was 0.1530-0.7045 in 2010, 0.2056-0.7512 in 2015, and 0.2248-0.7662 in 2020. 0.12% was in the pathological area in 2010. After 2015, the pathological condition of ecosystem health has completely solved, and the proportion of unhealthy ecosystems was 11.95% in 2010, 7.38% in 2015, and 5.97% in 2020. The ecosystem health index of the study region was 0.5523 in 2010, 0.5807 in 2015, and 0.5815 in 2020, it indicates that the ecosystem was in a sub-health state. From 2010 to 2020, the ecosystem health around Qilu Lake was the most worrying, followed by the northwest of Fuxian Lake and the northern and southern regions of Xingyun Lake. The ecosystem health of the three-lake watershed showed significant improvement from 2010 to 2020. The study ecosystem health assessment and early warning in the three-lake watershed is significant to the ecological environment protection and management of the plateau lake basin, the restoration of the territorial space ecology and the economic development of the surrounding area.

Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms.

To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Organic Materials Promote Rhododendron simsii Growth and Rhizosphere Soil Properties in a Lead-Zinc Mining Wasteland.

The mining of metal minerals generates considerable mining wasteland areas, which are characterized by poor soil properties that hinder plant growth. In this study, a field plot experiment was carried out in the mining wasteland of the Lanping lead-zinc mine in Yunnan Province to study the effects of applying three organic materials-biochar (B), organic fertilizer (OF), and sludge (S)-at concentrations of 1% (mass fraction), on promoting the soil of mining wasteland and the growth of two plant varieties (Huolieniao and Yingshanhong). The results showed that the amount of available nutrients in the surface soil of a mining wasteland could be considerably increased by S and OF compared to the control check (CK). In the rhizosphere soils of two Rhododendron simsii varieties, the application of S increased the available phosphorus (P) content by 66.4% to 108.8% and the alkali-hydrolyzed nitrogen (N) content by 61.7% to 295.5%. However, the contents of available cadmium (Cd) and available lead (Pb) were reduced by 17.1% to 32.0% and 14.8% to 19.0%, respectively. Moreover, three organic materials increased the photosynthetic rate and biomass of two R. simsii varieties. Specifically, OF and S were found to significantly increase the biomass of R. simsii. Organic materials have direct impacts on the increased plant height and biomass of R. simsii. Additionally, organic materials indirectly contribute to the growth of R. simsii by reducing the content of available Cd and available Pb in rhizosphere soil while increasing the content of available nutrients according to the structural equation model (SEM). Overall, S can stabilize Cd and Pb, increase soil nutrient contents, and promote the growth of R. simsii effectively, and has great potential in the vegetation reconstruction of mining wasteland.

The association between urbanization and depression in the elderly: A network analysis from the complexity science perspective.

IMPORTANCE: The global increase in urbanization has coincided with a rise in depression prevalence. However, the effect of urbanization on depression remains controversial, especially for the elderly. OBJECTIVE: To clarify how urbanization impacts depression in the elderly from a network perspective. DESIGN, SETTING, AND PARTICIPANTS: This sectional cohort study used data from China Health and Retirement Longitudinal Study (CHARLS). MAIN OUTCOMES AND MEASURES: The occurrence of depressive symptoms in urban and rural elderly residents. Network metrics of depressive symptoms. RESULTS: Of the 13,993 participants, lower incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 24.7 %-27.8 %) than in rural (40.4 %, 95 % CI, 39.5 %-41.3 %, P < 0.0001) residents. However, higher incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 25.2 %-28.4 %) than in rural (17.5 %, 95 % CI, 16.1 %-18.9 %, P < 0.0001) residents in a subset of 2898 pairs of participants after PSM. No difference in the network structure and metrics between urban and rural residents before (M = 0.071, p = 0.054, S = 0.037, p = 0.80) and after (M = 0.085, p = 0.133, S = 0.086, p = 0.47) PSM was detected. The networks structure revealed that negative affect was strongly connected to somatic symptoms and that the two anhedonic symptoms were also strongly connected. CONCLUSIONS: The current study indicated the homogeneity of the ultimate nature of depression between rural and urban residents from the network perspective, supporting the viewpoint that urbanization might not impose influence on depression. Further researches delving deeper into the complexity of the issue may provide new insights into our understanding of depression in an urban environment among the elderly.

Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement.

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Follow-up study on ThinPrep cytology test-positive patients in tropical regions.

BACKGROUND: As shown in the statistics from the World Health Organization, it is estimated that approximately 75000 new cases of cervical cancer occur every year in China. In 2008, 33000 people died of cervical cancer in China. It is proven that most women are at risk of cervical cancer. The progression from human papillomavirus (HPV) infection to cervical cancer can be several years or decades, which offers a unique opportunity to prevent cancer. AIM: To observe the changes in ThinPrep cytology tests (TCT) and HPV infection in patients who were detected to be positive via TCT screening of cervical cancer and further explore the biopsy results. METHODS: This paper performed a follow-up study on 206 cervical cancer screening-positive patients of 12231 total cases from our previous research. We conducted an observational study on the TCT results based on the interpretation of The Bethesda System. RESULTS: Over a 5-year period, 10 cases received consistent follow-up. The proportions of cases in which glandular epithelial lesions were detected increased over the follow-up period. The differences between the years were statistically significant (P < 0.01). Over the 5 years, the proportion of patients whose squamous epithelial lesions transformed into glandular epithelial lesions increased yearly. Annual positive rates of HPV infection were: year 1, 73% (24/33); year 2, 43% (6/14); year 3, 36% (9/25); year 4, 50% (9/18); and year 5, 25% (6/24). The positive detection rate after biopsy over a 9-year period was 29%. CONCLUSION: The follow-up study for 5 years to 9 years revealed a tendency to change from squamous epithelial lesions to glandular epithelial lesions and an improvement of the disease (which had not been reported previously). The HPV test indicated a high negative conversion ratio of the viral infection. However, the follow-up cases were not found to have persistent infection of high-risk HPV. Therefore, early intervention of cervical cancer screening is necessary. Low re-examination compliance, patient education, and preventive measures should be enhanced.

Tract-specific white matter microstructural alterations in subjects with schizophrenia and unaffected first-degree relatives.

White matter tracts alterations have been reported in schizophrenia (SZ), but whether such abnormalities are associated with the effects of the disorder itself and/or genetic vulnerability remains unclear. Moreover, the specific patterns of different parts of these altered tracts have been less well studied. Thus, diffusion-weighted images were acquired from 38 healthy controls (HCs), 48 schizophrenia patients, and 33 unaffected first-degree relatives of SZs (FDRs). Diffusion properties of the 25 major tracts automatically extracted with probabilistic tractography were calculated and compared among groups. Regarding the peripheral regions of the tracts, significantly higher diffusivity values in the left superior longitudinal fasciculus (SLF) and the left anterior thalamic radiation (ATR) were observed in SZs than in HCs and unaffected FDRs. However, there were no significant differences between HCs and FDRs in these two tracts. While no main effects of group with respect to the core regions of the 25 tracts survived multiple comparisons correction, FDRs had significantly higher diffusivity values in the left medial lemniscus and lower diffusivity values in the middle cerebellar peduncle than HCs and SZs. These findings enhance the understanding of the abnormal patterns in the peripheral and core regions of the tracts in SZs and those at high genetic risk for schizophrenia. Our results suggest that alterations in the peripheral regions of the left SLF and ATR are features of established illness rather than genetic predisposition, which may serve as critical neural substrates for the psychopathology of schizophrenia.

Stressful events induce long-term gut microbiota dysbiosis and associated post-traumatic stress symptoms in healthcare workers fighting against COVID-19.

OBJECTIVE: The microbiota-gut-brain axis is a key pathway perturbed by prolonged stressors to produce brain and behavioral disorders. Frontline healthcare workers (FHWs) fighting against COVID-19 typically experience stressful event sequences and manifest some mental symptoms; however, the role of gut microbiota in such stress-induced mental problems remains unclear. We investigated the association between the psychological stress of FHW and gut microbiota. METHODS: We used full-length 16S rRNA gene sequencing to characterize the longitudinal changes in gut microbiota and investigated the impact of microbial changes on FHWs' mental status. RESULTS: Stressful events induced significant depression, anxiety, and stress in FHWs and disrupted the gut microbiome; gut dysbiosis persisted for at least half a year. Different microbes followed discrete trajectories during the half-year of follow-up. Microbes associated with mental health were mainly Faecalibacterium spp. and [Eubacterium] eligens group spp. with anti-inflammatory effects. Of note, the prediction model indicated that low abundance of [Eubacterium] hallii group uncultured bacterium and high abundance of Bacteroides eggerthii at Day 0 (immediately after the two-month frontline work) were significant determinants of the reappearance of post-traumatic stress symptoms in FHWs. LIMITATIONS: The lack of metabolomic evidence and animal experiments result in the unclear mechanism of gut dysbiosis-related stress symptoms. CONCLUSION: The stressful event sequences of fighting against COVID-19 induce characteristic longitudinal changes in gut microbiota, which underlies dynamic mental state changes.

Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks.

BACKGROUND: Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia. AIMS: To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers. METHOD: We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites. RESULTS: We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19-85.74%; sensitivity, 75.31-89.29% and area under the receiver operating characteristic curve, 0.797-0.909. CONCLUSIONS: These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Systems biological assessment of altered cytokine responses to bacteria and fungi reveals impaired immune functionality in schizophrenia.

Evidence suggests that complex interactions between the immune system and brain have important etiological and therapeutic implications in schizophrenia. However, the detailed cellular and molecular basis of immune dysfunction in schizophrenia remains poorly characterized. To better understand the immune changes and molecular pathways, we systemically compared the cytokine responses of peripheral blood mononuclear cells (PBMCs) derived from patients with schizophrenia and controls against bacterial, fungal, and purified microbial ligands, and identified aberrant cytokine response patterns to various pathogens, as well as reduced cytokine production after stimulation with muramyl dipeptide (MDP) in schizophrenia. Subsequently, we performed single-cell RNA sequencing on unstimulated and stimulated PBMCs from patients and controls and revealed widespread suppression of antiviral and inflammatory programs as well as impaired chemokine/cytokine-receptor interaction networks in various immune cell subpopulations of schizophrenic patients after MDP stimulation. Moreover, serum MDP levels were elevated in these patients and correlated with the course of the disease, suggesting increased bacterial translocation along with disease progression. In vitro assays revealed that MDP pretreatment altered the functional response of normal PBMCs to its re-stimulation, which partially recapitulated the impaired immune function in schizophrenia. In conclusion, we delineated the molecular and cellular landscape of impaired immune function in schizophrenia, and proposed a mutual interplay between innate immune impairment, reduced pathogen clearance, increased MDP translocation along schizophrenia development, and blunted innate immune response. These findings provide new insights into the pathogenic mechanisms that drive systemic immune activation, neuroinflammation, and brain abnormalities in schizophrenia.

Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score.

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation.

We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence fields from semantic matching provide supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the efficacy of our approach on five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks.

A neuroimaging biomarker for striatal dysfunction in schizophrenia.

Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia1-5. We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners (n = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders.

Metagenome-wide association of gut microbiome features for schizophrenia.

Evidence is mounting that the gut-brain axis plays an important role in mental diseases fueling mechanistic investigations to provide a basis for future targeted interventions. However, shotgun metagenomic data from treatment-naïve patients are scarce hampering comprehensive analyses of the complex interaction between the gut microbiota and the brain. Here we explore the fecal microbiome based on 90 medication-free schizophrenia patients and 81 controls and identify a microbial species classifier distinguishing patients from controls with an area under the receiver operating characteristic curve (AUC) of 0.896, and replicate the microbiome-based disease classifier in 45 patients and 45 controls (AUC = 0.765). Functional potentials associated with schizophrenia include differences in short-chain fatty acids synthesis, tryptophan metabolism, and synthesis/degradation of neurotransmitters. Transplantation of a schizophrenia-enriched bacterium, Streptococcus vestibularis, appear to induces deficits in social behaviors, and alters neurotransmitter levels in peripheral tissues in recipient mice. Our findings provide new leads for further investigations in cohort studies and animal models.

Three patients with 46,X,inv(Y)(p11.2q11.2)pat/45,X and their pedigree analysis.

The present study aimed to perform chromosome examination and pedigree analysis on three patients with semen abnormality who had undergone in vitro fertilization-embryo transfer (IVF-ET). Peripheral blood cell culture and chromosome karyotyping were performed on 4,200 individuals who had undergone chromosome examination. Among them, 155 pregnant women who had successfully conceived were subjected to amniotic cell culture and chromosome karyotyping and those with abnormal chromosome karyotype were further subjected to C-banding and whole-genome sequencing. Mosaicism for a 46,X,inv(Y)(p11.2q11.2)pat/45,X karyotype was identified in the probands and immediate adult male relatives. The incidence of this mosaicism in the study population was only 0.07% (3/4,200), which is reported for the first time. For the proband of pedigree A, the results of whole-genome sequencing and other tests were normal, and the chromosome karyotype of IVF fetuses was 46,X,inv(Y)(p11.2q11.2)pat. All the male members of three pedigrees have normal phenotypes, with no features of Turner's syndrome (45,X) or hermaphroditism (45,X/46,XY), suggesting that the inverted Y chromosome is extremely unstable and particularly susceptible to loss in somatic cells. So we speculate this karyotype may be a unique type of inverted Y chromosome in somatic cells.

Six families with balanced chromosome translocation associated with reproductive risks in Hainan Province: Case reports and review of the literature.

BACKGROUND: Balanced translocation refers to the process where breakage and reconnection of chromosomes occur at abnormal positions. As the genetic substance with balanced translocation in individuals does not change, which is usually characterized by normal phenotype and intelligence, the individuals seek medical service after many miscarriages, resulting in considerable mental and physical burdens of the family members. In the current era with rapid advances in detection technology, cytogenetic examination, as a definitive approach, still plays an essential role. CASE SUMMARY: We report six cases with balanced chromosome translocation: Case 1: 46,XY,t(3;12)(q27;q24.1), infertility after 3 years of marriage; Case 2: 46,XX,t(4;16)(q31;q12), small uterus and irregular menstruation; Case 3: 46,XY,t(4;5)(q33;q13),9qh+, not pregnant after arrested fetal development; Case 4: 46,XX,t(11;17)(q13;p11.2), not pregnant after two times of spontaneous abortion; Case 5: 46,XX,t(10;13)(q24;q21.2), not pregnant after arrested fetal development for once; Case 6: 46,XX,t(1;4)(p36.1;q31.1), not pregnant after arrested fetal development for two times. The first four cases had chromosomal aberration karyotypes. CONCLUSION: These results suggested that balanced chromosomal translocation carriers are associated with reproductive risks and a very high probability of abnormal pregnancy. The discovery of the first four reported chromosomal aberration karyotypes provides an important basis for studying the occurrence of genetic diseases.

Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity.

BACKGROUND: Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated. AIMS: To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD: Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS: The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS: These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.