AutoAMS: Automated attention-based multi-modal graph learning architecture search.

Multi-modal attention mechanisms have been successfully used in multi-modal graph learning for various tasks. However, existing attention-based multi-modal graph learning (AMGL) architectures heavily rely on manual design, requiring huge effort and expert experience. Meanwhile, graph neural architecture search (GNAS) has made great progress toward automatically designing graph-based learning architectures. However, it is challenging to directly adopt existing GNAS methods to search for better AMGL architectures because of the search spaces that only focus on designing graph neural network architectures and the search objective that ignores multi-modal interactive information between modalities and long-term content dependencies within different modalities. To address these issues, we propose an automated attention-based multi-modal graph learning architecture search (AutoAMS) framework, which can automatically design the optimal AMGL architectures for different multi-modal tasks. Specifically, we design an effective attention-based multi-modal (AM) search space consisting of four sub-spaces, which can jointly support the automatic search of multi-modal attention representation and other components of multi-modal graph learning architecture. In addition, a novel search objective based on an unsupervised multi-modal reconstruction loss and task-specific loss is introduced to search and train AMGL architectures. The search objective can extract the global features and capture multi-modal interactions from multiple modalities. The experimental results on multi-modal tasks show strong evidence that AutoAMS is capable of designing high-performance AMGL architectures.

Teleost Muc2 and Muc5ac: Key guardians of mucosal immunity in flounder (Paralichthys olivaceus).

Mucins secreted by mucous cells constitute a core part of the defense line against the invasion of pathogens. However, mucins' structure and immunological functions remain largely unknown in teleost fish. In this study, two typical mucins, Muc2 and Muc5ac of flounder (Paralichthys olivaceus), were cloned and their physicochemical properties, structure and conservation were analyzed. Notably, specific antibodies against flounder Muc2 and Muc5ac were developed. It was verified at the gene and protein level that Muc2 was expressed in the hindgut and gills but not in the skin, while Muc5ac was expressed in the skin and gills but not in the hindgut. After flounders were immunized by immersion with inactivated Edwardsiella tarda, Muc2 and Muc5ac were significantly upregulated at both the gene expression and protein levels, and Muc2+/Muc5ac+ mucous cells proliferated and increased secretion of Muc2 and Muc5ac. Moreover, Muc2 and Muc5ac exerted retention and clearance effects on E. tarda in a short period (within 1 dpi). These results revealed the characterization of fish mucins Muc2 and Muc5ac at the protein level and clarified the role of mucins as key guardians to maintain the mucus barrier, which advanced our understanding of teleost mucosal barrier.

Sclerostin Transduced Bone Marrow Mesenchymal Stem Cells Promote Fracture Healing in Rats Through the Wnt/ß-Catenin Signal Pathway.

The prognosis of fracture is directly related to several factors. Due to the limitations of existing treatment strategies, there are still many fractures with poor healing. Bone marrow mesenchymal stem cells (BMSCs) have the potential to differentiate into osteoblasts and chondrocytes. Therefore, BMSC transplantation is promised as an effective method for treating bone fractures. We aim to explore whether silently expressing sclerostin gene (SOST) can promote bone formation through the SOST/Wnt/ß-catenin signal pathway. We isolated rat BMSCs and the target gene (SOST shRNA) was transduced into them for osteogenic induction. The results showed that SOST significantly inhibited the proliferation and osteogenic differentiation of BMSCs during osteogenic induction, whereas silently expressing SOST not only increased the number of surviving BMSCs but also promoted the expression of osteogenesis-related proteins RUNX2, osteoprotegerin, Collagen I (COL-I), and bone morphogenetic protein-2 during osteogenic induction. The results of imaging examination in rats show that downregulating the expression of SOST can promote the formation of bony callus and the transformation of cartilage tissue into normal bone tissue, and then accelerate the healing of osteoporotic fracture. In addition, we also found that SOST silencing can activate the Wnt/ß-catenin pathway to achieve these effects. In conclusion, SOST silencing can promote the proliferation and osteogenic differentiation of BMSCs in situ, and therefore may enhance the therapeutic efficiency of BMSC transplantation in OPF.

Dysregulation of interleukin-8 is involved in the onset and relapse of schizophrenia: An independent validation and meta-analysis.

As a major mental health disorder, symptoms of schizophrenia (SCZ) include delusions, reduced motivation, hallucinations, reduced motivation and a variety of cognitive disabilities. Many of these symptoms are now known to be associated with abnormal regulation of the immune system. Low blood levels of cytokines and chemokines have been suggested to be one of the underlying causes of SCZ. However, their biological roles at different stages of SCZ remain unclear. Our objective was to investigate expression patterns of cytokines and chemokines at different stages of onset and relapse in SCZ patients and to conduct an analysis of their relationship to disease progression. We also aimed to identify immune features associated with different disease trajectories in patients with SCZ. Gene set enrichment analysis (GSEA) was used to interrogate the GSE27383 dataset and identify key genes associated with inflammation. These results led us to recruit 36 healthy controls, 40 patients with first-episode psychosis (FEP), and 39 patients with SCZ relapse. Meso Scale Discovery technology was used to independently validate serum levels of 35 cytokines and chemokines. This was followed by a meta-analysis to gain a more comprehensive understanding of the role of interleukin-8 (IL-8/CXCL8) in SCZ. Analysis of the GSE27383 database revealed 3596 genes with distinct expression patterns. A significant portion of these genes were identified as inflammation-related and showed remarkable enrichment in three key pathways: IL-17, cytokine-cytokine receptor, and AGE-RAGE signaling in diabetic complications. We observed co-expression of CXCL8 and IL-16 within these three pathways. In a subsequent analysis of independently validated samples, a notable discrepancy was detected in the inflammatory status between individuals experiencing FEP and those in relapse. In particular, expression of CXCL8 demonstrated superior predictive capability in FEP and relapsed patients. Notably, results of the meta-analysis confirmed that Chinese and European populations were consistent with the overall results (Z = 4.60, P < 0.001; Z = 3.70, P < 0.001). However, in the American subgroup, there was no significant difference in CXCL8 levels between patients with SCZ compared to healthy controls (Z = 1.09, P = 0.277). Our findings suggest that the inflammatory response in patients with SCZ differs across the different stages, with CXCL8 emerging as a potential predictive factor. Collectively, our data suggest that CXCL8 has the potential to serve as a significant immunological signature of SCZ subtypes. Trial registration: The clinical registration number for this trial is ChiCTR2100045240 (Registration Date: 2021/04/09).

AutoDDI: Drug-Drug Interaction Prediction With Automated Graph Neural Network.

Drug-drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug-drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug-drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug-drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug-drug interaction prediction.

Transparent multifunctional cellulose-based conductive hydrogel for wearable strain sensors and arrays.

Conductive hydrogels as promising candidate materials for flexible strain sensors have gained considerable attentions. However, it is still a great challenge to construct hydrogel with multifunctional performance via natural polymer. Herein, a novel multifunctional conductive hydrogel based on methylcellulose and cellulose nanocrystal was prepared via a facile and low-cost strategy. Methylcellulose (MC) was introduced to not only guarantee the stability of tannic acid coated cellulose nanocrystal (TA@CNCs) in LiCl solution, but also improve anti-freezing ability. The obtained hydrogel exhibited high transparency (98 % at 800 nm), good stretchability (663.1 %), low temperature tolerance (-23.9 °C), superior conductivity (2.89 S/m) and excellent UV shielding behavior. Flexible strain sensor assembled by the prepared hydrogels can be used to detect human body motions include subtle and large motions, and exhibited good sensitivity and stability over a wide temperature range. Multiple flexible hydrogels can also be assembled into a 3D sensor array to detect the distribution and magnitude of spatial pressure. Therefore, the hydrogels prepared via natural polymers will have broad application prospects in wearable devices, electronic skin and multifunctional sensor components.

Surface modification of chitin nanofibers with dopamine as efficient nanosorbents for enhanced removal of dye pollution and metal ions.

The development of environmentally friendly and low-cost adsorbents with high adsorption capacity remains a challenge. Herein, chitin nanofiber-polydopamine composite materials (CNDA) have been obtained by surface modification of chitin nanofiber using dopamine. According to the results of transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectrometer (FTIR), and X-ray photoelectron spectrometer (XPS), polydopamine have been successfully coated on the surface of chitin nanofiber (ChNF). The ability to remove methylene blue (MB) has been analyzed via standard adsorption experiments, indicating that the maximum adsorption capacity (qmax) can reach 196.6 mg/g at MB initial concentration of 50 mg/L. Most importantly, the adsorption kinetics, isotherm, and thermodynamics were used to investigate the MB adsorption mechanism on composites. This indicated that the polydopamine on the surface of chitin nanofiber (ChNF) plays an important role in the MB dye adsorption. Moreover, the removal ability of CNDA to metal ions has also been investigated, indicating high capacities for Fe3+, Mn2+, Cu2+, and Ni2+. Based on their biodegradability and good adsorption capacity, the CNDA composite material can be considered a promising adsorbent for wastewater treatment.

Cancer drug response prediction with surrogate modeling-based graph neural architecture search.

MOTIVATION: Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge. RESULTS: In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness. AVAILABILITY AND IMPLEMENTATION: https://github.com/BeObm/AutoCDRP.

[Corrigendum] Icaritin promotes the osteogenesis of bone marrow mesenchymal stem cells via the regulation of sclerostin expression.

Following the publication of the above article, the authors have contacted the Editorial Office to explain that they had assembled the cellular morphological images in Fig. 1A on p. 819 incorrectly; essentially, the cell morphology of 2 passages of hBMSCs (centre panel) should have been shown as the data panel for 3 passages of hBMSCs (right-hand panel), and likewise, the cell morphology of 3 passages of hBMSCs should have been shown as the data panel for 2 passages of hBMSCs. The revised version of Fig. 1 is shown below. The authors confirm that the errors associated with this figure did not have any significant impact on either the results or the conclusions reported in this study, and are grateful to the Editor of International Journal of Molecular Medicine for allowing them the opportunity to publish this Corrigendum. Furthermore, they apologize to the readership of the Journal for any inconvenience caused. [International Journal of Molecular Medicine 45: 816-824, 2020; DOI: 10.3892/ijmm.2020.4470].

Automatic Thoughts, Self-Stigma, and Resilience Among Schizophrenia Patients with Metabolic Syndrome: A Cross-Sectional Study.

Purpose: The study aims to clarify the negative psychological state and resilience impairments of schizophrenia (SCZ) with metabolic syndrome (MetS) while evaluating their potential as risk factors. Patients and Methods: We recruited 143 individuals and divided them into three groups. Participants were evaluated using the Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD)-24, Hamilton Anxiety Rating Scale (HAMA)-14, Automatic Thoughts Questionnaire (ATQ), Stigma of Mental Illness scale and Connor-Davidson Resilience Scale (CD-RISC). Serum biochemical parameters were measured by automatic biochemistry analyzer. Results: The score of ATQ was highest in the MetS group (F = 14.5, p < 0.001), and the total score of CD-RISC, subscale tenacity score and subscale strength score of CD-RISC were lowest in the MetS group (F = 8.54, p < 0.001; F = 5.79, p = 0.004; F = 10.9, p < 0.001). A stepwise regression analysis demonstrated that a negative correlation was observed among the ATQ with employment status, high-density lipoprotein (HDL-C), and CD-RISC (ß=-0.190, t=-2.297, p = 0.023; ß=-0.278, t=-3.437, p = 0.001; ß=-0.238, t=-2.904, p = 0.004). A positive correlation was observed among the ATQ with waist, TG, WBC, and stigma (ß=0.271, t = 3.340, p = 0.001; ß=0.283, t = 3.509, p = 0.001; ß=0.231, t = 2.815, p = 0.006; ß=0.251, t=-2.504, p = 0.014). The area under the receiver-operating characteristic curve analysis showed that among all independent predictors of ATQ, the TG, waist, HDL-C, CD-RISC, and stigma presented excellent specificity at 0.918, 0.852, 0.759, 0.633, and 0.605, respectively. Conclusion: Results suggested that the non-MetS and MetS groups had grievous sense of stigma, particularly, high degree of ATQ and resilience impairment was shown by the MetS group. The TG, waist, HDL-C of metabolic parameters, CD-RISC, and stigma presented excellent specificity to predict ATQ, and the waist showed excellent specificity to predict low resilience level.

Intermediary roles of prospective memory and retrospective memory in the comorbidity of depression and pain.

Background: Patients who suffer comorbidity of major depressive disorder (MDD) and chronic pain (CP) maintain a complex interplay between maladaptive prospective memory (PM) and retrospective memory (RM) with physical pain, and their complications are still unknown. Aims: We aimed to focus on the full cognitive performance and memory complaints in patients with MDD and CP, patients with depression without CP, and control subjects, considering the possible influence of depressed affect and chronic pain severity. Methods: According to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders and the criteria given by the International Association of Pain, a total of 124 participants were included in this cross-sectional cohort study. Among them, 82 depressed inpatients and outpatients from Anhui Mental Health centre were divided into two groups: a comorbidity group(patients with MDD and CP) (n=40) and a depression group (patients with depression without CP) (n=42). Meanwhile, 42 healthy controls were screened from the hospital's physical examination centre from January 2019 to January 2022. The Hamilton Depression Rating Scale-24 (HAMD-24) and Beck Depression Inventory-II (BDI-II) were used to evaluate the severity of depression. The Pain Intensity Numerical Rating Scale (PI-NRS), Short-Form McGill Pain Questionnaire-2 Chinese version (SF-MPQ-2-CN), Montreal Cognitive Assessment-Basic Section (MoCA-BC), and Prospective and Retrospective Memory Questionnaire (PRMQ) were used to assess pain-related features and the global cognitive functioning of study participants. Results: The impairments in PM and RM differed remarkably among the three groups (F=7.221, p<0.001; F=7.408, p<0.001) and were severe in the comorbidity group. Spearman correlation analysis revealed the PM and RM were positively correlated with continuous pain and neuropathic pain (r=0.431, p<0.001; r=0.253, p=0.022 and r=0.415, p<0.001; r=0.247, p=0.025), respectively. Regression analysis indicated a significant positive relationship between affective descriptors and total BDI-II score (ß=0.594, t=6.600, p<0.001). Examining the mediator pathways revealed the indirect role of PM and RM in patients with comorbid MDD and CP. Conclusions: Patients with comorbid MDD and CP presented more PM and RM impairments than patients with MDD without CP. PM and RM are possibly mediating factors that affect the aetiology of comorbid MDD and CP. Trial registration number: chiCTR2000029917.

Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction.

Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automatically design the optimal neural architectures for many problems such as node classification and graph classification. In the existing GNAS frameworks, the designed graph neural network (GNN) architectures learn the representation of homogenous graphs with one type of relationship connecting two nodes. However, multi-view graphs, where each view represents a type of relationship among nodes, are ubiquitous in the real world. The traditional GNAS frameworks learn the graph representation without considering the interactions between nodes and multiple relationships, so they fail to solve multi-view graph-based problems, such as multi-view graphs modelling the biomedical entity and relation extraction tasks. In this paper, we propose MVGNAS, a multi-view graph neural network automatic modelling framework for biomedical entity and relation extraction, to resolve this challenge. In MVGNAS, we propose an automatic multi-view representation learning to learn low-dimensional representations of nodes that capture multiple relationships in a multi-view graph, representing the first research work in literature to solve the problem of multi-view graph representation learning architecture search for biomedical entity and relation extraction tasks. The experimental results demonstrate that MVGNAS can achieve the best performance in biomedical entity and relation extraction tasks against the state-of-the-art baseline methods.

Childhood maltreatment increases the suicidal risk in Chinese schizophrenia patients.

Objectives: Childhood trauma might be a modifiable risk factor among adults with serious mental illness. However, the correlation of child trauma and suicide is unclear, which were cited most frequently as the biggest challenge to schizophrenia (SCZ) patients in China. We aim to study relationships between child trauma and suicide in SCZ patients of different disease stages. Methods: Ninety-one participants were included and divided into two groups, namely, first-episode group (n = 46), relapsed group (n = 45). The Positive and Negative Syndrome Scale was used to evaluate the severity of psychotic symptoms. The Beck's Suicide Intent Scale and The Nurses' Global Assessment of Suicide Risk were conducted by patient self-report to assess suicide symptom. The childhood trauma questionnaire was used to estimate severity of traumatic stress experienced during childhood. Results: Childhood trauma and different dimensions of suicide were significantly higher in the relapsed group than first-episode group (P < 0.01, respectively). BMI has a significant positive relationship with recent psychosocial stress (ß = 0.473, t = 3.521, P < 0.001) in first-episode group. As in relapsed group, BMI has a positive effect between severe mental illness and suicide ideation (ß = 0.672, t = 5.949, P < 0.001; ß = 0.909, t = 2.463, P < 0.001), Furthermore, emotional neglect presented positively related to the suicide risk and proneness to suicidal behavior (ß = 0.618, t = 5.518, P < 0.001; ß = 0.809, t = 5.356, P < 0.001). Conclusion: Relapsed group of patients had significantly more severe childhood trauma, recent psychosocial stress, suicidal risk and proneness to suicidal behavior. BMI and emotional neglect are unique predictors for different dimensions of suicide.

Effect of Mixed Acid Fluid on the Pore Structure of High Rank Coal and Acid Fluid Optimization.

Acidizing technology is an important means to increase production in oil-gas reservoirs. In recent years, acidizing technology has been widely used to increase the permeability of coal seams to enhance gas extraction, where acidizing fluid is the key factor to determine the permeability improvement effect by acidizing technology. In order to clarify the influence of mixed acid fluid on the pore structure of high rank coal and seek the optimal mixed acid fluid suitable for acidizing and permeability improvement of high rank coal in the Jiaozuo coal mine area. Taking the Jiulishan Mine in the Jiaozuo mining area as an example, low field nuclear magnetic resonance (LFNMR) test and static dissolution test were conducted to obtain the T 2 spectrum, porosity, movable fluid saturation, pore throat distribution, nuclear magnetic permeability, and dissolution rate of coal samples before and after treatment with distilled water and three mixed acid fluids. On this basis, the influence of mixed acid fluid on the pore structure of high rank coal was analyzed and the optimal mixed acid fluid suitable for high rank coal was selected. The results showed that the pore size, number, and volume of all kinds of pore sizes of coal samples treated with distilled water all decreased, which was manifested by the decrease of effective porosity and nuclear magnetic permeability. After acidification, the proportion of micropore volume in coal decreased significantly, the number and proportion of pore volume of mesopores and macropore-microfractures increased significantly, and the connectivity between mesopores and macropore-microfractures was enhanced, which was characterized by the increase in effective porosity and nuclear magnetic permeability of coal samples. After acidification, the pore-throat ratio of adsorption pores of all coal samples decreased, while the pore-throat ratio of seepage pores increased. By comparatively analyzing the change law of pore structure of coal samples before and after acidizing with three kinds of mixed acid fluids, the optimal mixed acid fluid suitable for acidizing and permeability improvement of high rank coal in the Jiaozuo coal mine area was selected, which was 12%HCL +3%HF.

AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction.

It is significant to comprehend the relationship between metabolic pathway and molecular pathway for synthesizing new molecules, for instance optimizing drug metabolization. In bioinformatics fields, multi-label prediction of metabolic pathways is a typical manner to understand this relationship. Graph neural networks (GNNs) have become an effective method to extract molecular structure's features for multi-label prediction of metabolic pathways. Though GNNs can effectively capture structural features from molecular structure graphs, building a well-performed GNN model for a given molecular structure data set requires the manual design of the GNN architecture and fine-tuning of the hyperparameters, which are time-consuming and rely on expert experience. To address the above challenge, we design an end-to-end automatic molecular structure representation learning framework named AutoMSR that can design the optimal GNN model based on a given molecular structure data set without manual intervention. We propose a multi-seed age evolution (MSAE) search algorithm to identify the optimal GNN architecture from the GNN architecture subspace. For a given molecular structure data set, AutoMSR first uses MSAE to search the GNN architecture, and then it adopts a tree-structured parzen estimator to obtain the best hyperparameters in the hyperparameters subspace. Finally, AutoMSR automatically constructs the optimal GNN model based on the best GNN architecture and hyperparameters to extract the molecular structure features for multi-label metabolic pathway prediction. We test the performance of AutoMSR on the real data set KEGG. The experiment results show that AutoMSR outperforms baseline methods on different multi-label classification evaluation metrics.

Potential Role of Pain Catastrophic Thinking in Comorbidity Patients of Depression and Chronic Pain.

Background: Although comorbidity of major depressive disorder (MDD) and chronic pain (CP) has been well-studied, their association with pain catastrophizing is largely elusive. This study aimed to investigate the potential effects of pain catastrophizing in patients with a comorbidity. Methods: In total, 140 participants were included in this study and divided into three groups according to the Diagnostic and Statistical Manual of Mental Disorders and the International Association for the study of pain (i.e., the comorbidity group: patients with depression with chronic pain, n = 45; depression group: patients with depression without chronic pain, n = 47; and healthy controls: n = 48). The Hamilton Depression Rating Scale (HAMD)-24 and Hamilton Anxiety Rating Scale (HAMA)-14 were used by professional psychiatrists to evaluate the severity of depression and anxiety. Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) were conducted by patients' self-report to assess the symptom severity. The pain intensity numerical rating scale (PI-NRS) was used to assess the pain intensity. Pain Catastrophizing Scale (PCS) and Pain Anxiety Symptoms Scale (PASS) were used to estimate pain-related negative thinking. Results: The results showed that PASS and PCS scores were significantly different among the three groups. Particularly, the scores in the comorbidity group were the highest. The Pearson correlation analysis revealed a positive correlation between PCS (including the patients' helplessness, magnification, rumination, and total scores) and the severity of depression symptoms, anxiety symptoms, and pain intensity (P < 0.05). A stepwise regression analysis further demonstrated that the total PCS score, high monthly income level, and BDI score had positive impacts on PASS (P < 0.05). We also found that the total BDI score, disease course ≥1 year, and pain intensity had positive effects on PCS (P < 0.05), whereas years of education (≤ 12 years) had a negative effect on PCS (P = 0.012). In all, we have clearly demonstrated that PCS and PASS could serve as potentially predictive factors in patients suffering from comorbidity of MDD and CP. Conclusion: Our results suggested that the pain-related catastrophic thinking and anxiety were more severe in the comorbidity group than in MDD-only group and healthy group. Pain-related catastrophizing thoughts and anxiety may have potentially effects on the comorbidity of depression and chronic pain.

The Developing Human Connectome Project Neonatal Data Release.

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.

The Predictive Role of Aberrant Metabolic Parameters and Negative Automatic Thinking on the Cognitive Impairments Among Schizophrenia Patients with Metabolic Syndrome.

Purpose: The study aimed to clarify the cognitive impairments of schizophrenia with metabolic syndrome while evaluating their potential as risk factors. Patients and Methods: We recruited 153 participants and divided them into three groups according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria and the guideline standards for the prevention and treatment of dyslipidemia in Chinese adults in 2007 for metabolic syndrome, as follows: healthy control group (n = 47); nonmetabolic syndrome group (n = 58); and metabolic syndrome group (n = 48). Psychotic symptoms were evaluated using the Positive and Negative Syndrome Scale. Cognitive function and automatic thinking were estimated using the Montreal Cognitive Assessment Scale, Verbal Fluency Test, and Automatic Thoughts Questionnaire. Serum biochemical parameters were measured by automatic biochemistry analyzer. Results: One-way ANOVA analysis revealed that differential cognition impairments in schizophrenia patients compared to controls. Furthermore, results of multiple comparisons showed that more serious barriers in orientation, language fluency, and negative automatic thinking existed in the metabolic syndrome group than in the healthy and non-metabolic syndrome groups. Spearman correlation and stepwise linear regression analyses showed that psychopathological symptoms, high waist circumference, and high triglyceride were the predictive factors for negative automatic thoughts, orientation, and language fluency. Those results collectively revealed that high waist circumference, high triglyceride and negative automatic thinking had validity and effectiveness in predicting the cognitive function impairments of the metabolic syndrome group. Conclusion: The present findings strongly supported the notion that aberrant parameters of high waist circumference, high triglyceride and high negative automatic thoughts had validity and effectiveness predictive role for cognitive impairments in the schizophrenics with metabolic syndrome. The schizophrenia patients with metabolic syndrome should receive regular monitoring and adequate treatment for metabolic and psychological risk factors.

Network Association of Biochemical and Inflammatory Abnormalities With Psychiatric Symptoms in First-Episode Schizophrenia Patients.

Background: Cardiovascular disease (CVD) risk factors such as dyslipidemia and systemic aberrant inflammatory processes may occur in patients with psychotic disorders, which may cause increased mortality. The interplay between immune and metabolic markers and its contribution to the clinical symptoms of schizophrenia (SCZ) remain unclear. This study aimed to examine the association of a series of inflammatory factors, plasma biochemical indicators, and SCZ clinical symptomatology with the severity of SCZ symptoms. Methods: A total of 115 participants, including 79 first-episode drug-naïve patients with SCZ and 36 healthy controls, were enrolled in this study. Semi-structured interviews were used to collect sociodemographic data, family history of SCZ, and medical and psychiatric history. The Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale (PANSS) were administered by a clinical psychiatrist to evaluate the symptom severity of patients with SCZ. Plasma inflammatory cytokines were measured by a fully automated electrochemiluminescent immunoassay (Meso Scale Discovery). Results: Blood routine, biochemical, and inflammation cytokine test results showed that the levels of white blood cell count, neutrophil count, natrium, CRP, IL-8, IL-6, IL-13, and IL-16 significantly increased in the case group than in the healthy controls (p < 0.05), whereas levels of red blood cell count, hemoglobin concentration, mean corpuscular hemoglobin concentration, total protein, albumin, total bile acid, high-density lipoprotein (HDL), apolipoprotein A1, blood urea nitrogen, kalium and IL-15 were lower than in the healthy controls (p < 0.05). Correlation network analysis results shown that the natrium, HDL and red blood cell count were the top 3 factors closely to with BPRS and PANSS related clinical symptoms among of correlation network (degree = 4). ROC curve analysis explored the IL-16, IL-8, IL-13, IL-15, natrium, and HDL had highly sensitivity and specificity to the predictive validity and effectiveness for SCZ symptoms. Conclusion: Our study revealed a complex interactive network correlation among the cardiovascular risk factors, biological immunity profiles, and psychotic symptoms in first-episode patients. Abnormal inflammatory factors and CVD risk factors had high sensitivity and specificity for predicting SCZ symptoms. Generally, our study provided novel information on the immune-related mechanisms involved in early CVD risk in patients with psychotic disorders.