Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes.

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.

Tumor tissue microorganisms are closely associated with tumor immune subtypes.

Studies have found that different immune subtypes are present in the same tumor. Different tumor subtypes have different tumor microenvironments (TME). This means that the efficacy of immunotherapy in actual applications will, therefore, have different results. Existing tumor immune subtype studies have mostly focused on immune cells, stromal cells, genes and molecules without considering the presence of microbes. Some studies have shown that microflora can strongly promote many gastrointestinal cancers. The microbiome has, therefore, become an important biomarker and regulatory factor of cancer progression and therapeutic responses. In addition, the presence of microflora can strongly regulate the host immune system, indirectly affecting tumor growth. Taken together, it is important to study the relationships that develop among tumor tissue microorganisms, tumor immune subtype, and the TME. In this study, correlations between microbial abundance, immune cell infiltration, immune gene expression and tumor immune subtype were studied. To accomplish this, tissue microorganisms and immune cell ratios with significant differences between the different cancers were obtained by comparing 203 gastric cancer and intestinal cancer samples. Two immune subtypes of intestinal samples were obtained by K-means clustering algorithm and tissue microorganisms, immune cell ratios and immune-related genes with significant differences between different immune subtypes were screened through Wilcoxon rank sum test. The results showed that Clostridioides difficile, Aspergillus fumigatus, Yarrowia lipolytica, and Fusarium pseudograminearum were all closely associated with the identified tumor immune subtypes. Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/IMM-subtype.git.

Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework.

As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its biological and phenotypic effects. Experiments have demonstrated that CRISPR/Cas9-generated cellular-repair outcomes depend on local sequence features. Therefore, the repair outcomes after DNA break can be predicted by sequences near the cleavage sites. However, existing prediction methods rely on manually constructed features or insufficiently detailed prediction labels. They cannot satisfy clinical-level-prediction accuracy, which limit the performance of these models to existing knowledge about CRISPR/Cas9 editing. We predict 557 repair labels of DNA, covering the vast majority of Cas9-generated mutational outcomes, and build a deep learning model called Apindel, to predict CRISPR/Cas9 editing outcomes. Apindel, automatically, trains the sequence features of DNA with the GloVe model, introduces location information through Positional Encoding (PE), and embeds the trained-word vector matrixes into a deep learning model, containing BiLSTM and the Attention mechanism. Apindel has better performance and more detailed prediction categories than the most advanced DNA-mutation-predicting models. It, also, reveals that nucleotides at different positions relative to the cleavage sites have different influences on CRISPR/Cas9 editing outcomes.

Joint Analysis of Microbial and Immune Cell Abundance in Liver Cancer Tissue Using a Gene Expression Profile Deconvolution Algorithm Combined With Foreign Read Remapping.

Recent transcriptomics and metagenomics studies showed that tissue-infiltrating immune cells and bacteria interact with cancer cells to shape oncogenesis. This interaction and its effects remain to be elucidated. However, it is technically difficult to co-quantify immune cells and bacteria in their respective microenvironments. To address this challenge, we herein report the development of a complete a bioinformatics pipeline, which accurately estimates the number of infiltrating immune cells using a novel Particle Swarming Optimized Support Vector Regression (PSO-SVR) algorithm, and the number of infiltrating bacterial using foreign read remapping and the GRAMMy algorithm. It also performs systematic differential abundance analyses between tumor-normal pairs. We applied the pipeline to a collection of paired liver cancer tumor and normal samples, and we identified bacteria and immune cell species that were significantly different between tissues in terms of health status. Our analysis showed that this dual model of microbial and immune cell abundance had a better differentiation (84%) between healthy and diseased tissue. Caldatribacterium sp., Acidaminococcaceae sp., Planctopirus sp., Desulfobulbaceae sp.,Nocardia farcinica as well as regulatory T cells (Tregs), resting mast cells, monocytes, M2 macrophases, neutrophils were identified as significantly different (Mann Whitney Test, FDR< 0.05). Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/PSO-SVR.git.

The risk factors of preterm birth: A multicentre case-control survey in China in 2018.

OBJECTIVES: To investigate the risk profile of preterm birth (PTB) in 2018 in China. METHOD: A prospective multicentre case-control study was conducted in 15 hospitals located in seven provinces throughout three geographical areas (the Eastern, South-Central and North-Western regions) in China. A total of 3147 preterm (<37+0 weeks) and 3147 term (37+0 to 41+6 weeks) live-birth mothers were included. Designed questionnaires were used to investigate maternal and fetal information. We calculated multivariable logistic regression and population attributable risk (PAR). RESULTS: Iatrogenic PTB accounted for 48.1% of preterm mothers. Multivariable analysis showed PTB was significantly associated with six categories of maternal and fetal factors, adverse life-style and psychological conditions (adjusted odds ratio (aOR) 2.063, 95% confidence interval (CI) 1.601-2.657) had the highest PAR% (60.1%). High school and below education level (PAR% = 25.8%), living in town or village (PAR% = 24.4%), low pregnant weight gain (PAR% = 16.8%), hypertensive disorders in pregnancy (aOR: 5.010, 95% CI: 4.039-6.216, PAR% = 15.3%), placental abnormality (aOR: 4.242, 95% CI: 3.454-5.211, PAR% = 14.1%) and multiple pregnancy (aOR: 10.990, 95% CI: 7.743-15.599, PAR% = 11.8%) were significantly associated with PTB with high PAR% value. CONCLUSION: The main risk factors for PTB in China were placental abnormality, hypertensive disorders in pregnancy and multiple pregnancy. Adverse life-style and psychological conditions and socio-economic disadvantage had high public health significance.

CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction.

As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this process more accurate and efficient. Although there are already many sophisticated machine learning or deep learning models to predict the on-target cleavage efficiency of sgRNA, prediction accuracy remains to be improved. XGBoost is good at classification as the ensemble model could overcome the deficiency of a single classifier to classify, and we would like to improve the prediction efficiency for sgRNA on-target activity by introducing XGBoost into the model. We present a novel machine learning framework which combines a convolutional neural network (CNN) and XGBoost to predict sgRNA on-target knockout efficacy. Our framework, called CNN-XG, is mainly composed of two parts: a feature extractor CNN is used to automatically extract features from sequences and predictor XGBoost is applied to predict features extracted after convolution. Experiments on commonly used datasets show that CNN-XG performed significantly better than other existing frameworks in the predicted classification mode.

Core self-evaluation, mental health and mobile phone dependence in Chinese high school students: why should we care.

BACKGROUND: Mobile phone dependence is a common problem in the population of high school students. We aimed to evaluate the core self-evaluation, mental health and mobile phone dependence in Chinese high school students, to provide reliable evidence to the support of high school students. METHODS: We conducted a survey of high school students in Xiamen, China. The self-assessment questionnaire on mobile phone use dependence among teenagers (SQAPMPU), Mental Health Scale for Middle School Students (MSSMHS) and Core self-evaluation scale were used to evaluate the mobile phone dependence, mental health and core self-evaluation. t-tests, Pearson correlation and multiple linear stepwise regression analyses were conducted to analyze the potential relationships. RESULTS: A total of 1692 students were enrolled. The total score of mobile phone dependence of students in grades 10-12 was higher than that of students in grades 7-9. A total of 329 students in grades 7-9 and 371 students in grades 10-12 had abnormal mental status. The detection rate of psychological abnormalities among students in grades 10-12 was higher than that of students in grades 10-12. Core self-evaluation had significantly negative correlation with various factors of mental health (P < 0.01). The worse the mental health, the higher the degree of mobile phone dependence, core self-evaluation played a mediating role between mental health and mobile phone dependence (8.03%). CONCLUSIONS: Core self-evaluation is an important factor affecting the mental health of high school students and mobile phone dependence. Educators should strengthen core self-evaluation of high school students to improve the mental health and reduce the mobile phone dependence.

Human exposure to F-53B in China and the evaluation of its potential toxicity: An overview.

Chlorinated polyfluoroalkyl ether sulfonic acid (Cl-PFESAs, trade name F-53B), an alternative to perfluorooctane sulfonate (PFOS), has been widely used as a mist suppressant in the Chinese electroplating industry since the 1970 s. Due to greater restrictions on PFOS globally in recent years, the production and use of F-53B correspondingly increased, consequently causing more emissions into the environment. In China, an increasing number of studies report frequent detection and broad exposure to F-53B in the natural environment, various wildlife and the human body. In human blood, the detection rate of F-53B is almost 80%, accounting for 8.69 to 28% of ∑per- and polyfluoroalkyl substances (PFASs). F-53B is the most biopersistent PFAS in humans to date, with a half-life of 15.3 years. In addition, F-53B displays protein binding affinity and high human placental permeability. Recently, some epidemiological studies have reported the health risks associated with F-53B in humans, including abnormal serum lipid metabolism, vascular dysfunction, endocrine disorders and even adverse birth outcomes. Various in vivo and in vitro studies have demonstrated the toxicity of F-53B, such as hepatotoxicity, interference effects on the endocrine system, as well as reproductive and developmental toxicity. Our aims are to review studies on human F-53B exposure levels, trends and associated health effects; evaluate the potential toxicity; and predict directions for future research.

DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference.

Tissues are constituted of heterogeneous cell types. Although single-cell RNA sequencing has paved the way to a deeper understanding of organismal cellular composition, the high cost and technical noise have prevented its wide application. As an alternative, computational deconvolution of bulk tissues can be a cost-effective solution. In this study, we propose DecOT, a deconvolution method that uses the Wasserstein distance as a loss and applies scRNA-seq data as references to characterize the cell type composition from bulk tissue RNA-seq data. The Wasserstein loss in DecOT is able to utilize additional information from gene space. DecOT also applies an ensemble framework to integrate deconvolution results from multiple individuals' references to mitigate the individual/batch effect. By benchmarking DecOT with four recently proposed square loss-based methods on pseudo-bulk data from four different single-cell data sets and real pancreatic islet bulk samples, we show that DecOT outperforms other methods and the ensemble framework is robust to the choice of references.

Kawasaki disease with pulmonary nodules: two case reports and literature review.

In this study, we report case studies of two infants diagnosed with Kawasaki disease with pulmonary nodules. The typical symptoms of Kawasaki disease include changes to the lips and oral cavity, including reddening of the lips and strawberry tongue, the diffuse injection of oral and pharyngeal mucosa, a rash (including redness at the site of the Bacille Calmette-Guerin inoculation), and changes to peripheral extremities, which in the initial stage include the reddening of palms and soles and edema, and in the convalescent stage include periungual desquamation and non-suppurative cervical lymphadenopathy. One of the patients had mild, transient bilateral conjunctival redness, and the other patient had a fever. However, chest imaging revealed multiple lung nodules, and echocardiography revealed coronary artery dilatation. Based on the symptoms, the laboratory indicators and echocardiography, diagnoses of incomplete Kawasaki disease were confirmed. The physicians provided treatments to the patients that significantly alleviated their conditions, including intravenous immunoglobulin (IVIG) and aspirin. The coronary arteries of the two infants returned to normal, and the lung nodules of the two infants disappeared. We conducted a literature search and identified publications in which nine children reported pulmonary nodules associated with Kawasaki disease. At present, lung damage remains a challenge in diagnosing and differential diagnosis of Kawasaki disease and incomplete Kawasaki disease.

Deep learning improves the ability of sgRNA off-target propensity prediction.

BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no longer satisfy the prediction accuracy at the clinical level. RESULTS: Here, we propose a prediction method named CnnCrispr to predict the off-target propensity of sgRNA at specific DNA fragments. CnnCrispr automatically trains the sequence features of sgRNA-DNA pairs with GloVe model, and embeds the trained word vector matrix into the deep learning model including biLSTM and CNN with five hidden layers. We conducted performance verification on the data set provided by DeepCrispr, and found that the auROC and auPRC in the "leave-one-sgRNA-out" cross validation could reach 0.957 and 0.429 respectively (the Pearson value and spearman value could reach 0.495 and 0.151 respectively under the same settings). CONCLUSION: Our results show that CnnCrispr has better classification and regression performance than the existing states-of-art models. The code for CnnCrispr can be freely downloaded from https://github.com/LQYoLH/CnnCrispr.

A Branch Point on Differentiation Trajectory is the Bifurcating Event Revealed by Dynamical Network Biomarker Analysis of Single-Cell Data.

The advance in single-cell profiling technologies and the development in computational algorithms provide the opportunity to reconstruct pseudo temporal trajectory with branch point of cellular development. On the other hand, theories such as dynamical network biomarkers (DNB) theory have been recently proposed to characterize the pre-transition state in biological systems. Few studies have validated whether the branch point identified in pseudo time is the critical point in dynamical system. In this study, the dynamical behavior of the branch point on the pseudo trajectory has been investigated. We study the pseudo temporal trajectories reconstructed by Wishbone and diffusion pseudotime analysis (DPT) algorithms, as well as the simulated trajectory. DNB theory is applied to justify the bifurcating event on the pseudo trajectories. Our results demonstrate that the branch point recovered by Wishbone and DPT algorithms is confirmed as a transition state in cell differentiation process by DNB theory. Furthermore, we show that an appropriate DNB group will amplify the comprehensive index of critical event as defined in DNB theory. Our study provides biological insights on pseudo trajectory with branch point in a dynamical view and also indicates that DNB theory may serve as a benchmark to check the validity of branch point.

Causes and Follow-Up of Central Diabetes Insipidus in Children.

OBJECTIVE: To identify the causes of central diabetes insipidus (CDI) by evaluating the values of magnetic resonance imaging (MRI) in the diagnosis of pediatric CDI, providing evidence for the clinical diagnosis and treatment of CDI. METHODS: Seventy-nine patients with CDI (CDI group) hospitalized from July 2012 to March 2017 and 43 healthy children (control group) were enrolled in this study. All cases underwent MRI examination including T1-weighted three-dimensional magnetization-prepared rapid gradient-echo (T1WI-3D-MP RAGE) imaging sequences. The pituitary volume, the signal intensity of posterior pituitary, and the morphology of pituitary stalk were measured between two groups. The medical history, urine testing, imaging of hypothalamic-pituitary region, and hormone levels were also recorded. RESULTS: Age and gender were matched between the CDI and control groups. The height and BMI in the CDI group were less and the urine volume in 24 h was higher than those in the control group. The signal intensity of the posterior pituitary was higher in the control group, whereas the pituitary volume was smaller in the CDI group. In the CDI group, 44 cases presented with morphological changes of the pituitary stalk. Clinical symptoms mainly included polydipsia, polyuria, short stature, and vomiting. All patients were confirmed by water deprivation vasopressin test. Forty-four CDI children were associated with hypopituitarism, including 33 cases of PSIS with multiple pituitary hormone deficiencies (MPHD) and 11 cases of growth hormone deficiency (IGHD). The pituitary volume in the cases of pituitary stalk interruption syndrome (PSIS) with MPHD was smaller than that in the IGHD patients. CONCLUSIONS: The signal intensity ratio of the posterior lobe, pituitary volume, and the morphology of pituitary stalk on T1WI-3D-MP RAGE image contribute to the diagnosis of CDI.

Prediction of Long Non-Coding RNAs Based on Deep Learning.

With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify long non-coding RNAs (lncRNAs) from transcripts is a challenging task. The identification and inclusion of lncRNAs not only can more clearly help us to understand life activities themselves, but can also help humans further explore and study the disease at the molecular level. At present, the detection of lncRNAs mainly includes two forms of calculation and experiment. Due to the limitations of bio sequencing technology and ineluctable errors in sequencing processes, the detection effect of these methods is  not very satisfactory. In this paper, we constructed a deep-learning model to effectively distinguish lncRNAs from mRNAs. We used k-mer embedding vectors obtained through training the GloVe algorithm as input features and set up the deep learning framework to include a bidirectional long short-term memory model (BLSTM) layer and a convolutional neural network (CNN) layer with three additional hidden layers. By testing our model, we have found that it obtained the best values of 97.9%, 96.4% and 99.0% in F1score, accuracy and auROC, respectively, which showed better classification performance than the traditional PLEK, CNCI and CPC methods for identifying lncRNAs. We hope that our model will provide effective help in distinguishing mature mRNAs from lncRNAs, and become a potential tool to help humans understand and detect the diseases associated with lncRNAs.

Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients.

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

A Novel Dual Eigen-Analysis of Mouse Multi-Tissues' Expression Profiles Unveils New Perspectives into Type 2 Diabetes.

Type 2 diabetes (T2D) is a complex and polygenic disease yet in need of a complete picture of its development mechanisms. To better understand the mechanisms, we examined gene expression profiles of multi-tissues from outbred mice fed with a high-fat diet (HFD) or regular chow at weeks 1, 9, and 18. To analyze such complex data, we proposed a novel dual eigen-analysis, in which the sample- and gene-eigenvectors correspond respectively to the macro- and micro-biology information. The dual eigen-analysis identified the HFD eigenvectors as well as the endogenous eigenvectors for each tissue. The results imply that HFD influences the hepatic function or the pancreatic development as an exogenous factor, while in adipose HFD's impact roughly coincides with the endogenous eigenvector driven by aging. The enrichment analysis of the eigenvectors revealed diverse HFD impact on the three tissues over time. The diversity includes: inflammation, degradation of branched chain amino acids (BCAA), and regulation of peroxisome proliferator activated receptor gamma (PPARγ). We reported that in the pancreas remarkable up-regulation of angiogenesis as downstream of the HIF signaling pathway precedes hyperinsulinemia. The dual eigen-analysis and discoveries provide new evaluations/guidance in T2D prevention and therapy, and will also promote new thinking in biology and medicine.

Usefulness of Positron Emission Tomography in Patients with Syphilis: A Systematic Review of Observational Studies.

BACKGROUND: Diagnosis of syphilis is difficult. Follow-up and therapy evaluation of syphilitic patients are poor. Little is known about positron emission tomography (PET) in syphilis. This review was to systematically review usefulness of PET for diagnosis, disease extent evaluation, follow-up, and treatment response assessment in patients with syphilis. METHODS: We searched PubMed, EMBASE, SCOPUS, Cochrane Library, Web of Science, ClinicalTrials.gov, and three Chinese databases (SinoMed, Wanfang, and CNKI) for English and Chinese language articles from inception to September 2016. We also collected potentially relevant studies and reviews using a manual search. The search keywords included the combined text and MeSH terms "syphilis" and "positron emission tomography". We included studies that reporting syphilis with a PET scan before and/or after antibiotic treatment. The diagnosis of syphilis was based on serological criteria or dark field microscopy. Outcomes include pre- and post-treatment PET scan, pre- and post-treatment computed tomography, and pre- and post-treatment magnetic resonance imaging. We excluded the articles not published in English or Chinese or not involving humans. RESULTS: Of 258 identified articles, 34 observational studies were included. Thirty-three studies were single-patient case reports and one study was a small case series. All patients were adults. The mean age of patients was 48.3 ± 12.1 years. In primary syphilis, increased fluorodeoxyglucose (FDG) accumulation could be seen at the site of inoculation or in the regional lymph nodes. In secondary syphilis with lung, bone, gastrointestinal involvement, or generalized lymphadenopathy, increased FDG uptake was the most commonly detected changes. In tertiary syphilis, increased glucose metabolic activity, hypometabolic lesions, or normal glucose uptake might be seen on PET. There were five types of PET scans in neurosyphilis. A repeated PET scan after treatment revealed apparent or complete resolution of the asymmetry of radiotracer uptake. CONCLUSION: PET is helpful in targeting diagnostic interventions, characterizing disease extent, assessing nodal involvement, and treatment efficacy for syphilis.

The reduction of CSF tumor necrosis factor alpha levels in schizophrenia: no correlations with psychopathology and coincident metabolic characteristics.

AIM: The aim of this study was to assess whether tumor necrosis factor alpha (TNF-α) levels are correlated with the behavioral syndrome of schizophrenia and/or metabolic abnormalities. METHODS: Sixty patients with first-onset schizophrenia were recruited. The concentrations of TNF-α in the cerebrospinal fluid (CSF) were determined in 22 schizophrenia patients and ten patients with nonsuppurative appendicitis using a radioimmunoassay. Physiological characteristics such as fasting blood glucose, fasting insulin, triglycerides, corrected QT interval, waist circumference, and body mass index were measured prior to CSF collection. Subjects were screened for insulin resistance using the homeostasis model assessment. The extent of positive and negative behavioral symptoms was scored using the Positive and Negative Syndrome Scale. RESULTS: The CSF TNF-α levels in schizophrenic patients were significantly lower than those in the control group. The age of disease onset was positively correlated with the CSF TNF-α level using Pearson correlation analysis (r=0.37, P<0.05). There were no significant differences in CSF TNF-α levels in terms of age, duration of schizophrenia, or systolic and diastolic blood pressure. Furthermore, the CSF TNF-α levels were not significantly correlated with fasting blood glucose, fasting insulin, insulin resistance index, triglycerides, corrected QT interval, waist circumference, or body mass index. No significant correlation was found between CSF TNF-α levels and the Positive and Negative Syndrome Scale total scores or other factors scores. There were also no significant differences in CSF TNF-α levels between patients with schizophrenia types I and II. CONCLUSION: CSF TNF-α levels are decreased in schizophrenia, although this reduction does not correlate with the psychopathology or coincident metabolic characteristics of this disease.

Restless Legs Syndrome in Adults in Peking Union Medical College Hospital.

Objective To investigate the clinical characteristics of restless legs syndrome (RLS) in adults in Peking Union Medical College Hospital and explore the sleep quality,fatigue degree,daytime sleepiness,disease severity,depression and anxiety of RLS patients.Methods Totally 4739 consecutive patients who visited the outpatient departments with any sleep complaint or leg discomforts were recruited in the study. Patients under 18 years were excluded. All participants answered RLS questionnaire. The subjects fulfilled all four criteria would be followed up and given advanced examinations to rule out secondary RLS and RLS mimics. Primary RLS patients were evaluated with International Restless Legs Scale (IRLS),Pittsburgh Sleep Quality Index (PSQI),Fatigue Severity Scale (FSS),Epworth Sleepiness Scale (ESS),and Hospital Anxiety and Depression Scale for depression and anxiety (HADD and HADA). Another two groups of age-and gender-matched healthy subjects and non-RLS insomnia patients were served as normal and non-RLS insomnia controls.Results There were 162 (3.42%,162/4739) subjects fulfilling all four criteria for RLS; 42 (0.89%,42/4739) subjects were diagnosed as primary RLS and 33 (0.70%,33/4739) as RLS mimics. In primary RLS patients,41(97.6%) were found to be with poor sleep,13 (31.0%) with anxiety,and 4(9.5%) with depression. The scores of PSQI(q=11.69,P=0.000),HADA(q=8.02,P=0.000),and HADD(q=6.60,P=0.000)in primary RLS patients were significantly higher than those in normal controls. The scores of FSS(q=3.74,P=0.001),ESS(q=2.97,P=0.012),and HADD(q=4.15,P=0.000) in primary RLS patients were significantly lower than those in non-RLS insomnia controls. The scores of HADA and HADD were significantly correlated with those of PSQI(r=0.340,P=0.028;r=0.383,P=0.012),FSS(r=0.445,P=0.003;r=0.511,P=0.001),and IRLS(r=0.477,P=0.001;r=0.578,P=0.000). Conclusions RLS should be considered in the patients with any sleep-related complaint or leg discomforts. Primary RLS patients suffer from bad sleep and are more susceptible to anxiety and depression. Secondary RLS and RLS mimics should be excluded before the diagnosis of primary RLS.

Bone morphogenetic protein 2 regulates the differentiation of nitrergic enteric neurons by modulating Smad1 signaling in slow transit constipation.

Bone morphogenetic proteins (BMPs) belong to the transforming growth factor superfamily and have been implicated in chondrogenesis and neuronal differentiation. In order to examine the function of bone morphogenetic protein 2 (BMP­2) on the differentiation of nitrergic enteric neurons in slow transit constipation (STC), the expression of BMP­2 and neuronal nitric oxide synthase (nNOS) was investigated in the myenteric nerve plexus in STC and control tissues by immunohistochemical assays. The present study demonstrated that BMP­2 and nNOS were expressed in the myenteric nerve plexus and their levels were differentially altered in the STC group and control group. In addition, the effect of BMP­2 on primary myenteric neurons was investigated by measuring the neurite length. The results demonstrated that BMP­2 regulated the differentiation of primary enteric neurons and increased the length of neurites compared with the control group. In addition, the effect of BMP­2 on the expression of nNOS was also investigated in primary enteric neurons and the Smad1 signal transduction pathway by western blot analysis, reverse transcription quantitative polymerase chain reaction and immunofluorescence assay. The results suggested that BMP­2 promoted the expression of nNOS in primary myenteric neurons and induced phosphorylation of Smad1. These data indicate a new role for BMP­2 as an important transcriptional cofactor that regulates the differentiation of nitrergic enteric neurons through the Smad1 pathway. Intervention of BMP­2 may be useful for the treatment of STC.