Integrated multi-omics analysis reveals gut microbiota dysbiosis and systemic disturbance in major depressive disorder.

Major depressive disorder (MDD) involves systemic changes in peripheral blood and gut microbiota, but the current understanding is incomplete. Herein, we conducted a multi-omics analysis of fecal and blood samples obtained from an observational cohort including MDD patients (n = 99) and healthy control (HC, n = 50). 16S rRNA sequencing of gut microbiota showed structural alterations in MDD, as characterized by increased Enterococcus. Metagenomics sequencing of gut microbiota showed substantial functional alterations including upregulation in the superpathway of the glyoxylate cycle and fatty acid degradation and downregulation in various metabolic pathways in MDD. Plasma metabolomics revealed decreased amino acids and bile acids, together with increased sphingolipids and cholesterol esters in MDD. Notably, metabolites involved in arginine and proline metabolism were decreased while sphingolipid metabolic pathway were increased. Mass cytometry analysis of blood immune cell subtypes showed rises in proinflammatory immune subsets and declines in anti-inflammatory immune subsets in MDD. Furthermore, our findings revealed disease severity-related factors of MDD. Interestingly, we classified MDD into two immune subtypes that were highly correlated with disease relapse. Moreover, we established discriminative signatures that differentiate MDD from HC. These findings contribute to a comprehensive understanding of the MDD pathogenesis and provide valuable resources for the discovery of biomarkers.

Emergence of a novel recombinant of CV-A5 in HFMD epidemics in Xiangyang, China.

BACKGROUND: Hand, foot and mouth disease (HFMD) is caused by a variety of enterovirus serotypes and the etiological spectrum worldwide has changed since a large scale of outbreaks occurred in 1997. METHODS: A large number of clinical specimens of HFMD patients were collected in Xiangyang and genotyping was performed by qRT-PCR, conventional PCR amplification and sequencing. Among the 146 CV-A5 detected cases, the complete genome sequences of representative strains were determined for genotyping and for recombination analysis. RESULTS: It was found that CV-A5 was one of the six major serotypes that caused the epidemic from October 2016 to December 2017. Phylogenetic analyses based on the VP1 sequences showed that these CV-A5 belonged to the genotype D which dominantly circulated in China. Recombination occurred between the CV-A5 and CV-A2 strains with a breakpoint in the 2A region at the nucleotide 3791. CONCLUSIONS: The result may explain the emergence of CV-A5 as one of the major pathogens of HFMD. A multivalent vaccine against HFMD is urgently needed to control the disease and to prevent emerging and spreading of new recombinants.

Epidemical and etiological study on hand, foot and mouth disease following EV-A71 vaccination in Xiangyang, China.

Coxsackievirus A6 (CV-A6) and Coxsackievirus A10 (CV-A10) have been emerging as the prevailing serotypes and overtaking Enterovirus A71 (EV-A71) and Coxsackievirus A16 (CV-A16) in most areas as main pathogens of hand, foot and mouth disease (HFMD) in China since 2013. To investigate whole etiological spectrum following EV-A71 vaccination of approximate 40,000 infants and young children in Xiangyang, enteroviruses were serotyped in 4415 HFMD cases from October 2016 to December 2017 using Real Time and conventional PCR and cell cultures. Of the typeable 3201 specimen, CV-A6 was the predominant serotype followed by CV-A16, CV-A10, CV-A5, CV-A2 and EV-A71 with proportions of 59.54%, 15.31%, 11.56%, 4.56%, 3.78% and 3.03%, respectively. Other 12 minor serotypes were also detected. The results demonstrated that six major serotypes of enteroviruses were co-circulating, including newly emerged CV-A2 and CV-A5. A dramatic decrease of EV-A71 cases was observed, whereas the total cases remained high. Multivalent vaccines against major serotypes are urgently needed for control of HFMD.

Effect of layer-distributed carbon nanotube (CNT) on mechanical and piezoresistive performance of intelligent cement-based sensor.

Agglomerated carbon nanotube (CNT) powder was scattered into a cement paste layer-by-layer to form layer-distributed CNT composite (LDCC) as intelligent cement-based sensor. The characteristic of the CNT agglomerations and its effect on the mechanical and piezoresistive properties of cement paste were investigated in this study, and the results were compared with those of uniformly-dispersed CNT composites (UDCC). Based on the statistics of CNT agglomerations, it was found that the sizes of agglomerations varied from several to dozens of micrometres. The larger sized agglomerations with poorer roundness exhibited a higher possibility to cause the pores or voids accompanied with stress concentration when subjected to external forces. Hence, it is necessary to control the agglomeration sizes to reduce the porosity with edges and corners. The UDCC reached the highest compressive strength, followed by the plain cement paste and then LDCC. The mechanical strength of LDCC decreased with the increase of CNT layers. The piezoresistivity occurred in both the UDCC and LDCC, with the former possessing stable and repeatable performance. In addition, the strain-sensing ability of LDCC with moderate CNT layers presented similar sensing efficiency and repeatability to that of UDCC. The related results provide insight into the intelligent cement-based sensors with layer-distributed CNT and agglomerations, which can improve the efficiency and effectively reduce the cost for practical application.

Tongue color clustering and visual application based on 2D information.

PURPOSE: Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way. METHODS: A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI). RESULTS: The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively. CONCLUSIONS: The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave.

OBJECTIVE: In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). METHOD: The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. RESULT: Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. CONCLUSION: Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.

Tongue Image Database Construction Based on the Expert Opinions: Assessment for Individual Agreement and Methods for Expert Selection.

This study aims at introducing a method for individual agreement evaluation to identify the discordant raters from the experts' group. We exclude those experts and decide the best experts selection method, so as to improve the reliability of the constructed tongue image database based on experts' opinions. Fifty experienced experts from the TCM diagnostic field all over China were invited to give ratings for 300 randomly selected tongue images. Gwet's AC1 (first-order agreement coefficient) was used to calculate the interrater and intrarater agreement. The optimization of the interrater agreement and the disagreement score were put forward to evaluate the external consistency for individual expert. The proposed method could successfully optimize the interrater agreement. By comparing three experts' selection methods, the interrater agreement was, respectively, increased from 0.53 [0.32-0.75] for original one to 0.64 [0.39-0.80] using method A (inclusion of experts whose intrarater agreement>0.6), 0.69 [0.63-0.81] using method B (inclusion of experts whose disagreement score="0"), and 0.76 [0.67-0.83] using method C (inclusion of experts whose intrarater agreement>0.6& disagreement score="0"). In this study, we provide an estimate of external consistency for individual expert, and the comprehensive consideration of both the internal consistency and the external consistency for each expert would be superior to either one in the tongue image construction based on expert opinions.

Identification and profile of microRNAs in Xiang pig testes in four different ages detected by Solexa sequencing.

To further understand the role of microRNA (miRNA) during testicular development, we constructed four small RNA libraries from the testes of the Chinese indigenous Xiang pig at four different ages, which were sequenced using high-throughput Solexa deep sequencing methods. It yielded over 23 million high-quality reads and 1,342,579 unique sequences. At two and three months of age, the proportion which represented miRNAs was the most abundant class of small RNAs, but it was gradually replaced by the category that represented piRNAs in adult testes. We identified 543 known and homologous conserved porcine miRNAs and 49 potential novel miRNAs. There were 306 known miRNAs which were co-expressed in four libraries. Six miRNAs and three potential novel miRNAs were validated in testes and sperms of Xiang pig by RT-qPCR method. Many clusters of mature miRNA variants were observed, in which let-7 family was the most abundant one. After comparison among libraries, 204 miRNAs were identified as being differentially expressed and likely involved in the development and spermatogenesis of pig testes. This work presented a general genome-wide expression profile of the testes-expressed small RNAs in different ages of pig testes. Our results suggested that miRNAs performed a role in the regulation of mRNAs in puberty pig testes while piRNAs likely functioned mainly in sexually mature pig testes.