PTMA, a new identified autoantigen for oral submucous fibrosis, regulates oral submucous fibroblast proliferation and extracellular matrix.

Oral submucous fibrosis (OSF) is a chronic, insidious disease. The presence of autoantibodies in sera of OSF patients is the most characteristic and direct evidence of OSF being an autoimmune disease. To identify the specific autoantigens which could contribute to antibody production, the Human Proteome Microarrays composed of 19000 full-length unique proteins were employed. 45 proteins correlated with OSF were identified. To validate these results, we used ELISA to validate 28 OSF-associated autoantigens in extended samples. 8 autoantigens were positive in OSF serum with high frequency compared to the healthy controls. Moreover, the mRNA expression of 8 candidates was up-regulated in OSF oral submucous tissues; among them, the protein level of PTMA, the one with the highest positive frequency, was also increased. Through searching the Bioinformatics Public Database and performing the Spearman's rank correlation analysis, we observed that PTMA was positively correlated with fibrosis-related TGFß1 and SMAD4, the downstream gene of TGFß1. In TGFß1-induced fibrosis model of primary human oral submucous fibroblast, PTMA knockdown reversed TGFß1-induced fibrosis process through inhibiting the cell viability and proliferation of fibroblast, reducing the protein levels of PTMA, Collagen I, α-SMA and MMP9 and increasing the protein levels of SMAD4. In contrast, PTMA overexpression enhanced TGFß1-induced fibrosis process. Taken together, PTMA is involved in TGFß1-induced fibrosis in the primary human submucous fibroblast by regulating the expression of ECM-related markers and the downstream genes of TGFß1. In conclusion, PTMA presents an essential autoantigen during OSF process; targeting PTMA might be a promising strategy for OSF treatment.

Application of Visible/Near-Infrared Spectroscopy in the Prediction of Azodicarbonamide in Wheat Flour.

Azodicarbonamide is wildly used in flour industry as a flour gluten fortifier in many countries, but it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour. Applying a rapid, convenient, and noninvasive technique in food analytical procedure for the safety inspection has become an urgent need. This paper used Vis/NIR reflectance spectroscopy analysis technology, which is based on the physical property analysis to predict the concentration of azodicarbonamide in flour. Spectral data in range from 400 to 2498 nm were obtained by scanning 101 samples which were prepared using the stepwise dilution method. Furthermore, the combination of leave-one-out cross-validation and Mahalanobis distance method was used to eliminate abnormal spectral data, and correlation coefficient method was used to choose characteristic wavebands. Partial least squares, back propagation neural network, and radial basis function were used to establish prediction model separately. By comparing the prediction results between 3 models, the radial basis function model has the best prediction results whose correlation coefficients (R), root mean square error of prediction (RMSEP), and ratio of performance to deviation (RPD) reached 0.99996, 0.5467, and 116.5858, respectively. PRACTICAL APPLICATION: Azodicarbonamide has been banned or limited in many countries. This paper proposes a method to predict azodicarbonamide concentrate in wheat flour, which will be used for a rapid, convenient, and noninvasive detection device.

Association between chromosomal aberration of COX8C and tethered spinal cord syndrome: array-based comparative genomic hybridization analysis.

Copy number variations have been found in patients with neural tube abnormalities. In this study, we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents. Of eight copy number variations, four were non-polymorphic. These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes, and microcephaly. Gene function enrichment analysis revealed that COX8C, a gene associated with metabolic disorders of the nervous system, was located in the copy number variation region of Patient 1. Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome. Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease.

[Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy].

Grain hardness is an important quality parameter of wheat which has great influence on the classification, usage and composition research of wheat. To achieve rapid and accurate detection of wheat hardness, radial basis function (RBF) neural network model was built to predict the hardness of unknown samples on the basis of analyzing the absorptive characteristics of the composition of wheat grain in infrared, besides, the effects of different spectral pretreatment methods on the predictive accuracy of models were emphatically analyzed. 111 wheat samples were collected from major wheat-producing areas in China; then, spectral data were obtained by scanning samples. Mahalanobis distance method was used to identify and eliminated abnormal spectra. The optimized method of sample set partitioning based on joint X-Y distance (SPXY) was used to divide sample set with the number of calibration set samples being 84 and prediction set samples being 24. Successive projections algorithm (SPA) was employed to extract 47 spectral features from 262. SPA, first derivatives, second derivatives, standard normal variety (SNV) and their combinations were applied to preprocess spectral data, and the interplay of different prediction methods was analyzed to find the optimal prediction combination. Radial basis function (RBF) was built with preprocessed spectral data of calibration set being as inputs and the corresponding hardness data determined via hardness index (HI) method being as outputs. Results showed that the model got the best prediction accuracy when using the combination of SNV and SPA to preprocess spectral data, with the discriminant coefficient (R2), standard error of prediction (SEP) and ratio of performance to standard deviate (RPD) being 0.844, 3.983 and 2.529, respectively, which indicated that the RBF neural network model built based on visible-near infrared spectroscopy (Vis-NIR) could accurately predict wheat hardness, having the advantages of easy, fast and nondestructive compared with the traditional method. It provides a more convenient and practical method for estimating wheat hardness.