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.
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 TGFβ1 and SMAD4, the downstream gene of TGFβ1. In TGFβ1-induced fibrosis model of primary human oral submucous fibroblast, PTMA knockdown reversed TGFβ1-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 TGFβ1-induced fibrosis process. Taken together, PTMA is involved in TGFβ1-induced fibrosis in the primary human submucous fibroblast by regulating the expression of ECM-related markers and the downstream genes of TGFβ1. In conclusion, PTMA presents an essential autoantigen during OSF process; targeting PTMA might be a promising strategy for OSF treatment.
Abstracts Azodicarbonamide is wildly used as a flour gluten fortifier in many countries, but according to the research results of toxicology of azodicarbonamide, its acute toxicity is slightly toxic. A dosage of 10 g/kg is lethal to mice, and it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour; hence, there is a need to identify the concentration of azodicarbonamide in flour quickly. Compared to traditional methods like highperformance liquid chromatography, the core advantage of near-infrared reflectance spectroscopy is rapid and economical. Spectral data in a range of 850 to 1050 nm were obtained by scanning 101 samples with different concentrations. The Mahalanobis distance method was used to distinguish abnormal spectral data, and the correlation coefficient method was used to choose characteristic wave bands. Radial basis function in combination with near-infrared reflectance spectroscopy was used to establish models in accordance. The limit of quantitation and the limit of detection of the first model were 72 and 15 mg/kg, respectively. Through analyzing the relative tolerances of predictive values and true values, the method of secondary modeling was proposed for low-concentration (72 mg/kg) samples. The predictions showed that nearinfrared reflectance spectroscopy could be used for detecting the content of azodicarbonamide added to flour.
This research proposed to design a prediction model based on Radial Basis Function (RBF) neural network and Near Infrared Reflectance Spectroscopy (NIRS) in detecting concentration of Benzoyl Peroxide (BPO) in flour. Near Infrared Reflectance (NIR) spectra acquired from 100 different concentration samples were pre-processed by Standard Normal Variate (SNV) method, detection of leverage and student residual. NIRS models were designed to predict BPO in the 36 samples by means of Partial Least Squares (PLS), BP neural network and RBF, respectively. The Downloaded by [Deakin University Library] at 13:53 12 August 2015A c c e p t e d M a n u s c r i p t 2 results demonstrated that the RBF model, with prediction correlation coefficient (R), root mean squared error of prediction (RMSEP) and ratio of performance to standard deviate (RPD) reaching 0.9937, 15.5095 and 8.8216, respectively, had optimal prediction accuracy and feasibility providing quality evaluation and dynamic monitoring service for quality inspection department and consumers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.