A comparative research on obesity hypertension by the comparisons and associations between waist circumference, body mass index with systolic and diastolic blood pressure, and the clinical laboratory data between four special Chinese adult groups.

BACKGROUND: The obesity-hypertension pathogenesis is complex. From the phenotype to molecular mechanism, there is a long way to clarify the mechanism. To explore the association between obesity and hypertension, we correlate the phenotypes such as the waist circumference (WC), body mass index (BMI), systolic blood pressure (SB), and diastolic blood pressure (DB) with the clinical laboratory data between four specific Chinese adult physical examination groups (newly diagnosed untreated just-obesity group, newly diagnosed untreated obesity-hypertension group, newly diagnosed untreated just-hypertension group, and normal healthy group), and the results may show something. OBJECTIVE: To explore the mechanisms from obesity to hypertension by analyzing the correlations and differences between WC, BMI, SB, DB, and other clinical laboratory data indices in four specific Chinese adult physical examination groups. METHODS: This cross-sectional study was conducted from September 2012 to July 2014, and 153 adult subjects, 34 women and 119 men, from 21 to 69 years, were taken from four characteristic Chinese adult physical examination groups (newly diagnosed untreated just-obesity group, newly diagnosed untreated obesity-hypertension group, newly diagnosed untreated just-hypertension group, and normal healthy group). The study was approved by the ethics committee of Hangzhou Center for Disease Control and Prevention. WC, BMI, SB, DB, and other clinical laboratory data were collected and analyzed by SPSS. RESULTS: Serum levels of albumin (ALB)，alanine aminotransferase (ALT), low density lipoprotein cholesterol (LDLC), triglyceride (TG), high density lipoprotein cholesterol (HDLC), alkaline phosphatase (ALP), uric acid (Ua), and TC/HDLC (odds ratio) were statistically significantly different between the four groups. WC statistically significantly positively correlated with BMI, ALT, Ua, and serum levels of glucose (GLU), and TC/HDLC, and negatively with ALB, HDLC, and serum levels of conjugated bilirubin (CB). BMI was statistically significantly positively related to ALT, Ua, LDLC, WC, and TC/HDLC, and negatively to ALB, HDLC, and CB. DB statistically significantly positively correlated with ALP, BMI, and WC. SB was statistically significantly positively related to LDLC, GLU, serum levels of fructosamine (FA), serum levels of the total protein (TC), BMI, and WC. CONCLUSION: The negative body effects of obesity are comprehensive. Obesity may lead to hypertension through multiple ways by different percents. GGT, serum levels of gamma glutamyltransferase; ALB, serum levels of albumin; ALT, serum levels of alanine aminotransferase; LDLC, serum levels of low density lipoprotein cholesterol; TG, serum levels of triglyceride; HDLC, serum levels of high density lipoprotein cholesterol; FA, serum levels of fructosamine; S.C.R, serum levels of creatinine; IB, serum levels of indirect bilirubin; ALP, serum levels of alkaline phosphatase; CB, serum levels of conjugated bilirubin; UREA, Urea; Ua, serum levels of uric acid; GLU, serum levels of glucose; TC, serum levels of the total cholesterol; TB, serum levels of the total bilirubin; TP, serum levels of the total protein; TC/HDLC, TC/HDLC ratio.

[Study on Nondestructive Detecting Gannan Navel Pesticide Residue with Hyperspectral Imaging Technology].

Hyperspectral imaging technology is a rapid, non-destructive, and non-contact technique which integrates spectroscopy and digital imaging to simultaneously obtain spectral and spatial information. Hyperspectral images are made up of hundreds of contiguous wavebands for each spatial position of a sample studied and each pixel in an image contains the spectrum for that specific position. With hyperspectral imaging, a spectrum for each pixel can be obtained and a gray scale image for each narrow band can be acquired, enabling this system to reflect componential and constructional characteristics of an object and their spatial distributions. In this study, hyperspectral image technology is used to discuss the application of hyperspectral imaging detection technology of Jiangxi navel orange surface of different concentrations of pesticide residue changes with time relationship. The pesticide was diluted to 1 : 20, 1 : 100 and 1 : 1 000 solution with distilled water. A 1×2 matrix of dilutions was applied to each of 30 cleaned samples with different density pesticide residue. After 0, 4 and 20 d respectively, hyperspectral images in the wavelength range from 900 to 1 700 nm are taken. The characteristic wavelengths are achieved by using principal component analysis (PCA) and the PC-2 image based on PCA using characteristic wavelengths (930, 980, 1 100, 1 210, 1 300, 1 400, 1 620 and 1 680 nm) as the classification and recognition of image. Based on these 8 characteristic wavelengths for a second principal component analysis, the application of PC-2 image and appropriate image processing methods for different concentrations and different days of placing pesticide residues in non-destructive testing were applied. Using hyperspectral imaging technology to detect three periods a higher dilution of the fruit surface pesticide residues are more obvious. This research shows that the technology of hyperspectral imaging can be used to effectively detect pesticide residue on Gannan navel surface.

[Fast detection of sugar content in fruit vinegar using NIR spectroscopy].

For the fast and exact detection of sugar content of fruit vinegar, near infrared (NIR) spectroscopy technique combined with least squares support vector machines (LS-SVM) algorithm was used to build the prediction model of sugar content in the present research. NIR spectroscopy is a nondestructive, fast and accurate technique for the measurement of chemical compo nents based on overtone and combination bands of specific functional groups. The pivotal step for spectroscopy technique is how to extract quantitative data from mass spectral data and eliminate spectral interferences. Principal component analysis (PCA) is a method which has been widely used in the spectroscopic analysis, and LS-SVM is a new data mining algorithm developed from the machine learning community. In the present study, they were used for the spectroscopic analysis. First, the near infrared transmittance spectra of three hundred samples were obtained, then PCA was applied for reducing the dimensionality of the original spectra, and six principal components (PCs) were selected according the accumulative reliabilities (AR). The six PCs could be used to replace the complex spectral data. The three hundred samples were randomly separated into calibration set and validation set. Least squares support vector machines (LS-SVM) algorithm was used to build prediction model of sugar content based on the calibration set, then this model was employed for the prediction of the validation set. Correlation coefficient (r) of prediction and root mean square error prediction (RMSEP) were used as the evaluation standards, and the results indicated that the r and RMSEP for the prediction of sugar content were 0.9939 and 0.363, respectively. Hence, PCA and LS-SVM model with high prediction precision could be applied to the determination of sugar content in fruit vinegar.