[Prevalence and related factors of visual disability, hearing disability and comorbidity of visual and hearing disability among the elderly in China].

Objective: To estimate the prevalence of visual disability, hearing disability and comorbidity of visual and hearing disability among the elderly in China, and explore the related factors of comorbidity of visual and hearing disability in the elderly. Methods: This was a cross-sectional study. Based on the Second China National Sample Survey on Disability in 2006, the data of the elderly with visual and hearing disability were extracted and combined for descriptive analysis. Meanwhile, multivariate logistic regression model was used to analyze the related factors of comorbidity of visual and hearing disability among the elderly. Results: A total of 250 752 cases were in the final analysis (119 120 males and 131 632 females), and there were 164 003, 74 156 and 12 593 cases with the age of 65-<75, 75-<85 and ≥ 85 years, respectively. The prevalence of visual disability and hearing disability of the elderly in China was 8.10% (95%CI: 8.00%-8.21%), 13.41% (95%CI: 13.29%-13.54%), respectively, while the prevalence of comorbidity of visual and hearing disability was 1.97% (95%CI: 1.92%-2.02%). The severity of disability of the elderly with comorbidity of visual and hearing disability was higher, and the percentage of mild disabilities (18.31%, 966/5 277) was lower than those with visual (53.06%, 11 208/21 123) or hearing disabilities (32.96%, 11 536/34 995). Moreover, 19.40% (1 024/5 277) of visual or hearing disability occurred in the same year. Multivariate logistic regression analysis showed that education level below primary school (OR=0.65, 95%CI: 0.61-0.70, P<0.001), having a spouse (OR=0.68, 95%CI: 0.64-0.72, P<0.001), living in an urban area (OR=0.77, 95%CI: 0.71-0.82, P<0.001) and having a per capita household income higher than the national average (OR=0.73, 95%CI: 0.68-0.78, P<0.001) were protective factors for comorbidity of visual and hearing disability among the elderly. Conclusions: Visual disability is correlated with hearing disability in the elderly. Attention should be paid to the prevention and control of associated disabilities such as visual and hearing co-disabilities in the elderly population, with emphasis on strengthening publicity and education on prevention and control of visual and hearing disabilities for the elderly who are economically disadvantaged, have no spouse and live in remote areas.

[Current status of blood lipids in people with hypertension and diabetes in Henan province].

Objective: To investigate the blood lipid levels and prevalence of dyslipidemia in people with hypertension and diabetes in Henan province. Methods: From April 2016 to April 2017, multi-stage cluster sampling was adopted to investigate 71 285 local residents aged between 35 and 75 from 6 districts and counties in Henan province including Zhongmu county of Zhengzhou city, Huojia county of Xinxiang city, Hualong district of Puyang city, Qi county of Hebi city, Xigong district of Luoyang city, and Wugang city of Pingdingshan city. Blood samples were collected. According to the diagnostic criteria of hypertension and diabetes, the study population was divided into control group (n=29 427), hypertension group (n=21 965), diabetes group (n=8 009) and hypertension-diabetes group (n=11 884). Comparisons on blood lipid levels and dyslipidemia between 4 groups were performed. Results: The total cholesterol (TC) level of all subjects was 4.37 (3.78, 5.05) mmol/L. The triglyceride (TG) level was 1.27 (0.97, 1.80) mmol/L, the low-density lipoprotein cholesterol (LDL-C) level was 2.34 (1.88, 2.88) mmol/L and the high-density lipoprotein cholesterol (HDL-C) level was 1.31 (1.08, 1.59) mmol/L. Except for the TC level in women aged 65-75 years and LDL-C levels in women aged 55-64 and 65-75 years, there were significant differences in TC, TG, LDL-C and HDL-C levels between subjects of control group, hypertension group, diabetes group, and hypertension-diabetes group in different age ranges (including 35-44, 45-54, 55-64,and 65-75 years) and genders(all P<0.01).Except for the LDL-C and HDL-C in men aged 35-44 years and LDL-C in women aged 65-75 years, there were significant differences in the dyslipidemia rates of TC, TG, LDL-C and HDL-C between subjects of control group, hypertension group, diabetes group and hypertension-diabetes group in different age ranges and genders(P<0.01 or <0.05). After adjusting for age, gender, smoking, drinking, snoring, region, and body mass index, multivariate logistic regression analysis showed that hypertension (OR=1.221, 95%CI 1.113-1.339, P<0.01), diabetes (OR=1.636, 95%CI 1.461-1.833, P<0.01) and hypertension-diabetes (OR=1.832, 95%CI 1.658-2.023, P<0.01) were independent risk factors for TC abnormality. Hypertension (OR=1.566, 95%CI 1.478-1.659, P<0.01), diabetes (OR=2.182, 95%CI 2.031-2.342, P<0.01) and hypertension-diabetes (OR=2.655, 95%CI 2.492-2.829, P<0.01) were also independent risk factors for TG abnormality. Diabetes (OR=1.510, 95%CI 1.309-1.742, P<0.01) and hypertension-diabetes (OR=1.461, 95%CI 1.285-1.661, P<0.01) were independent risk factors for LDL-C abnormality. Diabetes (OR=1.261, 95%CI 1.180-1.346, P<0.01) and hypertension-diabetes (OR=1.195, 95%CI 1.126-1.268, P<0.01) were independent risk factors for HDL-C abnormality. Conclusion: The prevalence of dyslipidemia in patients with hypertension and diabetes is high in Henan province, so adequate blood lipid education and control should be applied to people with risk factors as soon as possible.

Portable fiber-optic immunosensor for detection of methsulfuron methyl.

This fiber optic sensor, based on competitive immunoreactions between coating-haptens and free haptens in solutions with corresponding antibodies, was developed to determine the concentration of the free hapten, methsulfuron-methyl. The ovalbumin (OVA)-methsulfuron-methyl conjugate was immobilized on a microscope slide. Horseradish peroxidase (HRP) labeled goat anti-rabbit IgG was used to generate an optical signal. The portable optical device consisted of a 0.25-W tungsten-halogen light source and a photosensitive diode detector. A typical competitive-binding calibration curve was seen between 0.3 and 100 ng/ml of methsulfuron-methyl. The detection limit for methsulfuron-methyl was 0.1 ng/ml.

Kinetic determination of organic vapor mixtures with single piezoelectric quartz crystal sensor using artificial neural networks.

A single piezoelectric quartz crystal coated with one kind of crown ether was applied to the simultaneous determination of binary acid and amine vapor mixtures. From the adsorption and desorption curves of analytes, which were somewhat different in shape, frequency shifts from ten time windows were taken as inputs for artificial neural networks (ANN). Prediction results were satisfactory for ANN in both sample sets. The average relative errors, for formic acid and acrylic acid were 5%, for n-butylamine and aniline, they were 3% with ANN respectively. The effects of number of neurons in the hidden layer of ANN on the performance of the network are also discussed.

Identification of combustible material with piezoelectric crystal sensor array using pattern-recognition techniques.

A promising way of increasing the selectivity and sensitivity of gas sensors is to treat the signals from a number of different gas sensors with pattern recognition (PR) method. A gas sensor array with seven piezoelectric crystals each coated with a different partially selective coating material was constructed to identify four kinds of combustible materials which generate smoke containing different components. The signals from the sensors were analyzed with both conventional multivariate analysis, stepwise discriminant analysis (SDA), and artificial neural networks (ANN) models. The results show that the predictions were even better with ANN models. In our experiment, we have reported a new method for training data selection, 'training set stepwise expending method' to solve the problem that the network can not converge at the beginning of the training. We also discussed how the parameters of neural networks, learning rate eta, momentum term alpha and few bad training data affect the performance of neural networks.