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ObjectiveTo explore the content difference of gallic acid，protocatechuic acid，catechin，total flavonoids， and total polysaccharides in Cynomorii Herba between different producing areas and the correlation between effective components and environmental factors. MethodNinety-five batches of Cynomorii Herba samples were collected from 12 major producing areas in five provinces （autonomous regions）， including Gansu，Inner Mongolia，Xinjiang，Qinghai， and Ningxia，and the geographical-climatic factors such as altitude，longitude and latitude，climate type，annual average frost-free period，annual rainfall，annual sunshine hours，annual average temperature， and annual average evaporation were recorded. The content of gallic acid，protocatechuic acid， and catechin in Cynomorii Herba was determined by high-performance liquid chromatography （HPLC），and the mathematical model of the correlation between the content of chemical components and environmental factors was optimized and established. ResultPearson correlation analysis showed that protocatechuic acid content in Cynomorii Herba increased with the increase in annual average frost-free period and annual average temperature， and catechin content increased with the increase in the annual average frost-free period，annual sunshine hours， and annual average evaporation，while total polysaccharides content decreased with the increase in altitude. Redundancy analysis （RDA） showed that the annual average frost-free period， annual average evaporation，annual sunshine hours， and altitude had great influences on the content of effective components in Cynomorii Herba. Curve fitting showed that the optimal conditions for the growth of Cynomorii Herba were as follows： altitude of 800-2 000 m，annual average frost-free period of 80-110 d， annual rainfall of 110-300 mm，annual sunshine hours of 2 400-3 000 h， annual average temperature of 2.2-8.8 ℃，and annual average evaporation of 1 700-2 500 mm. ConclusionThe content of effective components in Cynomorii Herba is diverse in terms of producing areas and shows a clear response rule to environmental factors. The areas suitable for growing and artificial cultivation introduction are those with high altitude，short annual average frost-free period，low annual rainfall，large average evaporation，long sunshine hours， and low annual average temperature.
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Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder, commonly found in males above the age of 60. Increased life expectancy due to better health care and aging population will see a proportional rise in case of PD in India during next three decades making it a major health problem. Objective: There was no published epidemiological study on PD from south-central India. Hence, a longitudinal study was undertaken by a neurocenter from 2004-2015. Materials and methods: 382 patients with symptoms of PD presented to outpatient department of neurocenter between 2004-2015 were evaluated in relation with various epidemiological parameters. Results: Out of 382 subjects having clinical PD, it was found to be more common in males and in patients above the age of 60 with rural habitation. Conclusion: The present study documents the epidemiological data of PD from south-central India called Deccan plateau (Telangana state). It represents varied geographical, social, economic, cultural ethnicity among rural/urban population. There is low literacy rate, predominant rural population and insufficient health infrastructure where PD poses an invisible but major health problem for policy makers.
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Objective:To discuss the relationship between the geographic environment and blood urea nitrogen(BUN) reference values of the healthy people,and to explore the distributional rule of BUN reference values of the healthy people,and to provide the scientific foundation for establishing the BUN reference value standards of different areas.Methods:A total of 23 geographic factors and 33 521 BUN reference values of healthy adults measured by 403 medical facilities from 23 provinces,4 municipalities and 5 autonomous regions were collected.The spatial autocorrelation analysis was used to determine the spatial autocorrelation of the sample data;the correlation analysis was used to detect the factors which correlated significantly with the BUN reference values;the multiple linear regression,principle component analysis and ridge regression analysis were respectively used to construct the predicted models;the paired-sample t test was used to choose the optimal model;the distribution map of BUN reference values was built by geostatistic analysis.Results:There were 5 geographic factors,latitude(X2),altitude(X3),annual mean temperature(X5),annual mean relative humidity(X6) and annual precipitation(X7),correlated significantly with the BUN reference values.The regression equation of optimal model was Y
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[Objective] To refer a geographical distribution rule of alpha-L-fucosidase (AFU) reference values for the health adults in China via exploring its spatial distribution trend and its correlation with geographical factors.[Methods] A total of 6564 samples of AFU reference values from 66 administrative units in the years 2004-2015 were collected,male and female of which were 3701 cases (56.4％) and 2863 cases (46.3％).A research concerning AFU reference values in whole country were calculated using methods of information content and ridge regression.[Results] AFU reference values for Chinese healthy adults were influenced by geographical factors and presented autocorrelation,and it showed eastern and northern areas were highery than western and southern areas.[Conclusions] AFU reference values have a spatial variation and the regional disparities should be considered in practice.
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Objective: To study the relationship between the lung compliance and the geographic factor in healthy Chinese adults, so as to provide scientific evidence for establishing the standard value of lung compliance for healthy Chinese adults. Methods and Results: We colleted the lung compliance data from 4 489 (2 726 males, 1 763 females) healthy adult Chinese from 46 cities and counties across China. The relationship between lung compliance data and geographic factors (altitude[X1], annual sunshine duration[X2], annual mean air temperature[X3], annual mean relative humidity[X4], and annual precipitation [X5]) were assessed by correlative analysis and co-linearity diagnostics. Then we analyzed the relationship between the reference value and the geographical factors by using the method Principal Component Analysis. The regression equation was: Y = 0.079-0.000 001 66X1-0.000 032 3X2-0.002 21X3+0.001 47X4-0.000 017 4X5±0.021. Conclusion: If geographical factors of an area are obtained, the reference value range of healthy adult lung compliance of this area can be calculated by using the regression equation.
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Objective: To provide a scientific basis for establishing a standard reference value of FEV1. 0% for aged Chinese males. Methods: The reference values of FEV1. 0% of 4 342 healthy aged Chinese males were collected from hospitals, research units, and higher institutions of 72 counties or cities. Correlation analysis, factor analysis and GIS software were used to investigate the relationship between the reference values of FEV1. 0% with six geographical factors. Results: It was found that the reference values of FEV1. 0% in aged Chinese males were significantly associated with geographical factors. Factor analysis and regression analysis yielded a regression equation as: y = 79.279 8 + 0.000 901 5X1 -0. 001 931 4X2 + 0. 001 966 8X 3 - 0.002 615X4+0.001 293 4X5+0.001 650 3X 6 ±1.032 8. The fitting degree between the predicted and the measured values was high. With the aid of geostatistical analysis module of GIS spatial analysis and Kriging interpolation method, we interpolated the geographical distribution map of the reference value of FEV1. 0% in aged Chinese males. Conclusion: The regression equation can be used to accurately calculate the reference value of FEV1. 0% for aged Chinese males, if the geographical values for an area are available. The reference value of FEV1. 0% can also be obtained by the geography Trend-surface distribution map of an area.