【目的】探究非特异性腰痛(nonspecific low back pain,NLBP)患者的中医体质类型及其黄韧带厚度与年龄、体质量指数(body mass index,BMI)、性别、中医体质类型、有无糖尿病、高血压分级的相关性。【方法】选取2023年1月~2023年6月广东...【目的】探究非特异性腰痛(nonspecific low back pain,NLBP)患者的中医体质类型及其黄韧带厚度与年龄、体质量指数(body mass index,BMI)、性别、中医体质类型、有无糖尿病、高血压分级的相关性。【方法】选取2023年1月~2023年6月广东省第二中医院收治的60例NLBP患者为研究对象,辨别患者的中医体质类型,测量电子计算机断层扫描(CT)图像腰椎4/5节段(L4/5)椎间盘水平黄韧带的厚度,记录患者的年龄、性别、中医体质类型、BMI、有无糖尿病、高血压分级,通过相关分析、线性回归分析探讨NLBP患者黄韧带厚度的相关影响因素。【结果】(1)60例NLBP患者黄韧带厚度平均为(2.60±0.72)mm。(2)NLBP患者的中医体质呈4类,其中以血瘀质分布最多,达21例(35.0%),其他从高到低依次为湿热质19例(31.7%)、痰湿质12例(20.0%)、气虚质8例(13.3%)。(3)相关分析结果显示:NLBP患者的BMI、性别、中医体质类型、有无糖尿病对黄韧带厚度的影响差异无统计学意义(P>0.05),而不同年龄、高血压分级对黄韧带厚度的影响差异有统计学意义(P<0.01)。(4)线性回归分析结果显示:不同年龄对黄韧带厚度的影响差异有统计学意义(b=0.034,t=6.282,P<0.01),而不同高血压分级对黄韧带厚度的影响差异无统计学意义(P>0.05)。【结论】NLBP患者的中医体质类型以血瘀质为主,其黄韧带厚度受年龄影响较为显著,而高血压可能是潜在的黄韧带厚度的影响因素。展开更多
Daily meteorological data are the critical inputs for distributed hydrological and ecological models. This study modified mountain microclimate simulation model （MTCLIM） with the data from 19 weather stations, and c...Daily meteorological data are the critical inputs for distributed hydrological and ecological models. This study modified mountain microclimate simulation model （MTCLIM） with the data from 19 weather stations, and compared and validated two methods （the MTCLIM and the modified MTCLIM） in the Qilian Mountains of Northwest China to estimate daily temperature （i.e., maximum temperature, minimum temperature） and precipitation at six weather stations from i January 2000 to 31December 2009. The algorithm of temperature in modified MTCLIM was improved by constructing the daily linear regression relationship between temperature and elevation, aspect and location information. There are two steps to modify the MTCLIM to predict daily precipitation： firstly, the linear regression relationship was built between annual average precipitation and elevation, location, and vegetation index; secondly, the distance weight for measuring the contribution of each weather station on target point was improved by average wind direction during the rainy season. Several regression analysis and goodness-of-fit indices （i.e., Pearson＇s correlation coefficient, coefficient of determination, mean absolute error, root-mean-square error and modelingefficiency） were used to validate these estimated values. The result showed that the modified MTCLIM had a better performance than the MTCLIM. Therefore, the modified MTCLIM was used to map daily meteorological data in the study area from 2000 to 2009. These results were validated using weather stations with short time data and the predicted accuracy was acceptable. The meteorological data mapped could become inputs for distributed hydrological and ecological models applied in the Qilian Mountains.展开更多
文摘【目的】探究非特异性腰痛(nonspecific low back pain,NLBP)患者的中医体质类型及其黄韧带厚度与年龄、体质量指数(body mass index,BMI)、性别、中医体质类型、有无糖尿病、高血压分级的相关性。【方法】选取2023年1月~2023年6月广东省第二中医院收治的60例NLBP患者为研究对象,辨别患者的中医体质类型,测量电子计算机断层扫描(CT)图像腰椎4/5节段(L4/5)椎间盘水平黄韧带的厚度,记录患者的年龄、性别、中医体质类型、BMI、有无糖尿病、高血压分级,通过相关分析、线性回归分析探讨NLBP患者黄韧带厚度的相关影响因素。【结果】(1)60例NLBP患者黄韧带厚度平均为(2.60±0.72)mm。(2)NLBP患者的中医体质呈4类,其中以血瘀质分布最多,达21例(35.0%),其他从高到低依次为湿热质19例(31.7%)、痰湿质12例(20.0%)、气虚质8例(13.3%)。(3)相关分析结果显示:NLBP患者的BMI、性别、中医体质类型、有无糖尿病对黄韧带厚度的影响差异无统计学意义(P>0.05),而不同年龄、高血压分级对黄韧带厚度的影响差异有统计学意义(P<0.01)。(4)线性回归分析结果显示:不同年龄对黄韧带厚度的影响差异有统计学意义(b=0.034,t=6.282,P<0.01),而不同高血压分级对黄韧带厚度的影响差异无统计学意义(P>0.05)。【结论】NLBP患者的中医体质类型以血瘀质为主,其黄韧带厚度受年龄影响较为显著,而高血压可能是潜在的黄韧带厚度的影响因素。
基金supported by National Natural Science Foundation of China (Grant Nos.91025015,51178209)
文摘Daily meteorological data are the critical inputs for distributed hydrological and ecological models. This study modified mountain microclimate simulation model （MTCLIM） with the data from 19 weather stations, and compared and validated two methods （the MTCLIM and the modified MTCLIM） in the Qilian Mountains of Northwest China to estimate daily temperature （i.e., maximum temperature, minimum temperature） and precipitation at six weather stations from i January 2000 to 31December 2009. The algorithm of temperature in modified MTCLIM was improved by constructing the daily linear regression relationship between temperature and elevation, aspect and location information. There are two steps to modify the MTCLIM to predict daily precipitation： firstly, the linear regression relationship was built between annual average precipitation and elevation, location, and vegetation index; secondly, the distance weight for measuring the contribution of each weather station on target point was improved by average wind direction during the rainy season. Several regression analysis and goodness-of-fit indices （i.e., Pearson＇s correlation coefficient, coefficient of determination, mean absolute error, root-mean-square error and modelingefficiency） were used to validate these estimated values. The result showed that the modified MTCLIM had a better performance than the MTCLIM. Therefore, the modified MTCLIM was used to map daily meteorological data in the study area from 2000 to 2009. These results were validated using weather stations with short time data and the predicted accuracy was acceptable. The meteorological data mapped could become inputs for distributed hydrological and ecological models applied in the Qilian Mountains.
