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ObjectiveTo screen the differential markers by analyzing volatile components in Dalbergia odorifera and its counterfeits， in order to provide reference for authentication of D. odorifera. MethodThe volatile components in D. odorifera and its counterfeits were detected by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the GC conditions were heated by procedure（the initial temperature of the column was 50 ℃， the retention time was 1 min， and then the temperature was raised to 300 ℃ at 10 ℃ for 10 min）， the carrier gas was helium， and the flow rate was 1.0 mL·min-1， the split ratio was 10∶1， and the injection volume was 1 mL. The MS conditions used electron bombardment ionization（EI） with the scanning range of m/z 35-550. The compound species were identified by database matching， the relative content of each component was calculated by the peak area normalization method， and principal component analysis（PCA）， orthogonal partial least squares-discrimination analysis（OPLS-DA） and cluster analysis were performed on the detection results by SIMCA 14.1 software， and the differential components of D. odorifera and its counterfeits were screened out according to the variable importance in the projection（VIP） value>2 and P<0.05. ResultA total of 26， 17， 8， 22， 24 and 7 volatile components were identified from D. odorifera， D. bariensis， D. latifolia， D. benthamii， D. pinnata and D. cochinchinensis， respectively. Among them， there were 11 unique volatile components of D. odorifera， 6 unique volatile components of D. bariensis， 3 unique volatile components of D. latifolia， 6 unique volatile components of D. benthamii， 8 unique volatile components of D. pinnata， 4 unique volatile components of D. cochinchinensis. The PCA results showed that， except for D. latifolia and D. cochinchinensis， which could not be clearly distinguished， D. odorifera and other counterfeits could be distributed in a certain area， respectively. The OPLS-DA results showed that D. odorifera and its five counterfeits were clustered into one group each， indicating significant differences in volatile components between D. odorifera and its counterfeits. Finally， a total of 31 differential markers of volatile components between D. odoriferae and its counterfeits were screened. ConclusionHS-GC-MS combined with SIMCA 14.1 software can systematically elucidate the volatile differential components between D. odorifera and its counterfeits， which is suitable for rapid identification of them.
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ObjectiveTo establish the identification method of Dalbergiae Odoriferae Lignum（DOL） and its counterfeits by nuclear magnetic resonance hydrogen spectrum（1H-NMR） combined with multivariate statistical analysis. Method1H-NMR spectra of DOL and its counterfeits were obtained by NMR， and the full composition information was established and transformed into a data matrix， and the detection conditions were as follows：taking dimethyl sulfoxide-d6（DMSO-d6） containing 0.03% tetramethylsilane（TMS） as the solvent， the constant temperature at 298 K（1 K=-272.15 ℃）， pulse interval of 1.00 s， spectrum width of 12 019.23 Hz， the scanning number of 16 times， and the sampling time of 1.08 s. Similarity examination and hierarchical cluster analysis（HCA） were performed on the data matrix of DOL and its counterfeits， and orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to analyze the data matrix and identify the differential components between them. In the established OPLS-DA category variable value model， the category variable value of DOL was set as 1， and the category variable value of the counterfeits was set as 0， and the threshold was set as ±0.3， in order to identify the commercially available DOL. The OPLS-DA score plot was used to determine the types of counterfeits in commercially available DOL， and it was verified by thin layer chromatography（TLC）. ResultThe results of similarity analysis and HCA showed that there was a significant difference between DOL and its counterfeits. OPLS-DA found that the differential component between DOL and its counterfeits was trans-nerolidol. The established category variable value model could successfully identify the authenticity of the commercially available DOL. The results of the OPLS-DA score plot showed that there were heartwood of Dalbergia pinnata and D. cochinchinensis in the commercially available DOL， and were consistent with the TLC verification results. ConclusionThere is a phenomenon that heartwood of D. pinnata and D. cochinchinensis are sold as DOL in the market. 1H-NMR combined with multivariate statistical analysis can effectively distinguish DOL and its counterfeits， which can provide a reference for the identification of them.
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Objective To investigate the difference between Han nationality residents and the Hani nationality residents in southern Yunnan about the effects of stroke risk factors on cerebral vascular hemodynamic indicators.Methods In the cohort crowds,Hani nationality outpatient patients were selected as the Hani nationality group,the Han nationality outpatient patients with the same gender and similar age were randomly selected as the Han nationality group with the ratio of nearly 1 ∶ 3.The risk factors of stroke and the differences of the hemodynamic indicators were compared between the two groups.Results The dyslipidemia proportion of the Hani nationality group was lower than that of the Han nationality group [48 cases (11.5 ％) vs.257 cases (17.5 ％),x2 =8.867,P =0.003],and the drinking proportion of the Hani nationality group was higher than that of the Han nationality group [79 cases (18.9％) vs.214 cases(14.6％),x2 =4.498,P =0.034],the other risk factors exposure rates had no statistically significant differences between the two groups (all P ＞ 0.05).The ratio of numbers of cerebral vascular hemodynamic indicators below 75 points(optimum truncation points) in the Hani nationality group and the Han nationality group were 30.3％ and 31.9％,respectively,the proportion of the two groups of integral value distribution had statistically significant difference (x2 =12.614,P ＜ 0.01);Vmean (left:t =0.632,P =0.527;right:t =0.642,P =0.532),Vmin (left:t =-0.349,P =0.727;right:t =-0.540,P =0.589),Wv(left:t =0.297,P =0.767;right:t =1.119,P =0.263),Zcv(left:t =0.297,P =0.767;right:t =1.247,P =0.213),Rv(left:t =0.837,P =0.403;right:t =1.222,P =0.222),DR(left:t =0.720,P =0.482;right:t =0.396,P =0.692),Cp(left:t =0.614,P =0.539;right:t =1.486,P =0.138) and other indicators of the two groups had no statistically significant differences.Conclusion The Hani nationality residents'stroke risk factors and CVHI integral distribution in different age groups are different from Han people in southern Yunnan.
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Objective To investigate the exposure to stroke risk factors, the proportion of high?risk individuals, and the relationship between risk factor exposure and impaired cerebrovascular hemodynamics in patients at Pu'er People's Hospital. Methods Between April 2014 and June 2017, this study enrolled inpatients and outpatients of the neurology department of Pu'er People's Hospital who underwent cerebrovascular hemodynamic examinations to evaluate stroke risk. A total of 30 103 (12 793 males and 17 310 females) participants aged 22 to 99 (53.3±14.5) years were included. The distribution of exposure rates for stroke risk factors and proportion of high?risk individuals were determined according to age and sex. The relationships between integral scores of cerebrovascular hemodynamics and exposure to risk factors were also analyzed. Results Exposure rates for risk factors of hypertension, dyslipidemia, diabetes, heart disease, stroke family history, cigarette smoking, alcohol drinking, and overweight or obesity in males were 36.6%, 11.4%, 8.8%, 5.9%, 7.0%, 41.6%, 37.8%, and 51.4%, respectively. The exposure rates in females were 31.2%, 11.3%, 7.6%, 5.5%, 6.8%, 0.7%, 1.6%, and 48.8%, respectively. Differences between age groups for all risk factors were significant (P<0.01). Exposure rates for hypertension, diabetes, cigarette smoking, alcohol drinking, and overweight or obesity in males were significantly higher than in females (P<0.01). High?risk males and females accounted for 41.8% and 35.6% (χ2=119.82, P<0.01) and the rates in both groups increased significantly with age (χ2=1 838.2, 2 881.5, P<0.01). Risk factors including age, hypertension, dyslipidemia, diabetes, heart disease, stroke family history, alcohol drinking, and body mass index were independent predictors of increased individual risk of stroke. Conclusions Exposure levels for stroke risk factors and the proportion of individuals at high risk of stroke were relatively high in the hospital population. Cerebrovascular dysfunction is closely related to exposure to common risk factors for stroke.
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Objective To explore the correlation study of cerebral vascular hemo -dynamic index(CVHI) with blood pressure,body mass index (BMI)etc.in outpatients.Methods Participants were outpatients with non -invasive CVHI test,who were selected as study subjects.Height,weight,blood pressure and other general information were investigated and the distribution characteristics of blood pressure,BMI and CVHI were compared in different age and gender groups.Results A total of 28 616 subjects were included in the study.The age range was 11 -99 years. Means of CVHI integral score in males and females were (77.78 ±28.44)points and (79.18 ±27.8)points(t =4.275,P <0.01),and abnormality rates of the score(<75 points)were 30.1% and 28.1%(χ2 =13.444,P <0.01),the differences were statistically significant.CVHI score was decreased and the abnormal rate was increased with aging.Systolic blood pressure was elevated with aging and diastolic blood pressure was higher in middle age subjects (40 -65 years).The proportions of over weighted and obesity in male were 37.5% and 15.4%,which in female were 33.2% and 14.9%(χ2 =70.661,P <0.01).It was especially higher in middle age subjects.Conclusion Degree of injury of cerebrovascular function and level of systolic blood pressure are along with aging.Means of BMI and diastolic blood pressure is higher in middle age population.The rate of high risk individuals in outpatients is about 30%.