ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Objective:To investigate the association of body weight and body mass index (BMI) with bone mineral density (BMD) and osteoporotic risk in elderly men with type 2 diabetes mellitus (T2DM).Methods:210 elderly male patients with T2DM admitted to the Department of Endocrinology of the First Hospital of Changsha from June 2017 to May 2018 were selected as the research objects. The height, weight and bone mass index (BMI) were measured. BMDs of the left hip [including femoral neck (FN), greater trochanter (G.T.), intertrochanter (InTro), and total hip (TH)] and lumbar spine (LS) were measured in 210 elderly male patients with T2DM by dual-energy X-ray absorption method. Patients were divided into three groups according to BMI: the overweight group (24.0 kg/m 2≤BMI<28.0 kg/m 2), the obesity (BMI≥28.0 kg/m 2) group, and the normal group (18.5 kg/m 2≤BMI<24.0 kg/m 2). The influence of body weight and BMI on BMD and osteoporotic risk in these elderly men with T2DM was analyzed. Results:The BMDs in various sites of the hip of the overweight group and obesity group were higher compared with those in the normal weight group ( P<0.05). There was a positive correlation between weight and BMI with BMDs in various sites of the hip femoral neck (including FN, G. T., InTro, and TH) ( r=0.239-0.427, P<0.05). All patients were divided into different tertiles (T1-T3) stratified by weight and BMI respectively. The BMDs in various sites of the hip increased with tertiles stratified by weight ( P<0.05). The TH-BMD also increased with tertiles stratified by BMI ( P<0.05). The odd ratios ( OR) were calculated using T3 as the control group and T1 as the case group, using T2 as the control group and T1 as the case group, respectively. The osteoporotic risks of T1/T3, T1/T2 at FN stratified by weight were significantly increased by 4.50 times ( OR=4.50, 95% CI: 1.41-14.35) and 9.27 times ( OR=9.27, 95% CI: 2.03-42.30); The osteoporotic risks of T1/T3, T1/T2 at TH were significantly increased by 3.25 times ( OR=3.25, 95% CI: 1.10-9.59) and 8.50 times ( OR=8.50, 95% CI: 1.85-38.99). The osteoporotic risks of T1/T3, T1/T2 at FN stratified by BMI respectively were significantly increased by 4.13 times ( OR=4.13, 95% CI: 1.28-13.25) and 5.58 times ( OR=5.58, 95% CI: 1.53-20.42); while the osteoporotic risks of T1/T3, T1/T2 at TH stratified by BMI were not significantly increased ( P>0.05). There was no statistically significant difference in BMDs and the osteoporotic risks of the LS among T1, T2, and T3, regardless of stratified by weight or BMI ( P>0.05). Conclusions:For elderly males with T2DM, weight and BMI are important factors affecting BMDs in the hip, and also affecting the osteoporotic risks of the hip, especially that of FN. Osteoporotic risks of the FN decrease with the increase of weight and BMI within a certain range.

Objective:To explore the effect of formative evaluation based on Rain Classroom on clinical skills teaching.Method:s A total of 70 students in clinical medicine from Grade 2016 were enrolled and divided into experimental group ( n=35) and control group ( n=35). The experimental group adopted Rain Classroom combined with formative evaluation, while the control group used conventional teaching method and evaluation. Test scores of the two groups were compared and students' satisfaction was collected via questionnaire. T-test was performed using SPSS 17.0. Result:The total score of clinical skills test in the experimental group was (84.11±7.76), which was significantly higher than that in the control group (74.37±12.58), and the difference was statistically significant ( P<0.05). According to the questionnaire survey, the experimental group was significantly better than the control group in terms of enhancing learning interest, improving knowledge comprehension, clinical skills, clinical thinking and analytical ability, as well as better satisfaction towards teaching ( P<0.05). Conclusion:Formative evaluation based on Rain Classroom is helpful to improve the teaching effect on clinical skills and comprehensive abilities of students, so as to promote their overall development.