OBJECTIVE Based on the Heracles NEO ultra-fast gas phase electronic nose,to analyze the odor composition of Abutili Semen before and after stir-frying,and to establish an effective and rapid identification method of raw and stir-frying Abutili Se-men based on odor.METHODS The decoction pieces of Abutili Semen were prepared by stir-frying method.An ultra-fast gas-phase electronic nose method was established for the detection of Abutili Semen before and after stir-frying,the odor spectrum was col-lected,and the possible odor components and chromatographic peak areas were obtained in combination with the AroChemBase data-base,and analyzed by chemometric model.RESULTS The odor fingerprints of Abutili Semen before and after stir-frying were estab-lished,and 19 odor peaks were matched between Abutili Semen decoction pieces and stir-fried Abutili Semen.The peak areas of 7 odor components,hexanal,2-furanmethanol,2-methyl-2-propanol,2-methylbutanal,3-methylbutanal,2-methylpropanal,2,3,5-trim-ethylpyrazine,all increased after stir-frying,and the VIP values of the peaks were greater than 1(P<0.05),which were presumed to be the markers for the differences in the odors of Abutili Semen before and after stir-frying.CONCLUSION The Heracles NEO ul-tra-fast gas phase electronic nose can quickly identify the odor components of Abutili Semen before and after frying,which can provide new ideas and methods for quality control of Abutili Semen.

Infection Prevention and Control(IPC)programs are crucial for prevention of hospital-associated infec-tions(HAIs),health and safety of patients and health care workers.The major task confronting the health care is to effectively carry out the IPC programs so as to raise the efficiency and quality of management of HAIs.In recent years,the rapid development of artificial intelligence(AI)technology has provided new ideas for the management of HAIs.The application and progress of research on application of AI technologies such as machine learning,nat-ural language processing and computer vision in surveillance,early warning,diagnosis,prevention and control,smart medical equipment and management of antibiotics were reviewed in the article.Meanwhile,the problems and challenges confronting the AI application,such as technical breakthroughs,data privacy and health care work-ers' acceptance,were also expounded.In the future,it is necessary to push forward the technology research and development,establish the safe and reliable data management system,increase the experience of health care work-ers and energize the interdisciplinary cooperation on IPC.

OBJECTIVE Based on the Heracles NEO ultra-fast gas phase electronic nose,to analyze the odor composition of Abutili Semen before and after stir-frying,and to establish an effective and rapid identification method of raw and stir-frying Abutili Se-men based on odor.METHODS The decoction pieces of Abutili Semen were prepared by stir-frying method.An ultra-fast gas-phase electronic nose method was established for the detection of Abutili Semen before and after stir-frying,the odor spectrum was col-lected,and the possible odor components and chromatographic peak areas were obtained in combination with the AroChemBase data-base,and analyzed by chemometric model.RESULTS The odor fingerprints of Abutili Semen before and after stir-frying were estab-lished,and 19 odor peaks were matched between Abutili Semen decoction pieces and stir-fried Abutili Semen.The peak areas of 7 odor components,hexanal,2-furanmethanol,2-methyl-2-propanol,2-methylbutanal,3-methylbutanal,2-methylpropanal,2,3,5-trim-ethylpyrazine,all increased after stir-frying,and the VIP values of the peaks were greater than 1(P<0.05),which were presumed to be the markers for the differences in the odors of Abutili Semen before and after stir-frying.CONCLUSION The Heracles NEO ul-tra-fast gas phase electronic nose can quickly identify the odor components of Abutili Semen before and after frying,which can provide new ideas and methods for quality control of Abutili Semen.

Infection Prevention and Control(IPC)programs are crucial for prevention of hospital-associated infec-tions(HAIs),health and safety of patients and health care workers.The major task confronting the health care is to effectively carry out the IPC programs so as to raise the efficiency and quality of management of HAIs.In recent years,the rapid development of artificial intelligence(AI)technology has provided new ideas for the management of HAIs.The application and progress of research on application of AI technologies such as machine learning,nat-ural language processing and computer vision in surveillance,early warning,diagnosis,prevention and control,smart medical equipment and management of antibiotics were reviewed in the article.Meanwhile,the problems and challenges confronting the AI application,such as technical breakthroughs,data privacy and health care work-ers' acceptance,were also expounded.In the future,it is necessary to push forward the technology research and development,establish the safe and reliable data management system,increase the experience of health care work-ers and energize the interdisciplinary cooperation on IPC.

Objective To establish a method for simultaneous determination of HPLC fingerprint and multi-target ingredients in Atractylodis Macrocephalae Rhizoma(AMR),in order to provide reference for its quality control.Methods HPLC-DAD multi-wavelength switching method was used to establish fingerprint of AMR,similarity evaluation combined with hierarchical clustering analysis(HCA),principal components analysis(PCA)and discriminant analysis of partial least squares(PLS-DA)were used to carry out chemometric study.The contents of differential component such as atractylenolide Ⅰ,Ⅱ,Ⅲ and atractylon were determined simultaneously.Results The HPLC fingerprint of 37 batches of AMR was established.Nine common peaks were marked,and 4 of them were identified as atractylon,atractylenolide Ⅰ,Ⅱ,Ⅲ.The similarity degrees were between 0.539 and 0.996,the quality of AMR from different origin and different batches varies greatly.Atractylon,atractylenolide Ⅰ,Ⅱ,Ⅲ and one unknown component(peak 9)are the important factors affecting the quality of AMR.Conclusion The combination methods of HPLC fingerprint and simultaneous determinations of multiple components are simple,stable,accurate and reliable,which can provide reference for the quality evaluation of AMR and the improvement of quality standard,as well as lay a foundation for the basic research of its pharmacodynamic substances and related compound.

OBJECTIVE To explore the color and odor changes of Salvia miltiorrhiza Bge.slices from different origins,and com-bine modern machine learning technology to achieve rapid differentiation of origins.METHODS Intelligent sensory technology was used to quantify the color and represent the odor of Salvia miltiorrhiza Bge.slices from different geographical origins.Various data a-nalysis methods including principal component analysis(PCA),discriminant analysis,discriminant factor analysis(DFA),component heat maps,correlation analysis,machine learning and so on,were employed to establish a discrimination function for distinguishing the origin of Salvia miltiorrhiza Bge.slices based on color data.RESULTS Classification and screening of odor information led to the i-dentification of 10 differential markers:ethanol,carbon disulfide,cyclopentane,3-methylfuran,propylene glycol,nonane,phenol,1,5-octadienone,1,8-cineole,and sotolon.It was also found that there was a significant correlation between the color and odor of the slices.Furthermore,based on the concept of data fusion,the study established classification models such as subspace clustering,and compared to single-color discriminant analysis,the classification accuracy was improved to 94.4%.CONCLUSION The feasibility and superiority of intelligent sensory technology in classifying the geographical origin of TCM is confirmed,providing new methods and insights for quality control of Salvia miltiorrhiza Bge.slices.

OBJECTIVE To explore the color and odor changes of Salvia miltiorrhiza Bge.slices from different origins,and com-bine modern machine learning technology to achieve rapid differentiation of origins.METHODS Intelligent sensory technology was used to quantify the color and represent the odor of Salvia miltiorrhiza Bge.slices from different geographical origins.Various data a-nalysis methods including principal component analysis(PCA),discriminant analysis,discriminant factor analysis(DFA),component heat maps,correlation analysis,machine learning and so on,were employed to establish a discrimination function for distinguishing the origin of Salvia miltiorrhiza Bge.slices based on color data.RESULTS Classification and screening of odor information led to the i-dentification of 10 differential markers:ethanol,carbon disulfide,cyclopentane,3-methylfuran,propylene glycol,nonane,phenol,1,5-octadienone,1,8-cineole,and sotolon.It was also found that there was a significant correlation between the color and odor of the slices.Furthermore,based on the concept of data fusion,the study established classification models such as subspace clustering,and compared to single-color discriminant analysis,the classification accuracy was improved to 94.4%.CONCLUSION The feasibility and superiority of intelligent sensory technology in classifying the geographical origin of TCM is confirmed,providing new methods and insights for quality control of Salvia miltiorrhiza Bge.slices.

ObjectiveIn order to find a fast odor-based method for the identification of sulfur fumigated Gastrodiae Rhizoma， an ultra-fast gas phase electronic nose technology was used to identify the odors of different degrees of sulfur fumigated Gastrodiae Rhizoma decoction pieces. MethodHeracles NEO ultra-fast gas phase electronic nose was employed to collect gas chromatograms of unsulfured and sulfured with different degrees of Gastrodiae Rhizoma decoction pieces， gas chromatograms were performed under programmed temperature （initial temperature of 40 ℃， 0.2 ℃·s^-1 to 60 ℃， and then 4 ℃·s^-1 to 250 ℃）， the sample volume was 5 mL， the incubation temperature was 65 ℃ and incubation time was 35 min. Kovats retention index and the AroChemBase database were used for qualitative analysis， and stoichiometric analysis was performed on this basis. Principal component analysis （PCA）， discriminant factor analysis （DFA） and partial least squares-discriminant analysis （PLS-DA） models were established to identify the Gastrodiae Rhizoma decoction pieces with different degrees of sulfur fumigation. ResultAccording to the comparative analysis of AroChemBase database， there were significant differences in the odor characteristics of sulfur fumigated and non-sulfur fumigated Gastrodiae Rhizoma， cyclopentane， acetone and heptane might be the odor components to distinguish the degree of sulfur fumigation in Gastrodiae Rhizoma decoction pieces. The identification index of PCA model was 81， the accumulative discriminant index of the discriminating factors was 92.09% in DFA model， the supervisory model interpretation rate of PLS-DA model was 0.963 and the predictive ability parameter was 0.956， indicating that PCA， DFA and PLS-DA models could well distinguish Gastrodiae Rhizoma decoction pieces with different sulfur fumigation degrees. ConclusionHeracles NEO ultra-fast gas phase electronic nose can be used as a rapid method to identify and distinguish Gastrodiae Rhizoma decoction pieces with different levels of sulfur fumigation. Meanwhile， it can provide a rapid， simple and green method and technology for identification of traditional Chinese medicine decoction pieces by sulfur fumigation.

Objective:To explore the reliability and validity of the 10-item Connor-Davidson resilience scale (CD-RISC-10) in Chinese community-dwelling adults.Methods:Using multistage stratified sampling, totally 2 051 community-dwelling adults were selected and they were investigated by the 10-item Connor-Davidson resilience scale, the center for epidemiological studies depression scale (CES-D) and 10-item Kessler psychological distress scale (K10). The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to evaluate the CD-RISC-10 by SPSS 22.0 and AMOS 22.0.A parallel analysis and Velicer's minimum average partial (MAP) test were also supplemented to confirm the EFA-derived structure of the scale.Results:The Cronbach α coefficient of the CD-RISC-10 was 0.94, and the Spearman-Brown reliability was 0.89.The total correlation of the questions ranged from 0.74 to 0.81 (all P<0.01). The exploratory factor analysis retained its single dimension, which can explain 63.24% of the total variance.Confirmatory factor analysis revealed the single factor model fit data adequately ( χ2/ df=4.596, CFI=0.967, TLI=0.973, RMSEA=0.065). The resilience(27.02±13.50) was negatively correlated with depression(9.75±5.24) and psychological distress (19.23±6.37)( r=-0.32, -0.35, both P<0.01). Conclusion:The CD-RISC-10 scale has acceptable reliability and validity, with using easily and conveniently.Therefore, it is an effective tool for measuring the resilience of the community-dwelling adults.

Objective To understand the current prevalence of clonorchiasis in Jiangxi Province. Methods A survey was performed according to the scheme of the 3rd Principal Human Parasites of Jiangxi Province. Based on the ecological regions,a stratified cluster sampling method was applied by the economic and geographic situation. In rural areas,the investigation of C. si?nensis was carried out together with the soil?transmitted helminths investigation,and in the urban areas,the random cluster sam?pling method was applied for the C. sinensis investigation. There were 92 survey sites from 32 counties. The eggs of C. sinensis in stool were examined by Kato?Katz technique,and health knowledge was also investigated by questionnaires in some people at the same time. Results A total of 23 606 sample residents were investigated,and 138 were found infected with C. sinensis, with the infection rate of 0.58%. Light infection was found in most of them. Totally 124 C. sinensis infected persons focused in Xinfeng County,and only a few of infected people scattered in the other counties. In Xinfeng County,851 residents were investi?gated. Among them,the infected people were found in all the age groups except the 0?year age group. The highest infection rate appeared in the 70? years group(24.00%). The male infection rate was 20.29%,which was higher than that of the female (6.25%),showing a statistically significant difference(P<0.01). The infection rate was highest in the population who received high school or technical secondary school education(31.48%). For the occupation distribution,the infection rate was highest in public officers(39.39%). The questionnaire survey showed that the infection rate in the populations in Xinfeng County who had the history of eating raw fish or raw shrimp was 33.15%. Conclusions The distribution of C. sinensis infection presents a region?al aggregation in Xinfeng County,but in other areas,the distribution is sporadic. It is necessary to continue to carry out the para?sitic disease screening,and in Xinfeng County,it is necessary to strengthen the comprehensive prevention and control interven?tion.