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Objective:To comprehensively evaluated the quality of Sargentodoxae Caulis from different habitats with a combination of indexes and characteristic chromatogram method from Chinese Pharmcopoeia (Edition 2020). Methods:The contents of water content, total ash, ethanolic extract, sulfur dioxide residue, heavy metals and harmful elements, total phenols, chlorogenic acid, salidroside and characteristic chromatogram of 17 batches of Sargentodoxae Caulis were determined. The quality of Sargentodoxae Caulis was comprehensively evaluated by combining chemical pattern recognition method. Results:The water content, total ash content, extracts, and content determination of 17 batches of Sargentodoxae Caulis from different habitats complyed with the provisions of the Chinese Pharmcopoeia (Edition 2020). There were differences in the contents of extracts, chlorogenic acid, and salidroside, among which the content of Anhui origin was higher. A total of 8 common peaks were identified from the 17 batches samples. Conclusion:Comprehensive evaluation of multiple indicators can demonstrate the quality of Sargentodoxae Caulis more correctly, and shows that the quality of Sargentodoxae Caulis from different habitats is different. The quality of Sargentodoxae Caulis from Anhui is better than that from other habitats.
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To study the material basis of cold and hot properties of traditional Chinese medicines (TCMs) in Lamiaceae and to establish a cold and hot properties identification model, a database of material components of TCMs in Lamiaceae was established. A three-level classification system of material components was used to obtain the material basis of cold and hot properties of the Lamiaceae family by using data mining methods such as frequency analysis, association rule analysis, logistic regression, and feature selection. Several identification models were established to recognize the cold and hot properties. The chi-square test results showed that the material composition ratios of cold and hot properties were significantly different at the first-level, second-level, and third-level classification (P < 0.05), and the differences varied as the levels of substance classification changed. The average coefficients of variation were 42.30%, 79.07%, and 91.51% at the first-level, second-level, and third-level classification levels, respectively. In other words, in terms of the percentage differences in material composition ratio, the first-level was smaller than the second-level, and the second-level was smaller than the third-level. The results of the association rule analysis showed that under the third-level classification, there were many effective association rules, and 27 core groups and 34 specific groups of chemical components were obtained based on these rules. 15 decisive groups were obtained from the feature selection results. Multinomial logistic regression analysis was used to successfully establish a cold and hot properties identification model with an overall accuracy of 89%. The material basis of cold and hot properties of TCMs in Lamiaceae is different and intersect with each other. Twenty-seven groups of chemical components, such as bicyclic diterpenes, are the core groups of cold and hot properties, of which 15 groups are the decisive groups. The cold and hot properties are often characterized by the interaction of multiple classes of substances, and a single class of substances often cannot be used to characterize the properties. The organic combination of multiple classes of substances is the material basis of cold and hot properties.
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OBJECTIVE@#To rapidly identify the two morphologies and chemical properties of similar herbal medicines, Blumea riparia and B. megacephala as the basis for chemical constituent analysis.@*METHODS@#UPLC-Q-Exactive-MS/MS was utilized for profiling and identification of the constituents in B. riparia and B. megacephala. Chemical pattern recognition (CPR) was further used to compare and distinguish the two herbs and to identify their potential characteristic markers. Then, an HPLC method was established for quality evaluation.@*RESULTS@#A total of 93 constituents are identified, including 54 phenolic acids, 35 flavonoids, two saccharides, one phenolic acid glycoside, and one other constituent, of which 67 were identified in B. riparia and B. megacephala for the first time. CPR indicates that B. riparia and B. megacephala samples can be distinguished from each other based on the LC-MS data. The isochlorogenic acid A to cryptochlorogenic acid peak area ratio calculated from the HPLC chromatograms was proposed as a differentiation index for distinguishing and quality control of B. riparia and B. megacephala.@*CONCLUSION@#This study demonstrates significant differences between B. riparia and B. megacephala in terms of chemical composition. The results provide a rapid and simple strategy for the comparison and evaluation of the quality of B. riparia and B. megacephala.
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Objective:To establish the HPLC fingerprint of Centellae herba and determine the content of asiaticoside, madecassic acid and asiaticoside B simultaneously; To compare the quality differences of Centellae herba collected in different months. Methods:The chromatographic condition was a Shimadzu InertSustain C18 column (4.6 mm×250 mm, 5 μm) with a mobile phase consisting of acetonitrile and 2 mmol/L beta cyclodextrin in gradient elution at the flow rate of 0.8 ml/min. The detection wavelength was 204 nm, and the column temperature was 30 ℃. The different Centellae herba materials of collected in 2-12 months from Chenzhou were studied by the similarity evaluation combined with cluster analysis, principal component analysis and the three contents determination. Results:The HPLC fingerprint of Centellae herba was established and 9 common peaks were designated. The eleven samples were different, which can be aggregated into 4 categories and the quality of Centellae herba collected in July was the best. Conclusion:The established fingerprint and multi-components quantitative method are stable and reliable, which can provide a reference for the quality control and the utilization of Centellae herba resource.
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OBJECTIVE To establish quantitative analysis of multi -components by single marker （QAMS） method to simultaneously detect the contents of cinnamic acid ，cinnamaldehyde，plantamajoside，verbascoside，isoacteoside，calceolarioside B ， psoralen，isopsoralen，neobavaisoflavone and bavachin in Gushen dingchuan pill ，and to perform quality evaluation of Gushen dingchuan pill by combining with chemical pattern recognition . METHODS High-performance liquid chromatography was adopted . Using psoralen as internal standard ，the relative correction factors of the other 9 components were established ，and the contents of each component were calculated and compared with those determined by external standard method . Cluster analysis ，principal component analysis and partial least squares discrimination analysis were performed by the results of QAMS method ，and the qualities of 15 batches of Gushen dingchuan pills were evaluated . RESULTS The above 10 components showed a good linear relationship in their respective ranges （r＞0.999 0）. RSDs of precision ，repeatability，stability and recovery tests were all lower than 2.00%. There was no significant difference between QAMS method and external standard method （P＞0.05）. The results of cluster analysis and principal component analysi showed that 15 batches of Gushen dingchuan pills could be clustered into 3 categories. The results of partial least squares discrimination analysis showed that psoralen ，verbascoside，cinnamaldehyde and isopsoralen were the main potential markers affecting the quality of Gushen dingchuan pills . CONCLUSIONS Established QAMS method for quantitative control of multi index components and chemical pattern recognition can be used for the quality evaluation of Gushen dingchuan pills .
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OBJECTIVE To establish the method for simultaneous determination of 11 components as narirutin in Biantong capsules，to conduct chemical pattern recognition analysis and to screen differential markers affecting their quality . METHODS HPLC method was adopted . The separation was carried out on Venusil XBP C 18 column with mobile phase consisted of acetonitrile - 0.1％ phosphoric acid solution with gradient elution at flow rate of 1.0 mL/min. The sample size was 10 µL，and column temperature was set at 30 ℃. The detection wavelengths were set at 283，330，520，220 nm，respectively. Using verbascoside as an internal standard ，the contents were determined by quantitative analysis of mult -components by single marker （QAMS），and the results were compared with those of external standard method . Cluster analysis ，principle component analysis and orthogonal partial least squares -discriminant analysis were performed with SPSS 26.0 and SIMCA 14.1 software. The differential markers affecting the quality of Biantong capsules were screened using the variable importance in projection （VIP）value greater than 1 as the standard . RESULTS The contents of narirutin ，naringin，neohesperidin，echinacoside，tubuloside A ，isoacteoside，cyanidin-3-O-glucoside， cyanidin-3-O-rutoside，atractylolide Ⅲand atractylolide Ⅰ were 0.739-1.265，1.134-2.158，1.407-2.359，1.368-2.502，0.304-0.522， 0.257-0.521，0.423-0.727，0.375-0.733，0.130-0.283 and 0.062-0.166 mg/g，respectively. The relative average deviation of them from the external standard method was less than 2％. The results of cluster analysis showed that 15 batches of samples could be grouped into three categories ，S1-S7 as a category ，S8-S10 as a category ，and S 11-S15 as a category ，which was consistent with the classification results of principal component analysis . The results of orthogonal partial least squares -discriminant analysis showed that the VIP values of cyanidin -3-O-rutoside，atractylolide Ⅲ， naringin，neohesperidin，echinacoside and verbascoside were all greater than 1. CONCLUSIONS The method for simultaneous determination of 11 components in Biantongcapsules， including narirutin ， is successfully established . Combined with chemical pattern recognition analysis ，it can be used for the quality control of Biantong capsules . Six components such as cyanidin -3-O-rutoside may be the differential markers that affect the quality of Biantong capsules .
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OBJECTIVE To establish the HPLC fingerprint of Mongolian medicine Sanzisan ，and to evaluate its internal quality by chemical pattern recognition technique comprehensively. METHODS HPLC method was used. Using geniposide as reference，HPLC fingerprints of 15 batches of Sanzisan were drawn with Similarity Evaluation System of TCM Chromatogram Fingerprint（2012 edition）. Similarity evaluation and common peaks identification were conducted. Combined with cluster analysis （CA），principal component analysis （PCA），and orthogonal partial least squares-discriminant analysis （OPLS-DA），the quality of 15 batches of Sanzisan was evaluated ，and the differential markers that affected its quality were screened. RESULTS There were 29 common peaks in 15 batches of Sanzisan ，and the similarity was no less than 0.952，indicating that the chemical composition of the 15 batches of Sanzisan had good consistency. A total of 13 common peaks were identified ，which were chebulic acid ，gallic acid，punicalin，punicalagin A ，punicalagin B ，jasminoside B ，caffeic acid ，corilagin，geniposide，chebulagic acid ，1，2，3，4，6- O-galloylglucose，chebulinic acid ，ellagic acid. Both CA and PCA could divide 15 batches of Sanzisan into four categories ，and the classification results were consistent ，indicating that the quality of 15 batches of Sanzisan had certain differences. Fourteen differential markers （chebulic acid ，gallic acid ，ellagic acid ，etc）that lead to the quality difference between batches were screened out by OPLS-DA. CONCLUSIONS Established HPLC fingerprint analysis method is simple and stable. Combined with chemical pattern recognition analysis ，it can be used for the quality control of Sanzisan.
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OBJECTIVE To establish the fingerprint of Tibetan medicine Adhatoda vasica ，and determine the contents of vasicine and vasicinone ，so as to comprehensively evaluate its quality combined with chemical pattern recognition. METHODS Using vasicine as control ，HPLC fingerprints of 11 batches of A. vasica were established with Similarity Evaluation System for Chromatographic Fingerprints of TCM （2012 edition）. The common peaks were identified and their similarities were evaluated. Cluster analysis （CA），principal component analysis （PCA）and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed by using SPSS 25 software and SIMCA 14.1 software. The variable importance in the projection （VIP）value＞1.0 was used as the standard to screen the differential components affecting the quality of A. vasica ；the contents of vasicine and vasicinone were determined by HPLC simultaneously. RESULTS A total of 23 common peaks were found ，and peak 2 was identified as vasicine ，and peak 4 was identified as vasicinone. Their similarities ranged 0.920-0.994. The results of CA showed that 11 batches of samples were clustered into 3 categories（distance was 14）：S1-S8 as one category （origin：Yunnan，Tibet），S9 as one category （origin：Yunnan），S10-S11 as one category （origin：Sichuan）；the results of P CA and OPLS-DA showed that S 9 and S10-S11 were divided into one category respectively ，and S1-S8 were further divided into 2 categories：S1，S4 as one category，S2-S3，S5-S8 as one category ；the common peaks with VIP value ＞1.0 included peak 2，peak 16，peak 21，peak 17，peak 1 and peak 13. Among 11 batches of samples ， contents of vasicine and vasicinone were 4.12-10.22 and 0.60-3.26 mg/g， respectively. CONCLUSIONS Established edu.cn HPLC fi ngerprint and content determination method are simple and accurate ，and can be used for the quality evaluation of Tibetan medicine A. vasica ，by combining with chemical pattern recognition. Vasicine and other components may be the differential components that affect the quality of the drug.
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OBJECTIV E To establish the method for evaluating the quality o f Plantago asi atica and fried P. asiatica . METHODS The fingerprints of 15 batches of P. asiatica and 15 batches of fried P. asiatica were established by HPLC. The common peaks were identified with the Similarity Evaluation System for Chromatographic Fingerprinting of TCM （2012 edition）， and similarity evaluation was performed. Analysis of chemical pattern recognition was performed by using SPSS 25.0 and SIMCA-P 14.1 software（cluster analysis ，principal component analysis and orthogonal partial least squares regression analysis ）. The markers which affected the difference in the quality between P. asiatica and fried P. asiatica were screened with variable importance projection（VIP）value greater than 1. RESULTS There were 18 common peaks in the fingerprints of 15 batches of P. asiatica and 13 common peaks in the fingerprints of 15 batches of fried P. asiatica . A total of 8 common peaks were found in both of them. Their similarities were greater than 0.920. Two common peaks were identified as geniposidic acid ，acteoside. The results of cluster analysis showed that when the spacing was 10，the 30 batches of samples could be clustered into three categories ，with S 1-S5 as one，S16-S20 as one ，S6-S15 and S 21-S30 as one . The results of the pri ncipal component analysis showed that the cumulative variance contribution rate of the first two principal components was 82.575％ . The results of the orthogonal partial least squares regression analysis showed that the VIP values of the three common peaks were greater than 1，namely peak E（acteoside）， peak D （geniposidic acid ） and peak G. CONCLUSIONS Established fingerprints are stable ，simple sina.com and rapid. It can be used for the quality evaluation of P. asiatica and fried P. asiatica ，by combining with analysis of chemical pattern recognition. Acteoside ，geniposidic acid and the component represented by peak G may be the markers affecting the difference in quality of P. asiatica and fried P. asiatica .
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Ischemia-reperfusion injury (IRI) is a pathophysiological process, which widely exists in organ transplantation and surgery. IRI is mainly manifested with hypoxia injury of organs or tissues during the ischemia period, which could be further aggravated after reperfusion. Ischemia-reperfusion induces tissue cell injury, releases damage-associated molecular pattern and further activates multiple immune cells via pattern recognition receptor, leading to aseptic inflammation and aggravating tissue injury. Cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS), as a critical member of pattern recognition receptor, could activate the stimulator of interferon genes (STING) signal pathway and play an important regulatory role in innate immune response. At present, increasing evidences have shown that cGAS-STING signal pathway plays a significant role in organ IRI. In this article, STING signaling pathway, its role and mechanism in IRI of different organs were reviewed, aiming to provide novel ideas for clinical interventions.
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OBJECTIVE To establish the fingerprints of Kangfuyan capsules and carry out chemical pattern recognition analysis，and simultaneously determine the contents of five components so as to promote the quality standard of the drug. METHODS High performance liquid chromatography （HPLC）fingerprints of 11 batches of Kangfuyan capsules （S1-S11）were established by Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition）；identification and attribution analysis of chromatographic peaks were carried out by comparison with the chromatograms of the reference substance and the decoction pieces of single ingredient. SPSS 26.0 and SIMCA 14.1 software were used for cluster analysis and principal component analysis. HPLC method was used to determine the contents of matrine ，phellodendrine chloride ，rutin，forsythoside A and berberine hydrochloride. RESULTS There were 29 common peaks in the fingerprints for 11 batches of samples ，and the similarity was higher than 0.99. A total of 5 chromatographic peaks were identified ，which are matrine （peak 3），phellodendron chloride （peak 14），rutin （peak 20），forsythiaside A （peak 22） and berberine hydrochloride （peak 28）. The results of cluster analysis and principal component analysis showed that S 1-S9 were clustered into one category ，and S 10 and S 11 were clustered into another category. The contents of above 5 components were 29.320 5-60.144 3，0.621 6-1.076 6，1.025 9-2.830 5，2.899 3-6.212 7 and 4.425 1-8.581 6 mg/g， respectively. CONCLUSIONS The established fingerprint and content determination method are stable and reliable ，and can provide reference for the quality control of the preparation in combination with chemical pattern recognition analysis.
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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.
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OBJECTIVE To determine the conte nts of 4 main components in Rougui renshen granules ，and to establish the fingerprint and to screen differential markers affecting its quality. METHODS HPLC method was employed to determine the contents of ammonium glycyrrhizinate ，glycyrrhizin，cinnamic acid and cinnamaldehyde. HPLC fingerprints of 10 batches of Rougui renshen granules were established simultaneously. Similarity Evaluation System of Chromatographic Fingerprint of TCM （2012 edition）was used to evaluate the similarity and determine the common peak ；SPSS 25.0 and SIMCA 14.1 software were applied for cluster analysis （CA），principal component analysis （PCA）and partial least square-discriminant analysis （OPLS-DA）. The differential markers affecting sample quality were screened by using the variable importance in projection （VIP）value＞ 1 as standard. RESULTS The methodology of content determination met the relevant requirements. The contents of ammonium glycyrrhizinate，glycyrrhizin，cinnamic acid and cinnamaldehyde were 1.808 4-2.770 0，1.137 2-1.481 4，0.076 5-0.091 8 and 0.130 9-0.478 4 mg/g，respectively. A total of 16 common peaks were found in the fingerprints of 10 batches of Rougui renshen granules. Four chromatographic peaks were identified ，i.e. glycyrrhizin （peak 6），cinnamic acid （peak 10），cinnamaldehyde（peak 11）and ammonium glycyrrhizinate （peak 15）. The similarities of samples were ＞0.95. Results of CA showed that 10 batches of samples could be classified into three categories ：S3 was grouped into one category ；S1-S2，S4-S5 and S 10 were grouped into one category；S6-S9 were grouped into one category. The results of PCA showed that the cumulative contribution rate of the first three principal components was 91.918％，and the classification results were consistent with CA. The results of OPLS-DA showed that the four peaks with VIP value ＞1 were peak 11（cinnamaldehyde），peak 15（ammonium glycyrrhizinate ），peak 6（glycyrrhizin） and peak 9. CONCLUSIONS Established methods of content determination and fingerprint are accurate and reproducible ，and can be used for the quality evaluation of Rougui renshen granules. The components as ammonium glycyrrhizinate ，cinnamaldehyde， glycyrrhizin may be differential markers affecting the quality of Rougui renshen granules.
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The quality control and evaluation methods of Schizonepeta tenuifolia were established by HPLC fingerprint, multi index component content determination and chemical pattern recognition to provide basis for the quality control of medicinal materials. The chemical components of 25 batches of Schizonepeta tenuifolia panicle medicinal materials and decoction pieces collected were analyzed by high performance liquid chromatography, and the common pattern of fingerprint was established. A total of 22 common chromatographic peaks were calibrated, and their similarity was more than 0.9. The samples were divided into three categories according to different producing areas by cluster analysis. The results of principal component analysis and cluster analysis were consistent. Finally, five differential markers of different batches of Schizonepeta tenuifolia were selected by orthogonal partial least squares discriminant analysis. Through the identification of the reference substance, it was determined that peak 9 was hesperidin, peak 10 was rosmarinic acid, peak 13 was tilianin, peak 14 was quercetin, and peak 20 was pulegone. The quality evaluation method established in this study is stable and reliable, and is suitable for the quality control of Schizonepeta tenuifolia.
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OBJECTIVE To e stablish the fingerprint of Qings hen tiaozhi xiaoke tablets （QTXT）and carry out the analysis of chemical pattern recognition ，and determine the contents of seven active components simultaneously. METHODS Using coptisine hydrochloride as reference ，the Similarity Evaluation System for Chromatographic Fingerprint of TCM （2012 edition）was utilized to establish the HPLC fingerprints of 13 batches of QTXT and analyze their similarity. The common peaks were confirmed by comparing with the chromatogram of the mixed control ；the attribution of the common peak was determined by comparing the chromatograms of the sample solutions of single decoction pieces and negative sample solutions ；using SPSS 22.0 and SIMCA 14.1 software，cluster analysis （CA），principal component analysis （PCA）and orthogonal partial least squares-discriminant analysis （OPLS-DA）were carried out ，and the markers affecting the quality of QTXT were screened ，using the variable importance in projection（VIP）value greater than 1 as the standard. Using coptisine hydrochloride as internal reference ，the contents of naringin ， hesperidin，neohesperidin，berberine hydrochloride ，palmatine hydrochloride and lovastatin were determined by quantitative analysis of multicomponents by single marker （QAMS），and then compared wi th the result s（except for coptisine hydrochloride ） of external standard method. RESULTS There were 17 Δ 基金项目：江苏省“双创团队”项目[No.（2018）2024号] ＊硕士研究生。研究方向：中药新药药学。E-mail：2769544062@ common peaks in 13 batches of QTXT ，and the similarity was qq.com 0.987-0.999. Seven chromatographic peaks were identified ， # 通信作者：副研究员，硕士生导师，博士。研究方向：中药药剂 namely naringin （peak 4）， hesperidin （peak 5）， 学。E-mail：tsliur411@sina.com neohesperidin（peak 6），coptisine hydrochloride （peak 8）， ·1204· China Pharmacy 2022Vol. 33 No. 10 中国药房 2022年第33卷第10期 palmatine hydrochloride （peak 9），berberine hydrochlo ride（peak 10），lovastatin（peak 14）. Peaks 7-10 were the exclusive peaks of Coptis chinensis ；peaks 3-6 and 11-13 were the exclusive peaks of bran-fried Fructus aurantii ；peak 14 was the exclusive peak of Monascus purpureus ；peak 1 was the common peak of C. chinensis and M. purpureus . Peak 2 and 15 were the common peak of bran-fried F. aurantii and M. purpureus ；peaks 16 and 17 were the common peaks of 6 traditional Chinese medicines. The results of CA showed that 13 batches of QTXT could be divided into three categories ，S2 was clustered into one category ，S1，S9，S10 were clustered into one category ，S3-S8 and S 11-S13 were clustered into one category. The results of PCA showed that accumulative variance contribution of the first three principal components was 85.120％. Compared with CA ，S1 was further distinguished from S9 and S 10 by PCA. OPLS-DA showed that 7 common peaks with VIP value greater than 1（from large to small ）were peak 10 （berberine hydrochloride ），peak 9（palmatine hydrochloride ），peak 5（hesperidin），peak 11 and peak 8（coptisine hydrochloride ）， peak 12 and peak 6（neohesperidin）. The contents of naringin ，hesperidin，neohesperidin，berberine hydrochloride ，palmatine hydrochloride and lovastatin measured by QAMS were 40.198-77.552，6.138-13.413，71.823-125.868，11.274-49.951，3.303- 5.367，1.821-3.185 mg/g，respectively. The contents of naringin ，hesperidin，neohesperidin，berberine hydrochloride ，coptisine hydrochloride，palmatine hydrochloride and lovastatin measured by external reference method were 41.454-79.976，6.404-13.993， 74.068-129.081，11.627-51.512，5.922-12.020，3.158-5.131 and 1.901-3.325 mg/g，respectively. The deviations of the two methods （except for coptisine hydrochloride ）were all less than 3.00％. CONCLUSIONS The established HPLC fingerprint and the method of QAMS are simple ，accurate and reproducible. Combined with chemical pattern recognition analysis ，it can be used for the quality evaluation of QTXT. Berberine hydrochloride ，palmatine hydrochloride and other components may be the markers affecting the quality of the drug.
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Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.
Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography , Evoked Potentials, Visual , Photic StimulationABSTRACT
The present study detected the component content in Dalbergiae Odoriferae Lignum by HPLC fingerprint and the multi-component determination method. HPLC analysis was performed on the Agilent ZORBAX SB-C_(18) column(4.6 mm×250 mm, 5 μm). Acetonitrile-0.5% phosphoric acid aqueous solution with gradient elution was employed as the mobile phase. The flow rate was 1.0 mL·min~(-1) and the column temperature was maintained at 30 ℃. The detection wavelength was 210 nm and the sample volume was 10 μL. The similarity of 18 batches of Dalbergiae Odoriferae Lignum was 0.343-0.779, indicating that there were great differences between different batches of Dalbergiae Odoriferae Lignum. Eighteen common peaks were identified, including eight flavonoids such as liquiritigenin and latifolin. The mass fractions of liquiritigenin, luteolin, naringenin, isoliquiritigenin, formononetin, dalbergin, latifolin, and pinocembrin were in the ranges of 0.134 1%-0.495 2%, 0.028 2%-0.167 0%, 0.016 3%-0.591 3%, 0.053 5%-0.188 0%, 0.142 4%-0.640 1%, 0.068 0%-0.590 7%, 0.003 2%-1.980 7%, and 0.009 6%-0.740 2%, respectively. Eighteen batches of Dalbergiae Odoriferae Lignum were divided into three categories by cluster analysis and eight differential components in Dalbergiae Odoriferae Lignum were marked by partial least-squares discriminant analysis(PLS-DA). The cumulative variance contribution rate was 90.5%. The HPLC fingerprint combined with the multi-component determination method for Dalbergiae Odoriferae Lignum is easy in operation and accurate in results, with good repeatability and reliability. The quality of Dalbergiae Odoriferae Lignum can be evaluated and analyzed by the PLS-DA model. This study is expected to provide a reference for the quality control and clinical application of Dalbergiae Odoriferae Lignum.
Subject(s)
Chromatography, High Pressure Liquid/methods , Drugs, Chinese Herbal/analysis , Flavonoids/analysis , Quality Control , Reproducibility of ResultsABSTRACT
Based on ITS sequences, the molecular identification of Cordyceps cicadae and Tolypocladium dujiaolongae was carried out, and high-performance liquid chromatography(HPLC) fingerprint combined with chemical pattern recognition method was established to differentiate C. cicadae from its adulterant T. dujiaolongae. The genomic DNA from 10 batches of C. cicadae and five batches of T. dujiaolongae was extracted, and ITS sequences were amplified by PCR and sequenced. The stable differential sites of these two species were compared and the phylogenetic tree was constructed via MEGA 7.0. HPLC was used to establish the fingerprints of C. cicadae and T. dujiaolongae, and similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and partial least squares discriminant analysis(PLS-DA) were applied to investigate the chemical pattern recognition. The result showed that the sources of these two species were different, and there were 115 stable differential sites in ITS sequences of C. cicadae and T. dujiao-longae. The phylogenetic tree could distinguish them effectively. HPLC fingerprints of 18 batches of C. cicadae and 5 batches of T. dujiaolongae were established. The results of CA, PCA, and PLS-DA were consistent, which could distinguish them well, indicating that there were great differences in chemical components between C. cicadae and T. dujiaolongae. The results of PLS-DA showed that six components such as uridine, guanosine, adenosine, and N~6-(2-hydroxyethyl) adenosine were the main differential markers of the two species. ITS sequences and HPLC fingerprint combined with the chemical pattern recognition method can serve as the identification and differentiation methods for C. cicadae and T. dujiaolongae.
Subject(s)
Chromatography, High Pressure Liquid/methods , Cordyceps/genetics , Hypocreales , PhylogenyABSTRACT
OBJETIVO: Determinar el tiempo que requiere una curva de aprendizaje para diagnóstico ecográfico específico histopatológico en masas anexiales basándonos en cálculos estadísticos no influidos por la prevalencia según diferentes grados de experiencia. MÉTODOS: Estudio observacional, descriptivo, transversal. Se estudiaron imágenes de 108 masas anexiales. La prueba estándar de oro fue el reporte histopatológico definitivo. Se comparó el rendimiento diagnóstico de 4 examinadores con la siguiente experiencia en diagnóstico ecográfico de patología anexial: A > 20 años, B ≤ 20 hasta > 10 años, C ≤ 10 hasta > 5 años y D ≤ 5 años, analizando solo imágenes y sin datos clínicos de las pacientes, para emitir un diagnóstico específico a libre escritura. RESULTADOS: Prevalencia de masas malignas 17,2 % (15/87). Nivel de confianza en los examinadores se consideró según falta de respuesta diagnóstica: alto (<6 %) con experiencia de más de 10 años y moderado a bajo ≤ 10 años. Examinadores con más de 5 años siempre mostraron likelihood ratio positivo mayor a 10, exactitud diagnóstica mayor a 90 % y Odds ratio diagnóstica mayor a 46, no así para examinador con menor tiempo de experiencia, quién presentó resultados con mala utilidad clínica. El cambio de probabilidad de acierto específico pre-test a post-test mejoró consistentemente con los años de experiencia. CONCLUSIÓN: Se necesitarían más de 10 años de experiencia con especial dedicación a ecografía ginecológica avanzada para un rendimiento diagnóstico específico deseado junto con alta confianza en ecografía de masas anexiales.
OBJECTIVE: To determine the time required for a learning curve of histopathological specific ultrasound diagnosis in adnexal masses based on statistical calculations not influenced by prevalence according to different degrees of experience. METHODS: Observational, descriptive, cross-sectional study. Images of 108 adnexal masses were studied. The gold standard test was the definitive histopathological report. The diagnostic performance of 4 examiners with the following experience in ultrasound diagnosis of adnexal pathology: A > 20 years, B ≤ 20 to > 10 years, C ≤ 10 to > 5 years and D ≤ 5 years was compared, analyzing only images and blinded of clinical data of the patients, to issue a specific diagnosis with free writing. RESULTS: Prevalence of malignant masses 17.2% (15/87). The level of confidence in the examiners was considered according to the lack of diagnostic response: high (<6%) with experience of more than 10 years and moderate to low ≤ 10 years. The examiners with more than 5 years always showed likelihood ratio positive greater than 10, diagnostic accuracy greater than 90% and diagnostic Odds ratio greater than 46, not so for the examiner with less experience time who presented results with little clinical utility. The change in specific probability from pre-test to post-test improved consistently with years of experience. CONCLUSION: More than 10 years of experience with special dedication to advanced gynecological ultrasound are probably needed for a desired specific diagnostic performance coupled with high confidence in adnexal mass ultrasound.
Subject(s)
Humans , Female , Adult , Middle Aged , Aged , Ultrasonics/education , Adnexal Diseases/diagnostic imaging , Ovarian Neoplasms/diagnostic imaging , Radiology/education , Time Factors , Cross-Sectional Studies , Probability , Adnexal Diseases/pathology , Clinical Competence , Learning CurveABSTRACT
Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.