ObjectiveTo establish the fingerprints of 15 batches of Hengzhi Kechuan capsules， to quantitatively analyze 10 index components， and to evaluate the quality uniformity of samples from different batches. MethodsThe fingerprints and quantitative analysis of Hengzhi Kechuan capsules were established by a combination method of high performance liquid chromatography coupled with diode array detector and charged aerosol detector（HPLC-DAD-CAD）， adenosine， guanosine， vanillic acid， safflomin A， agarotetrol， naringin， hesperidin， militarine， ginsenoside Rb₁， and glycyrrhizic acid were selected as quality attribute indexes. A total of 15 batches of Hengzhi Kechuan capsules from 2022 to 2024（3 boxes per batch） were qualitatively and quantitatively analyzed， and the quality uniformity level of the manufacturers was characterized by parameters of intra-batch consistency（P_A） and inter-batch consistency（P_B）. The homogeneity and difference of quality attribute indexes of samples from different years were analyzed by heatmap clustering analysis. ResultsHPLC fingerprints and quantitative method of Hengzhi Kechuan capsules were established， and the methods could be used for qualitative and quantitative analysis of this preparation， which was found to be stable and reliable by method validation. The similarity of fingerprints of 15 batches of samples was 0.887-0.975， a total of 13 common peaks were calibrated， and 10 common peaks were designated， all of which were quality attribute index components. The results of quantitative analysis showed that the contents of the above 10 ingredients in the samples were 0.038-0.078， 0.115-0.251， 0.007-0.018， 0.291-0.673， 0.122-0.257， 0.887-1.905， 1.841-3.364， 1.412-2.450， 2.207-3.112， 0.650-1.161， respectively. And the contents of ginsenoside Rb₁ and glycyrrhizic acid met the limit requirements in the 2020 edition of Chinese Pharmacopoeia. For the samples from 15 batches， the P_A values of the 10 index components were all <10%， indicating good intra-batch homogeneity， and the P_B values ranged from 33.86% to 92.97%， suggesting that the inter-batch homogeneity was poor. Heatmap clustering analysis showed that the samples from different years were clustered into separate categories， and adenosine， guanosine， safflomin A， naringin， hesperidin and agarotetrol were the main differential components. ConclusionThe intra-annual quality uniformity of Hengzhi Kechuan capsules is good and the inter-annual quality uniformity is insufficient， which may be related to the quality difference of Pinellinae Rhizoma Praeparatum， Carthami Flos， Citri Sarcodactylis Fructus， Citri Reticulatae Pericarpium， Aquilariae Lignum Resinatum， Citri Fructus， etc. In this study， the fingerprint and multi-indicator determination method of Hengzhi Kechuan capsules was established， which can be used for more accurate and efficient quality control and standardization enhancement.

OBJECTIVE To evaluate the quality of Mongolian medicine Sendeng-4 based on qualitative and quantitative analysis combined with chemical pattern recognition， in order to provide the reference for its quality control. METHODS The chemical components in Sendeng-4 were analyzed qualitatively by HPLC-Q-Exactive-MS. The contents of 16 components （methyl gallate， ethyl gallate， epicatechin， dihydromyricetin， genipin-1-O-β-D-gentiobioside， caffeic acid， catechin， corilagin， deacetylasperulosidic acid methyl ester， rutin， geniposide， luteolin， myricetin， quercetin， ferulic acid， and toosendanin） in 15 batches of Sendeng-4 （sample S1-S15） were quantitatively analyzed by HPLC-MS/MS. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares discriminant analysis were conducted and variable importance projection （VIP） value greater than 1 was used as the index to screen the differential components. RESULTS A total of 73 chemical components were identified in Sendeng-4， including 20 flavonoids， 16 tannins， 14 organic acids， etc. According to the quantitative analysis， the results exhibited that the average contentsthe of above 16 components in 15 batches of Sendeng-4 were 3.683-7.730， 2.391-6.952， 2 275.538-4 377.491， 2 699.188-3 537.924， 858.266-1 377.393， 3.366-11.003， 140.624-315.683，414.629-978.334， 285.501-1 510.457， 27.799-48.325， 3 625.415-6 309.563， 0.506-0.656， 442.337-649.283， 47.093-59.736， 12.942-15.822， 127.738-326.649 μg/g， respectively. According to the results of CA and PCA， 15 batches of samples could be clustered into two categories: S1-S3， S5-S6， S9-S10 and S13 were clustered into one category； S4， S7-S8， S11-S12， S14-S15 were clustered into one category. VIP values of geniposide， epicatechin， deacetylasperulosidic acid methyl ester and genipin-1-O- β-D-gentiobioside were all greater than 1. CONCLUSIONS HPLC-Q-Exactive-MS and HPLC-MS/MS techniques are employed for the qualitative and quantitative analysis of Sendeng-4. Through chemical pattern recognition analysis， four differential components are identified: geniposide， epicatechin， deacetylasperulosidic acid methyl ester， and genipin-1-O-β-D-gentiobioside.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

ObjectiveTo explore the effect of different drying conditions on the appearance and intrinsic quality indicators of Pinelliae Rhizoma for screening suitable drying conditions， so as to provide reference for its standardized production and quality evaluation. MethodsDifferent dried samples of Pinelliae Rhizoma were prepared by lime-assisted sweating method and intermittent drying method. Visual analysis was employed to measure the color brightness values（L^*） of the surface， cross-section and powder of the samples， texture analyzer was used to determine the hardness of the samples under different drying conditions. The total starch content was calculated by measuring the contents of amylose and amylopectin in the samples with ultraviolet-visible spectroscopy. High performance liquid chromatography（HPLC） was used to determine the contents of seven nucleoside components（uracil， hypoxanthine， uridine， inosine， guanosine， β-thymidine and adenosine） in the samples. Pearson correlation analysis was conducted to explore the correlation between the external characteristics and intrinsic indicators of the different dried samples. Principal component analysis（PCA） was used to comprehensively rank the data of various indicators， and partial least squares-discriminant analysis（PLS-DA） was used to screen differential components with variable importance in the projection（VIP） value>1. Furthermore， the difference between the optimal drying condition for Pinelliae Rhizoma and the traditional sun-drying method was explored by independent samples t-test. ResultsWith the increase of temperature， the color of the intermittently dried samples gradually deepened， while their hardness gradually decreased. Concurrently， the contents of extract， total starch， uridine and adenosine exhibited an upward trend， whereas the contents of uracil， hypoxanthine and inosine displayed a downward trajectory. Compared with the intermittent drying group， the content of extract in the samples subjected to lime-assisted sweating increased. With the increase of lime dose， the hardness and the total content of nucleoside components in the samples showed a downward trend， while the total starch content showed an upward trend. Correlation analysis showed that the comprehensive score of L^* was negatively correlated with the contents of uracil， hypoxanthine and inosine， and positively correlated with the contents of uridine， guanosine and adenosine. Hardness was negatively correlated with adenosine content， and positively correlated with the contents of inosine， uracil and hypoxanthine. Through comprehensive consideration and comprehensive score of principal components， the method of 5% lime-mixed sweating for 6 days emerged as the top-ranking approach. Except for the extract， the results of independent samples t-test showed that there was no significant difference between the 5% lime-mixed sweating for 6 days and the traditional sun-drying in terms of other content indicators. ConclusionThe whiteness and firmness of Pinelliae Rhizoma exhibit significant correlations with its chemical composition， while uridine， uracil， guanosine， adenosine and inosine are the key constituents responsible for the quality difference of Pinelliae Rhizoma under different drying conditions. The lime-assisted sweating method optimized in this study can be proposed as a viable alternative to the traditional sun-drying method. This method not only ensures the quality of the medicinal material but also effectively reduces the drying time and prevents mold contamination， which provides a valuable reference for the standardization of drying conditions and the establishment of quality evaluation criteria for Pinelliae Rhizoma.

Objective: To analyze the epidemiological characteristics and trends of other infectious diarrhea among children under 18 years old in Guangzhou City from 2014 to 2020, and to explore the correlation between climatic factors and the incidence of the disease, so as to provide reference for the early prevention of infectious diseases. Methods: The data of cases of other infectious diarrhea and meteorological data of children under 18 years old in Guangzhou City from 2014 to 2020 were collected through the Chinese Infectious Disease Reporting System and the Guangzhou Meteorological Bureau. The correlation between meteorological factors and the incidence of other infectious diarrhea was analyzed using negative binomial regression. Results: A total of 104 566 cases of other infectious diarrhea among children under 18 years old were reported in Guangzhou City from 2014 to 2020, with a male to female ratio of 1.48∶1. The incidence rate was the highest in 2017 (980.83 per 100 000) and the lowest in 2020 (388.22 per 100 000). The peak of incidence occurred from October to March of the following year. Children under 5 years old accounted for 87.95% of all cases. The number of cases of other infectious diarrhea was negatively correlated with the temperature of the previous 6 days ( IRR = -0.07 ), and positively correlated with the temperature difference on the day of onset ( IRR =0.02) (both P <0.05). It was also positively correlated with the wind speed of the previous 7 days ( IRR=0.07, P <0.05), but there was no statistically significant correlation with the relative humidity on the day of onset ( IRR=-0.00, P >0.05). Conclusions Low temperature, large temperature difference, and high wind speed can increase the risk of other infectious diarrhea. It is necessary to strengthen the prediction and early warning in conjunction with meteorological changes, and warn kindergartens and schools to enhance preventive measures against the clustering of other infectious diarrhea cases.

ObjectiveTo explore quality difference factors of Perillae Caulis based on the contents of multiple chemical components and comprehensively evaluate the quality. MethodsA total of 32 batches of Perillae Caulis samples were collected from 12 producing areas such as Hebei， Anhui and Guangdong， and their diameter range， epidermis color and producing areas were recorded. Total flavonoids， total phenols， volatile oils， 5 active components and 84 volatile components in 32 batches of samples were quantitatively or semi-quantitatively determined by colorimetry， ultra performance liquid chromatography-photodiode array detector（UPLC-PDA） and gas chromatography-mass spectrometry（GC-MS）. Then the differences between the contents of these components were analyzed by principal component analysis（PCA） and non-parametric test. According to the weights of the index components determined by PCA model， entropy weight-technique for order preference by similarity to ideal solution（TOPSIS） model was constructed to evaluate the quality of Perillae Caulis with different characters and origins. ResultsThere were significant differences in the composition of Perillae Caulis with different diameters， epidermis colors and producing areas， and 9 differential components were screened out， including 6 index constituents（total flavonoids， total phenols， caffeic acid， scutellarin， rosmarinic acid and luteolin） and 3 volatile components（caryophyllene oxide， （-）-humulene epoxide Ⅱ， 14-hydroxycaryophyllene）， of which 6 index constituents were higher in samples with small diameter， purple-brown epidermis and southern origin， while the contents of 3 volatile components were higher in samples with large diameter， dark-brown epidermis and northern origin. A significant difference was shown in the model scores of different diameters， epidermis colors and origins（P<0.05）， and the scores of Perillae Caulis with small diameter and purple-brown epidermis from southern area， especially Guangdong， had a high score. ConclusionThere are significant differences in the composition and content of chemical constituents between different diameters， epidermal colors and production areas of Perillae Caulis， samples showing small diameter， owing purple-brown epidermis， and originating from Guangdong were of higher-quality due to their higher content of 8 key indices.

ObjectiveTo explore quality difference factors of Perillae Caulis based on the contents of multiple chemical components and comprehensively evaluate the quality. MethodsA total of 32 batches of Perillae Caulis samples were collected from 12 producing areas such as Hebei， Anhui and Guangdong， and their diameter range， epidermis color and producing areas were recorded. Total flavonoids， total phenols， volatile oils， 5 active components and 84 volatile components in 32 batches of samples were quantitatively or semi-quantitatively determined by colorimetry， ultra performance liquid chromatography-photodiode array detector（UPLC-PDA） and gas chromatography-mass spectrometry（GC-MS）. Then the differences between the contents of these components were analyzed by principal component analysis（PCA） and non-parametric test. According to the weights of the index components determined by PCA model， entropy weight-technique for order preference by similarity to ideal solution（TOPSIS） model was constructed to evaluate the quality of Perillae Caulis with different characters and origins. ResultsThere were significant differences in the composition of Perillae Caulis with different diameters， epidermis colors and producing areas， and 9 differential components were screened out， including 6 index constituents（total flavonoids， total phenols， caffeic acid， scutellarin， rosmarinic acid and luteolin） and 3 volatile components（caryophyllene oxide， （-）-humulene epoxide Ⅱ， 14-hydroxycaryophyllene）， of which 6 index constituents were higher in samples with small diameter， purple-brown epidermis and southern origin， while the contents of 3 volatile components were higher in samples with large diameter， dark-brown epidermis and northern origin. A significant difference was shown in the model scores of different diameters， epidermis colors and origins（P<0.05）， and the scores of Perillae Caulis with small diameter and purple-brown epidermis from southern area， especially Guangdong， had a high score. ConclusionThere are significant differences in the composition and content of chemical constituents between different diameters， epidermal colors and production areas of Perillae Caulis， samples showing small diameter， owing purple-brown epidermis， and originating from Guangdong were of higher-quality due to their higher content of 8 key indices.

OBJECTIVE To comprehensively evaluate the quality of Sanzi powder from different batches based on 12 components quantitative analysis combined with chemical pattern recognition and entropy weight-TOPSIS method. METHODS The contents of 12 components in 15 batches of Sanzi powder （No. S1-S15） were determined by HPLC-MS/MS， such as ethyl gallate， gallic acid， ferulic acid， corilagin， genipin-1-O-β-D-gentiobioside， toosendanin， geniposide， caffeic acid， methyl deacetylated coumarinate， tannic acid， rutin， quercetin. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were conducted on the assay results. Using variable importance projection （VIP） value＞1 and P＜0.05 as the evaluation criteria， the quality differential markers in Sanzi powder were screened. The entropy weight method was used to calculate the weight value， and TOPSIS method was used to rank the quality of 15 batches of Sanzi powder from superior to inferior. RESULTS The contents of the 12 components were 13.494-24.292， 2 069.608-3 188.100， 1.410-3.616， 1 065.030-2 630.584， 1 404.704-1 838.078， 101.640-354.268， 9 193.720-14 777.854， 1.240-5.060， 148.028-5 541.990， 4 261.422-5 607.438， 107.560- 195.512， 2.226-4.192 μg/g， respectively. The results of CA， PCA and OPLS-DA indicated that 15 batches of Sanzi powder could be clustered into two groups. Specifically， batches S3， S7， S10 and S15 were grouped into one category， and remaining batches were grouped into one category. VIP values of geniposide， quercetin， caffeic acid， and methyl deacetylated coumarinate were all greater than 1， with corresponding P-values less than 0.05. The results of the entropy weight-TOPSIS analysis revealed that methyl deacetylate exhibited the smallest information entropy and the highest weight. The relative closeness degrees of samples S3， S7， S10 and S15 ranged from 0.789 to 0.973， while the remaining samples ranged from 0.054 to 0.172. CONCLUSIONS The contents of 12 components in Sanzi powder could be determined accurately by using HPLC-MS/MS technology. Methyl deacetylated coumarinate， geniposide， quercetin and caffeic acid were identified as the quality differential markers. It was found that the overall quality of samples S3， S7， S10 and S15 were superior to that of other batches. Notably， the quality of Gardeniae Fructus decoction pieces emerges as a critical factor in ensuring the consistency of the preparation’s quality.