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.

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 establish fingerprint， chemical pattern recognition and multi-component quantification analysis of Sanzi powder， and evaluate its quality. METHODS HPLC method was adopted. The fingerprints of 15 batches of Sanzi powder were established by using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were also conducted. The variable importance in projection （VIP） value greater than 1 was used as the index to screen the differential markers， and the contents of the differential markers were determined by the same HPLC method. RESULTS A total of 21 common peaks in the HPLC fingerprints of 15 batches of Sanzi powder were calibrated， and the similarities of them were 0.994- 0.999； 6 common peaks were identified， including gallic acid （peak 3）， garminoside （peak 10）， corilagin （peak 11）， chebulinic acid （peak 16）， ellagic acid （peak 18）， crocin Ⅰ （peak 19）. According to the results of cluster analysis， YKD2024LH005，No.YKD2023LH062） principal component analysis and orthogonal partial least squares-discriminant analysis， 15 batches of samples could be clustered into two categories： S1， S5， S7， S9， S14 were clustered into one category； S2-S4， S6， S8， S10-S13， S15 were clustered into one category. VIP values of 11 differential components such as corilagin， chebulinic acid and ellagic acid were higher than 1. Among 15 batches of samples， the contents of corilagin， chebulinic acid and ellagic acid ranged 2.667-5.152， 9.506- 13.522， 0.891-1.811 mg/g. CONCLUSIONS Established HPLC fingerprint and multi-component quantification analysis of Sanzi powder are rapid and simple， and can be used for quality evaluation of Sanzi powder by combining with chemical pattern recognition. Eleven components such as corilagin， chebulinic acid and ellagic acid are differential markers affecting the quality of Sanzi powder.

Background As one of the key populations in the prevention and treatment of iodine deficiency disorders, it is important to continuously monitor the iodine nutritional level of school-age children. The current reference interval for thyroid volume in China is based on age only, without taking into account differences in individual developmental levels, and the distribution of thyroid volume may vary regionally due to economic, demographic, and environmental factors. The current reference cut-off points for thyroid volume proposed by the World Health Organization are not based on the Chinese population. Objective To understand the iodine nutritional status and distribution of thyroid volume (Tvol) among children aged 8-10 years in Huangpu District, Shanghai, China, to identify impact factors of Tvol, and to propose a reference range upper limit for local thyroid health surveillance, so as to provide a basis for goiter control and prevention. Methods Six hundred children aged 8-10 years in Huangpu District were recruited in 2017, 2020, and 2023, and body height, weight, thyroid volume, urinary iodine, and iodine content of household edible salt were determined. A multilevel model was constructed using population density and area as regional variables, and age, body surface area (BSA), and body mass index (BMI) as potential impact factors for at the individual level, to assess their effects on thyroid volume. Quantile regression of thyroid volume was performed, and the 98th percentile (P₉₈) of thyroid volume was predicted based on age and BSA. Results The iodized salt coverage in the households of surveyed children in 2017, 2020, and 2023 was 72.0%, 57.0%, and 48.0%, respectively, and the iodized salt coverage decreased by year (χ²=24.31, P<0.001). The urinary iodine level of children in 2017 was higher than that in 2020 and 2023 (χ²=18.77, P<0.001). The Tvol medians of children in 2017, 2020, and 2023 were 2.29, 2.49, and 2.97 mL, respectively, and the Tvol increased by year (χ²=60.04, P<0.001). The proportion of goiter was higher in children in 2023 than in 2017 and 2020 (χ²=6.57, P<0.05). Sex differences were not statistically significant for urinary iodine levels, thyroid volume, and goiter. The median Tvol was 2.26, 2.58, and 2.76 mL in children of 8, 9, and 10 years old respectively, and the Tvol increased with age (χ²=49.02, P <0.001). Tvol was positively correlated with age, BSA, and BMI with correlation coefficients of 0.2846, 0.3723, and 0.2950, respectively. The final quantile regression model showed that the strongest effect of BSA was associated with the 98th percentile of Tvol. Multilevel models considering population density or area as regional variables failed to achieve convergence. Conclusion To establish the upper limit of the reference interval for thyroid volume of healthy children in Huangpu District, the effects of age and BSA should be considered at the individual level, and the upper limit of the normal interval for goiter screening is suggested to be the 98th percentile value of BSA at the same age. In addition, the adaptability of population density and regional area as indicators of regional aggregation of thyroid volume distribution still needs to be further explored.

Periodontitis is a chronic inflammatory disease of the periodontal supporting tissues caused by plaque microorganisms, whereas inflammatory bowel disease (IBD) is a chronic inflammatory disease characterized by gastrointestinal tract damage. Studies have revealed a close association between periodontitis and IBD, and gut microbiota has been shown to play an important role in the development of IBD. When the gut microbiota is disturbed, it leads to intestinal barrier disruption, triggers immune-inflammatory responses, and influences IBD progression. There are significant differences between the salivary microbiota of periodontitis patients and healthy individuals, and periodontal pathogens can enter the intestinal tract with saliva and participate in the development of IBD by influencing the interactions between gut microbiota composition, immune responses, metabolite production, and intestinal barrier function. Current gut microbiota-targeted intervention strategies, such as fecal microbiota transplantation (FMT) and probiotic supplementation, have shown potential therapeutic value in the treatment of periodontitis. These approaches may exert synergistic effects on both periodontitis and IBD through microbiota modulation. This review summarizes research progress on the relationship between periodontitis and IBD to provide a foundation for the prevention and treatment of these two diseases.

Objective To develop a machine learning-based risk prediction model for postoperative acute kidney injury(AKI)and a model for mortality in obese patients admitted to intensive care unit(ICU)in order to improve early warning and prognostic evaluation to support clinical decision-making.Methods Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-Ⅳ and eICU databases for statistical analysis.Ultimately,2 520 patients(670 from MIMIC-Ⅳ and 1 850 from eICU databases)were included to build the risk prediction models for AKI and mortality.The data included demographic information,vital signs,laboratory findings,surgical types,comorbidities,and medication use.After data cleaning and preprocessing,Boruta feature selection was applied,followed by the construction of prediction models using 7 machine learning algorithms,that is,Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),k-Nearest Neighbors(KNN),Na?ve Bayes(NB),Neural Network(NNET),Support Vector Machine(SVM),and XGBoost.Model performance was evaluated through cross-validation and external validation.Results In the risk prediction models of AKI,the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test.For the risk prediction models of mortality,the GBM model outperformed others in the prediction,attaining an AUC value of 0.91 in the testing set.Conclusion Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed,and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

The interactions between microorganisms and medicinal plants are crucial to the quality improvement of medicinal plants. Medicinal plants attract microorganisms to colonize by secreting specific compounds and provide niche and nutrient support for these microorganisms, with a symbiotic network formed. These microorganisms grow in the rhizosphere, phyllosphere, and endophytic tissues of plants and significantly improve the growth performance and medicinal component accumulation of medicinal plants by promoting nutrient uptake, enhancing disease resistance, and regulating the synthesis of secondary metabolites. Microorganisms are also widely used in the ecological planting of medicinal plants, and the growth conditions of medicinal plants are optimized by simulating the microbial effects in the natural environment. The interactions between microorganisms and medicinal plants not only significantly improve the yield and quality of medicinal plants but also enhance their geoherbalism, which is in line with the concept of green agriculture and eco-friendly development. This study reviewed the research results on the interactions between medicinal plants and microorganisms in recent years and focused on the analysis of the great potential of microorganisms in optimizing the growth environment of medicinal plants, regulating the accumulation of secondary metabolites, inducing systemic resistance, and promoting the ecological planting of medicinal plants. It provides a scientific basis for the research on the interactions between medicinal plants and microorganisms, the research and development of microbial agents, and the application of microorganisms in the ecological planting of medicinal plants and is of great significance for the quality improvement of medicinal plants and the green and sustainable development of TCM resources.
Plants, Medicinal/metabolism* ; Bacteria/genetics* ; Symbiosis