ObjectiveTo study on the different therapeutic effects and potential mechanisms of Rhei Radix et Rhizoma（RH） before and after steaming with wine on constipation model mice. MethodsFifty-four male ICR mice were randomly divided into control group， model group， lactulose group（1.5 mg·kg^-1）， high， medium and low dose groups of RH and RH steaming with wine（PRH）（8， 4， 1 g·kg^-1）. Except for the control group， the constipation model was replicated by gavage of loperamide hydrochloride（6 mg·kg^-1） in the other groups. After 2 weeks of modeling， each administration group was gavaged with the corresponding dose of drug solution， and the control and model groups were given an equal volume of normal saline， 1 time/d for 2 consecutive weeks. After administration， the feces were collected for 16S rRNA sequencing， the levels of gastrin（GAS）， motilin（MTL）， interleukin-6（IL-6）， γ-interferon（IFN-γ） in the colonic tissue were detected by enzyme-linked immunosorbent assay（ELISA）， the histopathological changes of colon were observed by hematoxylin-eosin（HE） staining， flow cytometry was used to detect the proportion changes of CD4⁺， CD8⁺ and regulatory T cell（Treg） in peripheral blood. ResultsCompared with the control group， the model group showed significantly decrease in fecal number in 24 h， fecal quality and fecal water rate（P<0.01）， the colon was seen to have necrotic shedding of mucosal epithelium， localized intestinal glands in the lamina propria were degenerated， necrotic and atrophied， a few lymphocytes were seen to infiltrate in the necrotic area in a scattered manner， the contents of GAS and MTL， the proportions of CD4⁺， CD8⁺ and Treg were significantly reduced（P<0.01）， the contents of IL-6 and IFN-γ were significantly elevated（P<0.05， P<0.01）. Compared with the model group， the fecal number in 24 h， fecal quality and fecal water rate of high-dose groups of RH and PRH were significantly increased（P<0.05， P<0.01）， the pathological damage of the colon was alleviated to varying degrees， the contents of GAS， MTL， IL-6 and IFN-γ were significantly regressed（P<0.05， P<0.01）， and the proportions of CD4⁺ and CD8⁺ were significantly increased（P<0.01）， although the proportion of Treg showed an upward trend， there was no significant difference. In addition， the results of intestinal flora showed that the number of amplicon sequence variant（ASV） and Alpha diversity were decreased in the model group compared with the control group， and there was a significant difference in Beta diversity， with a decrease in the relative abundance of Lactobacillus and an increase in the relative abundances of Bacillus and Helicobacter. Compared with the model group， the ASV number and Alpha diversity were increased in the high-dose groups of RH and PRH， and there was a trend of regression of Beta diversity to the control group， the relative abundance of Lactobacillus increased， and the relative abundances of Bacillus and Helicobacter decreased. ConclusionRH and PRH can improve dysbacteriosis， promote immune system activation， inhibit the release of inflammatory factors for enhancing the gastrointestinal function， which may be one of the potential mechanisms of their therapeutic effect on constipation.

[Objective] To perform genetic analysis on samples with weak agglutination and mixed agglutination of ABO blood group antigens in neonates, and to investigate the molecular biological characteristics of ABO subtypes in neonates. [Methods] Serological identification of ABO blood group was performed by tube method and microcolumn gel method. The ABO exons 2-7 were amplified by PCR, and the amplified products were sequenced by Sanger sequencing method to determine the genotype. [Results] Among the ABO blood group serological results of 14 neonates, 8 cases showed weakened A antigen, and 6 cases showed weakened B antigen. Seven samples were identified with ABO subtype alleles, with genotypes as A102/B101+c.538C>T, Aw26/B102, A205/O02, A205/B101(2 cases), Aw26/O02, B(A)06/O01, B101/O01(3 cases), A102/O01(2 cases), A102/B101 and B101/O02. Additionally, three other family members were also found to carry B(A)06 allele in a pedigree investigation. [Conclusion] For samples showing weakened antigens in ABO blood type identification of neonates, it is necessary to consider the possibility of ABO subtype in addition to age factors, and genetic testing can be used to prevent missed detection of ABO subtypes in neonates.

Objective: To explore the relationship between related factors of non-suicidal self-injury behavior (NSSI) among middle school students in Guizhou Province, so as to provide the evidence for preventing high risk behaviors in adolescents. Methods: A stratified cluster random sampling method was used to select 1 034 junior and senior middle school students from Zunyi City, Qiannan Prefecture and Tongren City in Guizhou Province from April to October in 2023. Questionnaire survey was conducted to collect information including Adolescent Self injury Scale and Family Assessment Device. The R 4.4.1 software was employed for network analysis visualization, centrality indicators, and result stability assessment. Results: The detection rate of NSSI behavior among middle school students in Guizhou province was 29.6%, with a detection rate of 25.5% for boys and 33.1% for girls, showing a statistically significant difference ( χ 2=7.07, P <0.05). There were statistically significant differences in scores of emotional communication, egoism, family rules, positive communication, problem solving, expression of positive emotions and management of negative emotions self-efficacy, and bullying victimization in various dimensions between middle school students with and without NSSI ( Z =-13.66 to -7.05, P <0.01). NSSI among middle school students was positively correlated with social/relational bullying, depression and anxiety, and there were relatively close connections in the network ( r =0.35, 0.43, 0.42, P <0.01). Centrality indicators showed that the highest in strength and closeness centrality were stress ( Z =1.29, 1.58), the highest in betweenness centrality was for emotional communication ( Z =1.91), and the highest in expected influence index was for physical bullying ( Z =1.44)( P < 0.05). Conclusions Stress, emotional communication and physical bullying have significant impacts in the network of factors related to NSSI. Social/relational bullying, depression and anxiety have strong direct correlations with NSSI behavior among middle school students.

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.

ObjectiveTo understand the infection characteristics of multidrug-resistant organisms (MDROs) in hospitalized patients in a tertiary sentinel hospital in Shanghai, so as to provide an evidence for the development of targeted prevention and control measures. MethodsData of MDROs strains and corresponding medical records of some hospitalized patients in a hospital in Shanghai from 2021 to 2023 were collected, together with an analysis of the basic information, clinical treatment, underlying diseases and sources of sample collection. ResultsA total of 134 strains of MDROs isolated from hospitalized patients in this hospital were collected from 2021 to 2023 , including 63 strains of methicillin-resistant Staphylococcus aureus (MRSA), 57 strains of carbapenem-resistant Acinetobacter baumannii (CRAB), and 14 strains of carbapenem-resistant Klebsiella pneumoniae (CRKP). Of the 134 strains, 30 strains were found in 2021, 47 strains in 2022 and 57 strains in 2023. The male-to-female ratio of patients was 2.05∶1, with the highest percentage (70.90%) in the age group of 60‒<90 years. The primary diagnosis was mainly respiratory disease, with lung and respiratory tract as the cheif infection sites. There was no statistically significant difference in the distribution of strains between different genders and infection sites (P>0.05). However, the differences in the distribution of strains between different ages and primary diagnosis were statistically significant (P<0.05). Patients who were admitted to the intensive care unit (ICU), had urinary tract intubation, were not artery or vein intubated, were not on a ventilator, were not using immunosuppresants or hormones, and were not applying radiotherapy or chemotherapy were in the majority. There was no statistically significant difference in the distribution of strains for whether received radiotherapy or chemotherapy or not (P>0.05), while the differences in the distribution of strains with ICU admission history, urinary tract intubation, artery or vein intubation, ventilator use, and immunosuppresants or hormones use or not were statistically significant (all P<0.05). The type of specimen was mainly sputum, the hospitalized ward was mainly comprehensive ICU, the sampling time was mainly in the first quarter throughout the year, the number of underlying diseases was mainly between 1 to 2 kinds, the application of antibiotics ≥4 kinds, and those who didn’t receive any surgery recently accounted for the most. There were statistically significant differences in the distribution of strains between different specimen types, wards occupied and history of ICU stay (P<0.05), but no statistically significant difference in the distribution of strains between different sampling times, number of underlying diseases and types of antibiotics applied (P>0.05). ConclusionThe situation of prevention and control on MDROs in this hospital is still serious. Focus should be placed on high-risk factors’ and infection monitoring and preventive measures should be strengthened to reduce the incidence rate of MDROs infection.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Emodin is a hydroxyanthraquinone compound that is widely distributed and has multiple pharmacological activities, including anti-diarrheal, anti-inflammatory, and liver-protective effects. Research indicates that emodin may be one of the main components responsible for inducing hepatotoxicity. However, studies on the mechanisms of liver injury are relatively limited, particularly those related to bile acids(BAs) metabolism. This study aims to systematically investigate the effects of different dosages of emodin on BAs metabolism, providing a basis for the safe clinical use of traditional Chinese medicine(TCM)containing emodin. First, this study evaluated the safety of repeated administration of different dosages of emodin over a 5-week period, with a particular focus on its impact on the liver. Next, the composition and content of BAs in serum and liver were analyzed. Subsequently, qRT-PCR was used to detect the mRNA expression of nuclear receptors and transporters related to BAs metabolism. The results showed that 1 g·kg~(-1) emodin induced hepatic damage, with bile duct hyperplasia as the primary pathological manifestation. It significantly increased the levels of various BAs in the serum and primary BAs(including taurine-conjugated and free BAs) in the liver. Additionally, it downregulated the mRNA expression of farnesoid X receptor(FXR), retinoid X receptor(RXR), and sodium taurocholate cotransporting polypeptide(NTCP), and upregulated the mRNA expression of cholesterol 7α-hydroxylase(CYP7A1) in the liver. Although 0.01 g·kg~(-1) and 0.03 g·kg~(-1) emodin did not induce obvious liver injury, they significantly increased the level of taurine-conjugated BAs in the liver, suggesting a potential interference with BAs homeostasis. In conclusion, 1 g·kg~(-1) emodin may promote the production of primary BAs in the liver by affecting the FXR-RXR-CYP7A1 pathway, inhibit NTCP expression, and reduce BA reabsorption in the liver, resulting in BA accumulation in the peripheral blood. This disruption of BA homeostasis leads to liver injury. Even doses of emodin close to the clinical dose can also have a certain effect on the homeostasis of BAs. Therefore, when using traditional Chinese medicine or formulas containing emodin in clinical practice, it is necessary to regularly monitor liver function indicators and closely monitor the risk of drug-induced liver injury.
Emodin/administration & dosage* ; Bile Acids and Salts/metabolism* ; Animals ; Male ; Liver/injuries* ; Chemical and Drug Induced Liver Injury/genetics* ; Drugs, Chinese Herbal/adverse effects* ; Humans ; Rats, Sprague-Dawley ; Mice ; Rats

OBJECTIVE: To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with this syndrome. METHODS: A total of 80 KOA patients were recruited from October 2022 to June 2024, including 40 cases in the non-Yang deficiency and blood stasis group (27 males and 13 females), with an average age of (61.75±3.45) years old;and 40 cases in the Yang deficiency and blood stasis group (22 males and 18 females), with an average age of (62.00±2.76) years old. The levels of body mass index (BMI), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fibrinogen, total cholesterol, and D-dimer were recorded and summarized. Blood and synovial fluid samples from patients were collected for gene expression profile microarray sequencing, and then PCR and immunohistochemistry were used for clinical verification on the patients' synovial fluid and cartilage samples. RESULTS: Logistic regression analysis showed that compared with KOA patients with non-Yang deficiency and blood stasis syndrome, those with Yang deficiency and blood stasis syndrome had increased BMI, LDL, fibrinogen, total cholesterol, and D-dimer, and decreased HDL, with a clear correlation between the two groups. There were 562 differential genes in the blood, among which 322 were up-regulated and 240 were down-regulated;755 differential genes were found in the synovial fluid, with 350 up-regulated and 405 down-regulated. KEGG signaling pathway analysis of synovial fluid revealed changes in lipid metabolism-related pathways, including cholesterol metabolism, fatty acid metabolism, and PPARG signaling pathway. Analysis of the involved differential genes identified 6 genes in synovial fluid that were closely related to lipid metabolism, namely LRP1, LPL, ACOT6, TM6SF2, DGKK, and PPARG. Subsequently, PCR and immunohistochemical verification were performed using synovial fluid and cartilage samples, and the results were consistent with those of microarray sequencing. CONCLUSION This study explores the clinical and genomic correlation between traditional Chinese medicine syndromes and knee osteoarthritis from the perspective of lipid metabolism, and proves that abnormal lipid metabolism is closely related to KOA with Yang deficiency and blood stasis syndrome from both clinical and basic aspects.
Humans ; Male ; Female ; Middle Aged ; Synovial Fluid/metabolism* ; Osteoarthritis, Knee/metabolism* ; Yang Deficiency/complications* ; Aged