Objective To explore the risk factors for mineral and bone disorder in maintenance hemodialysis patients, and to construct and validate a nomogram prediction model. Methods A total of 306 patients undergoing maintenance hemodialysis at Shanghai Eighth People’s Hospital from January 2021 to May 2025 were selected as study subjects and randomly divided into a training set (n＝214) and a validation set (n＝92) in a 7∶3 ratio. In the training set, patients were divided into a normal bone mineral metabolism group and an abnormal bone mineral metabolism group, and related factors were compared between the two groups. The multivariate logistic regression analysis was used to identify the influencing factors of mineral and bone disorder in maintenance hemodialysis patients in the training set, and a nomogram prediction model was constructed. ROC curves were drawn to evaluate the ability of the nomogram model for predicting mineral and bone disorder in these patients. Calibration curves and Hosmer-Lemeshow goodness-of-fit test were used to analyze the consistency of the predictive probability of nomogram model and actual probability of mineral and bone disorder in these patients. The decision curve was used to assess the clinical benefit using nomogram prediction model. Results Among the 306 hemodialysis patients, 254 patients had mineral and bone disorder, accounting for 83.01%. Among the 214 patients in the training set, 177 had mineral and bone disorder, accounting for 82.71%. In the training set, age, gender, body mass index (BMI), hypertension rate, dialysis age, blood urea nitrogen (BUN), hemoglobin (Hb), albumin (ALB), alkaline phosphatase (ALP), serum creatinine (SCr), uric acid (UA), estimated glomerular filtration rate (eGFR), and rate of taking phosphate binders were statistically significant different between the two groups (P＜0.05). The multivariate logistic regression analysis showed higher age, female, hypertension, longer dialysis duration, decreased eGFR, and not taking phosphate binders were identified as risk factors for mineral and bone disorder in maintenance hemodialysis patients (P＜0.01). The nomogram prediction model was constructed. The area under the ROC curve of the model for mineral and bone disorder in the training set and validation set was 0.895 (95%CI 0.850-0.941) and 0.881 (95%CI 0.830-0.932), respectively, with maximum Youden indice of 0.650 and 0.600, sensitivity of 0.856 and 0.849, and specificity of 0.794 and 0.751. The Hosmer-Lemeshow test showed the nomogram prediction model had good consistency in predictive probabilities with actual probabilities in training set and validation set. The decision curve showed the nomogram model could bring clinical net benefits when the threshold probabilities in the training set and validation set were less than 0.96 and 0.91. Conclusions The nomogram prediction model constructed based on six independent risk factors including age, gender, hypertension, dialysis duration, eGFR, and using phosphate binders or not, shows good discrimination and calibration, with good clinical predictive ability, which could provide guidance for the management of maintenance hemodialysis patients.

ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

OBJECTIVE To systematically review medication-related risk factors for falls in older adults， to provide references for ensuring medication safety among older adults. METHODS A systematic search was conducted in PubMed， Embase， Web of Science， and CNKI for relevant literature published from database inception to November 1， 2025. Relevant studies on medication-related falls in older adults， both domestic and international， were included. Drug factors influencing falls in older adults were summarized and analyzed. RESULTS A total of 22 studies were included. Four major classes of fall-risk-increasing drugs were identified： psychotropic medications [12 studies， odds ratio （OR） range 1.500-5.790]， cardiovascular system drugs （5 studies， OR range 1.236-4.784）， analgesics （3 studies， OR range 1.500-4.490）， and hypoglycemic agents （3 studies， OR range 2.070-2.751）. Additionally， anticholinergic burden （1 study， OR was 2.610） and polypharmacy （7 studies， OR range 2.902-25.897 for the use of ≥4 medications） were identified as significant risk factors for falls. CONCLUSIONS Falls in older adults are significantly associated with psychotropic medications， cardiovascular system drugs， analgesics， and hypoglycemic agents， among which psychotropic medications pose the highest risk. Anticholinergic burden and polypharmacy are also important risk factors. In clinical practice， interventions should be implemented through deprescribing and risk monitoring to effectively reduce the risk of falls in older adults.

Objective To establish a highly efficient and sensitive technical system for the identification and analysis of platycodin-type saponins, systematically compare the differences in platycodin-type saponins among Platycodon grandiflorum from different producing areas, and provide scientific references for the screening of high-quality Platycodon grandiflorum resources, authenticity evaluation, and construction of standardized quality control systems. Methods A total of 45 batches of P. grandiflorum medicinal materials from 3 producing areas （Anhui, Henan, and Jilin, with 15 batches per area） were selected as research objects. Qualitative identification and semi-quantitative analysis of saponin components were performed based on ultra-high performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry （UHPLC-Q-TOF/MS） technology. Meanwhile, two multivariate statistical methods, principal component analysis （PCA） and partial least squares-discriminant analysis （PLS-DA）, were combined to analyze the differences in platycodin-type saponins of Platycodon grandiflorus from different producing areas. Results A total of 28 saponin components were identified from Platycodon grandiflorus of the three producing areas. PCA results showed that there were minor differences in platycodin-type saponins between Henan Platycodon grandiflorus and Jilin Platycodon grandiflorus, while Anhui P. grandiflorum exhibited significant differences from both. PLS-DA further screened 15 major differential compounds. Among them, the contents of 6 components including 3''-O-acetylpolygalacin D2 and platycodin H in Anhui Platycodon grandiflorus were higher than those in Henan and Jilin Platycodon grandiflorus; platycodigenic acid A had the highest content in Jilin Platycodon grandiflorus; the contents of platycodin D3, polygalacin J, and polygalacin D were relatively higher in Henan Platycodon grandiflorus. Conclusion This study clarified the characteristic differences in core components of Platycodon grandiflorus from the three major producing areas, which provided an important theoretical basis for the screening of high-quality Platycodon grandiflorus resources, elucidation of the mechanism underlying its authenticity, and construction of a standardized quality control system.

Objective: To understand the impact of childhood trauma on psychological distress among upper grade elemetary school students, and to explore the mediating role of leisure activities in the relationship, so as to provide a basis for developing mental health intervention strategies. Methods: From August to November 2024, a combination of convenience sampling and stratified cluster random sampling was employed to recruit 1 373 fourth to sixth grade students from four primary schools in Harbin. The Childhood Trauma Questionnaire(CTQ), a self designed leisure activity scale (including active and passive leisure activities), and the Kessler Psychological Distress Scale (K10) were used to assess childhood trauma experiences, leisure activities, and levels of psychological distress. Spearman correlation analysis and linear regression analysis were conducted to explore the relationships among childhood trauma, leisure types, leisure time, and psychological distress. Based on the mediation analysis framework proposed by Hayes (Model 4), the mediating role of leisure types in the relationship between childhood trauma and psychological distress was examined. Results: Totally 19.1% of the upper elemetary school students exhibited psychological distress, while 30.2% had experienced childhood trauma. During school days, 64.6% of the students were reported of having leisure time concentrated between 1 and 5 hours per day, whereas 67.4% reported leisure time exceeding 5 hours per day on weekends. After controlling for potential demographic confounders such as gender, grade, ethnicity, household registration, being an only child, parents educational level, co residence, and whether parents are first time married,linear regression analysis showed that childhood trauma experience had positive predictive effect on psychological distress in upper primary school students( β =0.20, P <0.01). Leisure time showed no statistically significant association with psychological distress, both on school days ( β =-0.58 to -0.56) and weekends ( β =0.26- 0.98 )(all P >0.05). Active leisure activities were negatively associated with psychological distress ( β =-0.20), while passive leisure activities were positively associated with psychological distress ( β =0.29)(both P <0.01). Leisure type partially mediated the relationship between childhood trauma and psychological distress, accounting for 11.7% of the indirect effect. Conclusion Childhood trauma experiences positively predict psychological distress in upper elementary school students, and affect psychological distress through active leisure and passive leisure.

Lung cancer is the leading cause of cancer-related deaths worldwide， and tumor metastasis is a key factor contributing to the mortality of most lung cancer patients. Aberrant activation of epithelial-mesenchymal transition （EMT） is a major driver of lung cancer progression and metastasis. EMT is characterized by the loss of apical-basal polarity and intercellular adhesion in highly differentiated， polarized， and organized epithelial cells， which acquire motility， migratory potential， and invasive properties. During this process， cells undergo cytoskeletal remodeling and transform into a mesenchymal phenotype， accompanied by associated changes in cellular markers. The EMT process is highly complex and is tightly regulated by intricate networks involving multiple transcription factors， post-translational controls， epigenetic modifications， and non-coding RNAs. Therefore， therapies targeting the mechanisms of malignant transformation and their associated pathways in lung cancer are of significant clinical importance. In recent years， EMT has attracted increasing attention as a potential target for cancer therapy. Chinese medicine， with its characteristics of multi-target action， low side effects， and good therapeutic efficacy， has demonstrated an important role in anticancer treatment. A series of studies have investigated the role of Chinese medicine in inhibiting EMT in lung cancer. Active ingredients of Chinese medicine， including flavonoids， glycosides， phenols， terpenoids， saccharides， and alkaloids， as well as Chinese medicine compound formulas， have shown significant regulatory effects on EMT. Their mechanisms mainly involve multiple pathways， targets， and links， including signaling pathways， exosomes， microRNAs （miRNAs）， and the tumor-associated immune microenvironment. This article summarizes the mechanisms by which EMT promotes malignant tumor progression and reviews the current research on how Chinese medicine active ingredients， monomers， and compound formulas inhibit EMT and suppress lung cancer cell migration and invasion. This study is expected to provide comprehensive theoretical information for basic and translational research on lung cancer.

BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

The therapeutic effects of Yueju Pills on depression and cardiovascular diseases have been widely recognized. Previous studies have shown that the drug can significantly improve depressive-like behaviors induced by chronic unpredictable mild stress(CUMS) combined with atherosclerosis(AS). Given the complex pathogenesis of psychocardiac diseases, this study integrated metabolomics and network pharmacology to systematically elucidate the mechanism of Yueju Pills in alleviating depressive symptoms in psychocardiac diseases. The results demonstrate that, after Yueju Pill intervention, the levels of 9 abnormal metabolites in the hippocampus restore to normal ranges, primarily involving key pathways or signaling pathways, including the cyclic adenosine monophosphate(cAMP), mammalian target of rapamycin(mTOR), glycine/serine/threonine metabolism, and aminoacyl-tRNA biosynthesis. In a high-fat diet-induced CUMS ApoE~(-/-) mouse model, Yueju Pills significantly increases adenosine monophosphate(AMP) levels and decreases L-alanine and D-glyceric acid levels in the hippocampus. In conclusion, Yueju Pills exert antidepressant effects by regulating multiple metabolic axes, including glycine/serine/threonine metabolism and the cAMP, mTOR signaling pathways. Network pharmacology predictions reveal that the treatment of CUMS combined with AS by its core active components may be realized through modulating pathways concerning neuroinflammation and synaptic plasticity, including serine/threonine-protein kinase 1(AKT1), mitogen-activated protein kinase 1(MAPK1), and prostaglandin-endoperoxide synthase 2(PTGS2). This study provides a theoretical reference for the clinical application of Yueju Pills in alleviating the depressive symptoms of psychocardiac diseases.
Animals ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Male ; Depression/genetics* ; Humans ; Hippocampus/drug effects* ; Mice, Inbred C57BL ; Signal Transduction/drug effects*

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.