ObjectiveTo investigate the potential mechanism of Yiqi Yangyin Huoxue Formula （益气养阴活血方， YYHF） in the treatment of membranous nephropathy （MN） based on the NOX4-mediated stromal cell-derived factor-1α（SDF-1α）/CXC chemokine receptor 4 （CXCR4） signaling pathway. MethodsA total of 42 male SD rats were randomly divided into blank group （n=6） and modeling group （n=36）. The modeling rats were given a single injection of sheep antirat Fx1A （6 ml/kg） serum into the tail vein to establish MN model. Thirty-six rats with successful modeling were further divided into model group （normal saline， 10 ml/kg）， benazepril group （10 mg·kg^-1·d^-1）， the medium-dose YYHF group （11.0 g·kg^-1·d^-1） and high-dose YYHF group （22.0 g·kg^-1·d^-1）， with 9 rats in each group. All groups received daily intragastric administration for 6 consecutive weeks. During the experimental period， the general condition of rats in each group was observed. After gavage administration finished， a fully automated biochemical analyzer was used to measure the 24-hour urine total protein （24 h-UTP） and serum biochemical parameters， including albumin （ALB）， aspartate aminotransferase （AST）， blood urea nitrogen （BUN）， serum creatinine （SCr）， total cholesterol （TC）， and triglyceride （TG）. Hematoxylin-eosin （HE） staining and periodic acid-silver metheramine （PASM） staining were used to observe the histopathological changes of renal tissues in each group of rats. The levels of superoxide dismutase （SOD） and malondialdehyde （MDA） in renal tissues were detected by ELISA. The protein expression levels of NOX4， SDF-1α， CXCR4， Nrf2， nuclear factor kappa B subunit p65 （NF-κB p65） and podocyte slit diaphragm protein nephrin in rat renal tissues were detected by Western Blotting. The mRNA expressions of NOX4， SDF-1α， CXCR4， Nrf2， NF-κB p65 and Nephrin in renal tissues were detected by real-time fluorescence quantitative polymerase chain reaction （RT-PCR）. ResultsCompared to the blank group， rats in the model group exhibited typical features of MN， including lethargy， reduced activity， dull and coarse fur， marked edema， and massive proteinuria. Compared to the model group， these abnormal conditions were improved to varying degrees in all treatment groups. The high-dose YYHF group showed a level of improvement comparable to that of the benazepril group， with overall recovery superior to that of the medium-dose YYHF group. Compared to the blank group， the model group showed increased levels of 24 h-UTP， BUN， SCr， TC and TG， decreased level of ALB， increased levels of MDA and protein and mRNA expressions of NOX4， SDF-1α， CXCR4 and NF-κB p65 in renal tissue， along with decreased levels of SOD and protein and mRNA expressions of Nrf2， Nephrin （P＜0.01）， accompanied by severe renal pathological damage. Compared to the model group， the benazepril and the medium- and high-dose YYHF groups showed significant improvements in the above indicators （P＜0.01）， along with alleviation of renal pathological injury. The 24 h-UTP level in the high-dose YYHF group was lower than that in the medium-dose group （P＜0.01）， while no statistically significant difference was observed among the other treatment groups （P＞0.01）. ConclusionYYHF may alleviate oxidative stress and inflammatory responses by down-regulating the expression of NOX4 and inhibiting the SDF-1α/CXCR4-Nrf2 signaling pathway， thereby improving podocyte damage and protecting renal function.

Objective: To examine the prevalence of e-cigarette use and associated factors among primary and secondary school students in Pudong New Area, Shanghai, and to explore the pathways linking harm perception of e-cigarettes, interpersonal social influence, and attitudes toward e-cigarette use with experimentation behavior, in order to provide scientific basis for further optimizing the prevention and control strategies of e-cigarette among adolescents. Methods: From September to October 2025, a multi stage cluster random sampling method was used to select 5 144 primary and secondary school students aged 8-19 years from 47 primary and secondary schools in Pudong New Area, Shanghai, to conduct an anonymous questionnaire survey. The questionnaire collected information on e-cigarette use, harm perception of e-cigarette, interpersonal social influence, and attitudes toward e-cigarette use. The Chi-square test was applied to analyze the differences in cigarette and e-cigarette use behavior among primary and secondary school students with different demographic characteristics. Structural equation modeling (SEM) was constructed using Mplus 8.3 software, and the bootstrap method was utilized to test the mediating effects. Results: The reported rates of cigarette experimentation, e-cigarette experimentation, and current e-cigarette use among primary and secondary school students were 1.85%, 2.10%, and 0.70%, respectively. Higher reporting rates of e-cigarette experimentation were observed among boys (2.82%), secondary school students aged 16-19 (3.39%), senior high school students (3.05%), and those with weekly pocket money >100 yuan ( 6.11% ) ( χ 2=11.67, 8.61, 8.00, 54.18, all P <0.05). Structural pathway analysis results demonstrated that e-cigarette harm perception positively predicted interpersonal social influence ( β =0.61) and e-cigarette use attitude ( β = 0.53 ), and interpersonal social influence ( β =0.65) and e-cigarette use attitude ( β =0.25) further promoted e-cigarette experimentation behavior among primary and secondary school students (all P <0.01), with interpersonal social influence playing a major mediating role (indirect effect=0.40). Conclusions E-cigarette experimentation behavior among primary and secondary school students in Pudong New Area is jointly influenced by multiple psychosocial factors. Intervention strategies should strengthen interpersonal social factors such as family and peers on the basis of health education, thereby constructing a comprehensive prevention and control strategy for e-cigarette use among primary and secondary school students.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

OBJECTIVE: To explore the efficacy and apoptosis of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in the treatment of acute myeloid leukemia (AML) with ASXL1 mutation. METHODS: The clinical data of 80 AML patients with ASXL1 mutation treated in our hospital from January 2019 to December 2021 were retrospectively analyzed. The clinical characteristics of the patients were summarized, and the therapeutic effect and prognostic factors of allo-HSCT for the patients were analyzed. RESULTS: Among the 80 patients, 38 were males and 42 were females, and the median age was 39(14-65) years. There were 17 patients in low-risk group, 25 patients in medium-risk group and 38 patients in high-risk group. ASXL1 mutation co-occurred with many other gene mutations, and the frequent mutated genes were TET2 (71.25%), NRAS (18.75%), DNMT3A (16.25%), NPM1 (15.00%), CEBPA (13.75%). Among medium and high-risk patients, 29 underwent allo-HSCT, while 34 received chemotherapy. The 2-year overall survival (OS) rate and disease-free survival (DFS) rate of the allo-HSCT group were 72.4% and 70.2%, while those of the chemotherapy group were 44.1% and 34.0%, respectively. The statistical analysis showed significant differences between the two groups (both P < 0.01). Multivariate analysis showed that age at transplantation >50- years and occurrence of acute graft-versus-host disease after transplantation were poor prognostic factors for OS and DFS in transplantation patients. CONCLUSION Allo-HSCT can improve the prognosis of AML patients with ASXL1 mutation.
Humans ; Leukemia, Myeloid, Acute/therapy* ; Hematopoietic Stem Cell Transplantation ; Female ; Male ; Middle Aged ; Mutation ; Adult ; Repressor Proteins/genetics* ; Adolescent ; Retrospective Studies ; Aged ; Nucleophosmin ; Young Adult ; Transplantation, Homologous ; Prognosis ; Survival Rate

Objective:To investigate the prevalence and associated factors of anxiety and depression in outpatients with chronic obstructive pulmonary disease (COPD) visiting the Yuetan Community Health Service Center in Beijing.Methods:This cross-sectional study enrolled COPD patients who visited the center between January 1, 2022, and December 31, 2023, using convenience sampling. After pulmonary function tests, participants completed questionnaires covering demographic characteristics, GOLD classification, number of hospitalizations for acute exacerbations in the past year, modified Medical Research Council (mMRC) dyspnea scale score, COPD Assessment Test (CAT) score, Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). The status and influencing factors of anxiety and depression were analyzed with univariate and multivariate Logistic regression.Results:A total of 243 COPD patients were included, with 74.1% (180/243) being male and a mean age of 67±9 years. The prevalence rates of anxiety and depression were 17.3% (42/243) and 21.0% (51/243), respectively. Multivariate logistic regression analysis identified the following independent factors associated with anxiety (all P<0.05): CAT score 31-40 ( OR=26.621, 95% CI: 4.451-159.209), mMRC score ≥2 ( OR=5.387, 95% CI: 1.589-18.209), GOLD grade ≥3 ( OR=3.227, 95% CI: 1.205-8.645), presence of ≥1 comorbidity ( OR=8.571, 95% CI: 1.132-64.920), being divorced/separated/widowed ( OR=3.125, 95% CI:1.369-7.136), and ≥1 hospitalization for acute exacerbation in the past year ( OR=3.523, 95% CI:1.422-8.726). Independent factors associated with depression (all P<0.05) were: CAT score ≥21 ( OR=3.326, 95% CI:1.038-10.653), mMRC score≥1 ( OR=7.468, 95% CI:1.706-32.697), GOLD grade ≥3 ( OR=3.160, 95% CI:1.225-8.148), presence of ≥1 comorbidity ( OR=5.624, 95% CI:1.295-24.422), smoking history ≥40 pack-years ( OR=2.689, 95% CI: 1.092-6.623), and ≥1 hospitalization for acute exacerbation in the past year ( OR=2.663, 95% CI:1.093-6.486). Conclusions:The prevalence of anxiety and depression among COPD outpatients in this Beijing community health center is considerable. CAT score, mMRC grade, GOLD grade, number of comorbidities, marital status, and history of hospitalization for acute exacerbation are risk factors of anxiety, while CAT score, mMRC grade, GOLD grade, number of comorbidities, smoking pack-years, and hospitalization history are risk factors for depression.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To evaluate the safety and effectiveness of fuzuloparib for the treatment of ovarian epithelial cancer patients in the real world setting.Methods:A retrospective analysis was conducted on the baseline data of 4 620 ovarian cancer patients who had received fuzuloparib monotherapy or combination therapy. Another 224 ovarian cancer patients who were willing to receive fuzuloparib monotherapy or combination therapy were prospectively enrolled, and their baseline characteristics, drug effectiveness, and safety data were analyzed.Results:(1) Among the 4 620 patients in the retrospective cohort, the median age of patients was 60 years; tumor types: 89.8% (4 149/4 620) had ovarian cancer. Among patients with clearly documented information, the vast majority had a histological type of serous carcinoma (82.9%, 3 770/4 546) and International Federation of Gynecology and Obstetrics (FIGO) staging of Ⅲ-Ⅳ (90.9%, 1 537/1 691). (2) Among the 224 patients in the prospective cohort, the median age of patients was 57 years; tumor types: 83.9% (188/224) had ovarian cancer. Among patients with clearly documented records, the predominant pathologic type was serous carcinoma (91.9%, 193/210), and FIGO stage was Ⅲ-Ⅳ in 79.9% (139/174). (3) Among the 224 prospective patients: 84 patients received first-line fluzoparib maintenance therapy, 92 patients received fluzoparib maintenance therapy after platinum-sensitive recurrence, 23 patients received direct fluzoparib treatment after platinum-sensitive recurrence, 19 patients received direct fluzoparib treatment after platinum-resistant recurrence. The median follow-up durations were 8.5, 8.7, 7.9, and 6.7 months, respectively. The median durations of fluzoparib treatment were 6.7, 4.8, 3.1, and 1.9 months, respectively. The median progression-free survival (PFS) times were not reached during follow-up, 12.6 months, not reached during follow-up, and 4.8 months, respectively. The 1-year PFS rates were 84.1%, 55.0%, 69.8%, and 45.5%, respectively. The remaining 6 patients received other fluzoparib regimens. (4) Among the 224 patients in the prospective dataset, 205 had safety data recorded. Of these, 127 patients (62.0%, 127/205) experienced treatment-related adverse events, with common events including anemia (24.4%, 50/205), thrombocytopenia (21.0%, 43/205), and leukopenia (19.5%, 40/205). Among the 205 patients, 43 (21.0%, 43/205) experienced grade 3 or higher treatment-related adverse events, with common events including anemia (8.3%, 17/205) and thrombocytopenia (8.3%, 17/205).Conclusions:The effectiveness of fuzuloparib in clinical application is generally consistent with other drugs in the same class, with good safety. This study provids new clinical evidence for the treatment of ovarian cancer with fuzuloparib.

SPF chickens were immunized with HN-mRNA vaccine by intramuscular injection.He-magglutination inhibition(HI)test,lymphocyte proliferation test,RT-qPCR and pathological sec-tions were used to evaluate the humoral and cellular immunity and protection against challenge in-duced by the candidate HN-mRNA vaccine.The results showed that 2.5,5.0,and 7.5 μg HN-mR-NA induced HI antibody antibodies in a dose-dependent manner.Among them,the Hi antibody in-duced by 7.5 μg HN-mRNA was slightly higher than that of the weak toxic vaccine(La Sota strain).In addition,in response to the stimulation of inactivated NDV virus,the proliferation and activation of lymphocytes in 2.5,5.0 and 7.5 μg HN-mRNA immunization groups and commercial vaccine group were observed.To further evaluate the antiviral protection provided by HN mRNA immunization,the 105 ELD50 NDV strong strain NA-1 was used to attack the 7.5 μg HN-mRNA immunised group and the commercial weakened vaccine(La Sota strain)and PBS groups using na-sal and ocular drops.The results showed that the survival rate of 7.5 μg HN-mRNA immunization group and commercial vaccine group was 100％,and these vaccines could protect tissues and or-gans from the damage caused by virus infection.At the same time,7.5 μg HN-mRNA and commer-cial vaccine could shorten the time of virus shedding in vitro and the viral load in vivo.This study provides a foundational framework for the clinical application of NDV HN mRNA candidate vac-cines and offers insights for the development of novel mRNA vaccines for poultry.