ObjectiveTo investigate the therapeutic mechanism of Danhe granules in treating mixed hyperlipidemia based on network pharmacology， as well as animal and cell experiments. MethodsThe active compounds and targets of Danhe granules were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）. Related targets for mixed hyperlipidemia were obtained from the GeneCards database. The intersecting targets were subjected to Gene Ontology （GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. A high-fat model was established in human hepatocellular carcinoma cells （HepG2） induced by palmitic acid （PA）， followed by intervention with Danhe granules to assess intracellular lipid accumulation and oxidative stress levels. A mixed hyperlipidemia rat model was also established and divided into low-， medium-， and high-dose Danhe granules groups （1.134， 2.268， and 4.536 g·kg^-1， respectively）， as well as a positive control group treated with pravastatin sodium （4.020 mg·kg^-1）. After eight weeks of intervention， serum lipid levels， inflammatory factors， oxidative stress indices， and the expression of key hepatic lipid metabolism-related proteins were determined. ResultsNetwork pharmacology identified 93 intersecting targets between Danhe granules and mixed hyperlipidemia， with peroxisome proliferator-activated receptor gamma （PPARG）， peroxisome proliferator-activated receptor alpha （PPARA）， tumor necrosis factor （TNF）， interleukin-6 （IL-6）， and IL-1B among the key nodes. The PPAR signaling pathway， AGE/RAGE signaling pathway， lipid metabolism， atherosclerosis and non-alcoholic fatty liver disease （NAFLD） were among the most significantly enriched pathways. Cellular experiments demonstrated that Danhe granules significantly reduced reactive oxygen species （ROS） and malondialdehyde （MDA） levels while increasing catalase （CAT） activity （P<0.05）， thereby alleviating intracellular lipid accumulation and triglyceride （TG） content in HepG2. In animal experiments， Danhe granules markedly decreased serum total cholesterol （TC）， TG， and low-density lipoprotein cholesterol （LDL-C） levels （P<0.05）， reduced hepatic MDA levels， and elevated superoxide dismutase （SOD） and CAT levels. Histological analysis showed alleviation of hepatic steatosis， upregulation of hepatic PPARA and lipoprotein lipase （LPL） expressions， and downregulation of sterol regulatory element-binding protein 1 （SREBP1） expression （P<0.05， P<0.01）. ConclusionDanhe granules improve lipid metabolism disorders in mixed hyperlipidemia by reducing MDA levels， enhancing SOD and CAT activities， scavenging excessive ROS， inhibiting oxidative stress， and mitigating liver injury. The underlying mechanism may involve the upregulation of PPARA and LPL and the suppression of SREBP1 expression.

ObjectiveTo investigate the therapeutic mechanism of Danhe granules in treating mixed hyperlipidemia based on network pharmacology， as well as animal and cell experiments. MethodsThe active compounds and targets of Danhe granules were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）. Related targets for mixed hyperlipidemia were obtained from the GeneCards database. The intersecting targets were subjected to Gene Ontology （GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. A high-fat model was established in human hepatocellular carcinoma cells （HepG2） induced by palmitic acid （PA）， followed by intervention with Danhe granules to assess intracellular lipid accumulation and oxidative stress levels. A mixed hyperlipidemia rat model was also established and divided into low-， medium-， and high-dose Danhe granules groups （1.134， 2.268， and 4.536 g·kg^-1， respectively）， as well as a positive control group treated with pravastatin sodium （4.020 mg·kg^-1）. After eight weeks of intervention， serum lipid levels， inflammatory factors， oxidative stress indices， and the expression of key hepatic lipid metabolism-related proteins were determined. ResultsNetwork pharmacology identified 93 intersecting targets between Danhe granules and mixed hyperlipidemia， with peroxisome proliferator-activated receptor gamma （PPARG）， peroxisome proliferator-activated receptor alpha （PPARA）， tumor necrosis factor （TNF）， interleukin-6 （IL-6）， and IL-1B among the key nodes. The PPAR signaling pathway， AGE/RAGE signaling pathway， lipid metabolism， atherosclerosis and non-alcoholic fatty liver disease （NAFLD） were among the most significantly enriched pathways. Cellular experiments demonstrated that Danhe granules significantly reduced reactive oxygen species （ROS） and malondialdehyde （MDA） levels while increasing catalase （CAT） activity （P<0.05）， thereby alleviating intracellular lipid accumulation and triglyceride （TG） content in HepG2. In animal experiments， Danhe granules markedly decreased serum total cholesterol （TC）， TG， and low-density lipoprotein cholesterol （LDL-C） levels （P<0.05）， reduced hepatic MDA levels， and elevated superoxide dismutase （SOD） and CAT levels. Histological analysis showed alleviation of hepatic steatosis， upregulation of hepatic PPARA and lipoprotein lipase （LPL） expressions， and downregulation of sterol regulatory element-binding protein 1 （SREBP1） expression （P<0.05， P<0.01）. ConclusionDanhe granules improve lipid metabolism disorders in mixed hyperlipidemia by reducing MDA levels， enhancing SOD and CAT activities， scavenging excessive ROS， inhibiting oxidative stress， and mitigating liver injury. The underlying mechanism may involve the upregulation of PPARA and LPL and the suppression of SREBP1 expression.

ObjectiveTo investigate the mechanism by which Jianpi Xiao'ai prescription （JPXAP） inhibits colorectal cancer progression by regulating the inducible nitric oxide synthase-arginase 1 （iNOS-ARG1） metabolic axis and inducing mitochondrial reactive oxygen species （mito-ROS）-mediated mitochondrial structural and functional impairment. MethodsAn arginine metabolism disorder model of human colorectal cancer HCT116 cells was established by combined treatment with recombinant human interferon-γ （IFN-γ， 10 μg·L^-1） and N（ω）-hydroxy-L-arginine （Nor-NOHA， 200 μmol·L^-1） for 24 h， followed by intervention with 5%， 10%， or 20% JPXAP-containing serum. Cell proliferation was assessed using cell counting kit-8 （CCK-8）， 5-ethynyl-2′-deoxyuridine （EdU） staining， and colony formation assays. Cell invasion and migration were evaluated using Transwell chamber and wound healing assays. Mitochondrial membrane potential （MMP） and ROS levels were assessed by JC-1 and MitoSOX staining， respectively. Mitochondrial ultrastructure was observed by transmission electron microscopy （TEM）. The expression of iNOS， ARG1， and mitochondrial dynamics-related proteins， including mitofusin 2 （MFN2） and dynamin-related protein 1 （DRP1）， was analyzed by Western blot and immunofluorescence. The levels of L-arginine， citrulline， and urea were determined by colorimetric methods and enzyme-linked immunosorbent assay （ELISA）. ResultsCompared with the blank group， the model group exhibited significantly upregulated iNOS expression， downregulated ARG1 expression， a decreased ARG1/iNOS ratio， reduced L-arginine and urea levels， and increased citrulline levels （P<0.05）. Meanwhile， mito-ROS accumulation was significantly increased， the JC-1 red/green fluorescence ratio was decreased， and mitochondria showed swelling and cristae disruption， indicating that metabolic disorder induced mitochondrial injury. Compared with the model group， all JPXAP-treated groups further decreased the ARG1/iNOS ratio， enhanced nitric oxide （NO） and reactive nitrogen species accumulation， further reduced L-arginine and urea levels， and increased citrulline levels （P<0.01）. EdU-positive rate， colony formation rate， wound healing rate， and Transwell invasion number all decreased significantly with increasing serum concentration （P<0.01）. Mito-ROS levels were further elevated， and the JC-1 red/green ratio further decreased. TEM revealed aggravated mitochondrial swelling and vacuolization. MFN2 expression was downregulated and DRP1 expression was upregulated （P<0.01），in a dose-dependent manner. ConclusionJPXAP further activates NO-mediated oxidative/nitrosative stress under arginine metabolism imbalance， inducing mito-ROS accumulation， MMP collapse， and mitochondrial dynamics imbalance， thereby inhibiting colorectal cancer cell proliferation and migration. These findings reveal an antitumor mechanism of JPXAP based on coordinated targeting of the "metabolism-mitochondria" axis.

Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

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: 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: 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: 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 evaluate the efficacy and safety of the Chi-BEAM regimen (chidamide combined with carmustine, etoposide, cytarabine, and melphalan) followed by autologous hematopoietic stem cell transplantation (ASCT) in patients with high-risk or relapsed/refractory lymphoma.Methods:This retrospective case series included 78 patients with newly treated high-risk or relapsed/refractory lymphoma who underwent ASCT with the Chi-BEAM conditioning regimen in the Department of Hematology, the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), from June 2021 to May 2024. Descriptive statistics were employed to evaluate clinical characteristics, efficacy, and adverse events. The Kaplan-Meier method was applied to calculate cumulative progression-free survival (PFS) and overall survival (OS) rates.Results:The median age of the 78 evaluable patients was 47 years (range 16-68), with 8 patients (10.3%) aged ≥60 years. At the first post-transplant assessment (3 months), the objective response rate was 94.9% (74/78). The median follow-up was 20.1 months (range 2.9-44.9). The median PFS time was 20.1 months (range 1.6-45.1), with a 2-year cumulative PFS rate of 81.8%. The median OS time was 20.6 months (range 3.1-45.1), with a cumulative 2-year OS rate of 93.2%. The regimen was well-tolerated; mild-to-moderate hypocalcemia within 1 week post-infusion and transient mild erythrocyturia on the infusion day were the primary adverse reactions.Conclusion:The Chi-BEAM regimen combined with ASCT demonstrates both safety and clinical benefit in patients with high-risk or relapsed/refractory lymphoma.

Objective:To construct an evaluation indicator system for accessories of patient monitor,so as to provide a basis for clinical management,equipment procurement,and technical improvement for accessories of medical monitor.Methods:The initial selection indicators of corresponding accessories of three types of monitoring of medical monitors,including electrocardiogram(ECG),blood oxygen saturation(SpO2)and non-invasive blood pressure(NIBP),were determined through literature research,expert consultation,and actual investigation.The Delphi method was adopted to conduct two rounds of questionnaire consultation for experts from clinical medicine,biomedical engineering and other fields in medical institutions included Sichuan Provincial People's Hospital,The Affiliated Hospital of Southwest Medical University and Medical Institute of Chengdu Institute of Metrology Verification and Testing.The evaluation indicators were screened and optimized,and the Analytic Hierarchy Process(AHP)was used to calculate the weights of each indicator.The consistency test was conducted to verify the rationality of the evaluation indicator system.Results:The evaluation indicator system for accessories of medical monitor included three first-level indicators:clinical value,cost value,and management value.The number of second-level indicators about ECG,SpO2 and NIBP of evaluation indicator system were respectively 10,9 and 8,and the number of third-level indicators of that were respectively 20,19,and 14.In the first-level indicators,the clinical value had the highest weight,with 72.49%for ECG,70.88%for SpO2 and 70.32%for NIBP.In the second-level indicators,the accuracy(28.70%for ECG,38.13%for SpO2 and 43.03%for NIBP)and safety(27.47%for ECG,26.48%for SpO2 and 23.06%for NIBP)were the core indicators.The weights of cost value and management value were between 14.62%and 17.41%,and between 12.27%and 12.89%,respectively.Conclusion:The evaluation indicator system for accessories of medical monitor integrates multi-dimensional expert opinions and quantitative analysis,highlights the priority principle for clinical performance.It can provide theoretical support for optimizing selection about accessory for medical institutions,and improving quality of monitoring,and promoting standardized management in the industry.