ObjectiveTo investigate whether Shenmai injection （SMI） improves cisplatin resistance in non-small cell lung cancer （NSCLC） by modulating lipid metabolism and inducing ferroptosis. MethodsHuman lung adenocarcinoma cisplatin-resistant A549/DDP cells were divided into the following groups： Blank group， cisplatin group （23.3 μmol·L^-1 cisplatin）， SMI group （20 g·L^-1 SMI）， cisplatin combined with SMI group （23.3 μmol·L^-1 cisplatin + 20 g·L^-1 SMI）， cisplatin combined with ferroptosis inhibitor/inducer Ferrostatin-1/Erastin group （23.3 μmol·L^-1 cisplatin + 10 μmol·L^-1 Ferrostatin-1/5 μmol·L^-1 Erastin）， and cisplatin combined with SMI and Ferrostatin-1/Erastin group （23.3 μmol·L^-1 cisplatin + 20 g·L^-1 SMI + 10 μmol·L^-1 Ferrostatin-1/5 μmol·L^-1 Erastin）. Network pharmacology， transcriptomics and metabolomics， Cell Counting Kit-8 （CCK-8） assay， transmission electron microscopy （TEM）， colorimetric assays， and Western blot analysis were employed to evaluate the effects of these treatments on A549/DDP cell viability， lipid droplet formation， lipid metabolite levels， mitochondrial function， lipid peroxidation， glutathione （GSH） content， total and ferrous iron content， and effects on ferroptiosis and autophagy related protein expression levels. ResultsSMI improved cisplatin resistance in NSCLC mainly by targeting lipid metabolism-related pathways in A549/DDP cells， affecting tumor cell lipid metabolism via autophagy， ferroptosis， and glycerophospholipid metabolism pathways. Compared with the cisplatin group， the cisplatin combined with SMI group showed significantly decreased cell viability （P<0.01）， increased lipid droplet accumulation （P<0.01）， and reduced mitochondrial maximal respiration， basal respiration， mitochondrial membrane potential， GSH content， total iron， and ferrous iron （all P<0.01）. Mitochondrial reactive oxygen species （ROS） was significantly elevated（P<0.01）， and lipid peroxidation levels were significantly increased. Protein expression analysis showed significant downregulation of solute carrier family 7 member 11 （SLC7A11） and p62 （P<0.05，P<0.01） and upregulation of ferritin heavy chain （FTH） and microtubule-associated protein 1 light chain 3Ⅱ （LC3Ⅱ） （P<0.05，P<0.01）. Compared with the cisplatin combined with SMI group， addition of Ferrostatin-1 significantly increased cell viability （P<0.05）， decreased mitochondrial ROS levels （P<0.05）， alleviated mitochondrial shrinkage， and reduced lipid peroxidation. Conversely， addition of Erastin further decreased cell viability （P<0.01）. ConclusionSMI improves cisplatin resistance in NSCLC by inducing oxidative stress， which may trigger ferroptosis through upregulation of lipophagy.

This study aims to integrate data mining techniques of single cell transcriptomics and spatial transcriptomics, along with animal experiment validation, so as to systematically characterize the protective effects of Jianpi Tongluo Formula(JTF) on the cartilage in knee osteoarthritis(KOA) and elucidate the underlying molecular mechanisms. Single cell transcriptomics and spatial transcriptomics datasets(GSE254844 and GSE255460) of the cartilage tissue obtained from KOA patients were analyzed to map the single cell-spatial heterogeneity and identify key pathogenic factors. After that, a KOA rat model was established via knee joint injection of papain. The intervention effects of JTF on the expression features of these key factors were assessed through real-time quantitative polymerase chain reaction(PCR), Western blot, and immunohistochemical staining. As a result, the integrated single cell and spatial transcriptomics data identified distinct cell subsets with different pathological changes in different regions of the inflamed cartilage tissue in KOA, and their differentiation trajectories were closely related to the inflammatory fibrosis-like pathological changes of chondrocytes. Accordingly, the expression levels of the two key effect targets, namely nuclear receptor coactivator 4(NCOA4) and high mobility group box 1(HMGB1) were significantly reduced in the articular surface and superficial zone of the inflamed joints when JTF effectively alleviated various pathological changes in KOA rats, thus reversing the abnormal chondrocyte autophagy level, relieving the inflammatory responses and fibrosis-like pathological changes, and promoting the repair of chondrocyte function. Collectively, this study revealed the heterogeneous characteristics and dynamic changes of inflamed cartilage tissue in different regions and different cell subsets in KOA patients. It is worth noting that NCOA4 and HMGB1 were crucial in regulating chondrocyte autophagy and inflammatory reaction, while JTF could reverse the regulation of NCOA4 and HMGB1 and correct the abnormal molecular signal axis in the target cells of the inflamed joints. The research can provide a new research idea and scientific basis for developing a personalized therapeutic schedule targeting the spatiotemporal heterogeneity characteristics of KOA.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Osteoarthritis, Knee/pathology* ; Humans ; Male ; Cartilage, Articular/metabolism* ; Chondrocytes/metabolism* ; Rats, Sprague-Dawley ; Female ; Protective Agents/administration & dosage* ; Single-Cell Analysis ; Middle Aged ; HMGB1 Protein/metabolism*

ObjectiveTo investigate the ameliorative effect of Xingpi capsules on functional dyspepsia（FD） and the potential mechanism. MethodsSixty SPF-grade male SD neonatal rats（7 days old） were randomly divided into the normal group（n=12） and the modeling group（n=48）， and the FD model was prepared by iodoacetamide gavage in the modeling group. After the model was successfully prepared， the rats in the modeling group were randomly divided into the model group， the low-dose and high-dose groups of Xingpi capsules（0.135， 0.54 g·kg^-1） and the domperidone group（3 mg·kg^-1）， with 12 rats in each group. Rats in the normal and model groups were gavaged with distilled water， and rats in the rest of the groups were gavaged with the corresponding medicinal solution， once a day for 7 d. The general survival condition of the rats was observed， and the water intake and food intake of the rats were measured， the gastric emptying rate and the small intestinal propulsion rate were measured at the end of the treatment， the pathological damage of the rat duodenum was examined by hematoxylin-eosin（HE） staining， and the expressions of colonic tight junction protein（Occludin） and zonula occludens protein-1（ZO-1） were detected by immunofluorescence. The differentially expressed genes in the duodenal tissues of the model group and the normal group， and the high-dose group of Xingpi capsules and the model group were detected by transcriptome sequencing after the final administration， and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses were carried out. The transcriptomic results were validated by Western blot， immunofluorescence， and real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）， and the active ingredients of Xingpi capsules were screened for molecular docking with the key targets. ResultsCompared with the normal group， the general survival condition of rats in the model group was poorer， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly reduced（P<0.05）， inflammatory infiltration was seen in duodenal pathology， and the fluorescence intensities of Occludin and ZO-1 in the colon were significantly reduced（P<0.01）. Compared with the model group， the general survival condition of rats in the high-dose group of Xingpi capsules improved significantly， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly increased（P<0.05）， the duodenal pathology showed a decrease in inflammatory infiltration， and the fluorescence intensities of colonic Occludin and ZO-1 were significantly increased（P<0.01）. Transcriptomic results showed that Xingpi capsules might exert therapeutic effects by regulating the phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） through the key genes such as Slc5a1， Abhd6. The validation results showed that compared with the normal group， the phosphorylation levels of PI3K and Akt proteins， the protein expression level of interleukin（IL）-1β， and the fluorescence intensities of IL-6 and IL-1β were significantly increased in the model group（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3， Slc5a9 and other key genes were significantly increased（P<0.01）. Compared with the model group， the phosphorylation levels of PI3K and Akt， the protein expression level of IL-1β and the fluorescence intensities of IL-6 and IL-1β in the high-dose group of Xingpi capsules were significantly reduced（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3 and Slc5a9 were significantly reduced（P<0.05）. Weighted gene co-expression network analysis and molecular docking results showed that E-nerolidol and Z-nerolidol in Xingpi capsules were well bound to ABDH6 protein， and linarionoside A， valerosidatum and senkirkine were well bound to Slc5a1 protein. ConclusionXingpi capsules can effectively improve the general survival and gastrointestinal motility of FD rats， its specific mechanism may be related to the inhibition of PI3K/Akt signaling pathway to alleviate the low-grade inflammation of duodenum， and E-nerolidol， Z-nerolidol， linarionoside A， valerosidatum and senkirkine may be its key active ingredients.

ObjectiveTo investigate the ameliorative effect of Xingpi capsules on functional dyspepsia（FD） and the potential mechanism. MethodsSixty SPF-grade male SD neonatal rats（7 days old） were randomly divided into the normal group（n=12） and the modeling group（n=48）， and the FD model was prepared by iodoacetamide gavage in the modeling group. After the model was successfully prepared， the rats in the modeling group were randomly divided into the model group， the low-dose and high-dose groups of Xingpi capsules（0.135， 0.54 g·kg^-1） and the domperidone group（3 mg·kg^-1）， with 12 rats in each group. Rats in the normal and model groups were gavaged with distilled water， and rats in the rest of the groups were gavaged with the corresponding medicinal solution， once a day for 7 d. The general survival condition of the rats was observed， and the water intake and food intake of the rats were measured， the gastric emptying rate and the small intestinal propulsion rate were measured at the end of the treatment， the pathological damage of the rat duodenum was examined by hematoxylin-eosin（HE） staining， and the expressions of colonic tight junction protein（Occludin） and zonula occludens protein-1（ZO-1） were detected by immunofluorescence. The differentially expressed genes in the duodenal tissues of the model group and the normal group， and the high-dose group of Xingpi capsules and the model group were detected by transcriptome sequencing after the final administration， and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses were carried out. The transcriptomic results were validated by Western blot， immunofluorescence， and real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）， and the active ingredients of Xingpi capsules were screened for molecular docking with the key targets. ResultsCompared with the normal group， the general survival condition of rats in the model group was poorer， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly reduced（P<0.05）， inflammatory infiltration was seen in duodenal pathology， and the fluorescence intensities of Occludin and ZO-1 in the colon were significantly reduced（P<0.01）. Compared with the model group， the general survival condition of rats in the high-dose group of Xingpi capsules improved significantly， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly increased（P<0.05）， the duodenal pathology showed a decrease in inflammatory infiltration， and the fluorescence intensities of colonic Occludin and ZO-1 were significantly increased（P<0.01）. Transcriptomic results showed that Xingpi capsules might exert therapeutic effects by regulating the phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） through the key genes such as Slc5a1， Abhd6. The validation results showed that compared with the normal group， the phosphorylation levels of PI3K and Akt proteins， the protein expression level of interleukin（IL）-1β， and the fluorescence intensities of IL-6 and IL-1β were significantly increased in the model group（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3， Slc5a9 and other key genes were significantly increased（P<0.01）. Compared with the model group， the phosphorylation levels of PI3K and Akt， the protein expression level of IL-1β and the fluorescence intensities of IL-6 and IL-1β in the high-dose group of Xingpi capsules were significantly reduced（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3 and Slc5a9 were significantly reduced（P<0.05）. Weighted gene co-expression network analysis and molecular docking results showed that E-nerolidol and Z-nerolidol in Xingpi capsules were well bound to ABDH6 protein， and linarionoside A， valerosidatum and senkirkine were well bound to Slc5a1 protein. ConclusionXingpi capsules can effectively improve the general survival and gastrointestinal motility of FD rats， its specific mechanism may be related to the inhibition of PI3K/Akt signaling pathway to alleviate the low-grade inflammation of duodenum， and E-nerolidol， Z-nerolidol， linarionoside A， valerosidatum and senkirkine may be its key active ingredients.

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: 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: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies