ObjectiveTo investigate the effects of salidroside （SAL） on the impaired bioactivity of endothelial progenitor cells （EPCs） in atherosclerotic （As） mice and the potential mechanisms regarding AMP-activated protein kinase （AMPK）. MethodsAtherosclerosis was induced in 8-week-old male ApoE^-/- mice with high-fat diet. Intragastric administration of SAL was given to one mice group to investigate the effects of SAL on aortic plaque burden， plasma NO level， the migration and angiogenic capabilities of bone marrow-derived EPCs （BM-EPCs）. The proliferation， migration and vasculogenic properties of EPCs isolated from As mice were investigated in vitro. AMPK-sh-RNA or the AMPK inhibitor Compound C was used to investigate the role of AMPK/Akt/eNOS pathway in the regulatory effects of SAL. ResultsCompared with As group， NO level was significantly elevated in SAL group. The sizes of atherosclerotic plaques at the aortic root were reduced with smaller lipid cores in SAL group compared with As group. Moreover， the migration and angiogenesis capacity of EPCs markedly decreased in As mice， while SAL treatment reversed these impairments. Incubation with SAL at concentrations of 20， 40， and 80 μmol/L for 48 hours significantly promoted the proliferation， migration， and angiogenesis of EPCs. AMPK-sh-RNA transfection abrogated the 20 μmol/L SAL improvement in EPC biological activities. Western blot analysis further demonstrated that treatment with Compound C blocked the activation of AMPK/Akt/eNOS signaling pathway induced by SAL. ConclusionSAL upregulates the biological functions of EPCs through activating the AMPK/Akt/eNOS signaling pathway， thereby ameliorating EPC dysfunction during the pathological progression of atherosclerosis.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective To investigate the risk factor for adverse events(AEs)after non-sedated esophagogastroduodenoscopy(EGD).Methods The data on clinical manifestations,adverse events after non-sedated EGD and common risk factors were collected and retrospectively analyzed with statistical methods in patients who underwent non-sedated EGD from May 2018 to June 2019.These patients were divided into AEs group and non-AEs group.Results Of 2 384 patients,57.67%(1 375/2 384)presented with nausea,12.79%(305/2 384)vomiting,and 5.79%(138/2 384)presented with pharyngalgia.Multivariate Logistic regression analysis was performed.Advanced age(≥65 years old)(OR=0.683,95%CI:0.506-0.921)was protective factors for nausea after non-sedated EGD.Hypertension(OR=1.361,95%CI:1.026-1.806),overweight(OR=1.399,95%CI:1.154-1.695),obesity(OR=2.594,95%CI:1.760-3.823)and inspection duration>15 min(OR=3.107,95%CI:2.296-4.206)were independent risk factors for nausea after non-sedated EGD.Advanced age(OR=0.393,95%CI:0.221-0.699)and imported equipment(OR=0.697,95%CI:0.546-0.890)were protective factors for vomiting after non-sedated EGD.Moreover,inspection duration>15 min(OR=1.641,95%CI:1.008-2.699)was independent risk factors for vomiting after non-sedated EGD.There was no difference in success rate of non-sedated EGD between two groups(P<0.05).Conclusion Hypertension,overweight and obesity were independent risk factors for nausea after non-sedated EGD.The advanced age and imported equipment were protective factors for vomiting after non-sedated EGD.In addition,inspection duration over 15 min is a risk factor for AEs such as nausea and vomiting after nonsedative EGD.Whether AEs occurred or not is non-related to success rate of non-sedated EGD.

Gastric cancer is a common clinical tumor,and its incidence and mortality rates rank among the top of malignant tumors.Helicobacter pylori(Hp)is an important carcinogenic factor for gastric cancer.Studies have found that in addition to Hp,other microorganisms in the stomach also play a key role in the progression of gastric cancer.The composition and diversity of the gastric microbiota vary significantly under different gastric disease conditions,which may affect the progression of gastric cancer.Hp can induce gastric epithelial inflammation and oxidative stress through various virulence factors,thereby promoting the occurrence of gastric cancer.Non-Hp microorganisms can be involved in the process of gastric cancer through pathways such as metabolic changes and immune regulation.In recent years,with the development of high-throughput sequencing technology,the complexity of gastric microbiota has gradually been revealed,which provides new insights for the early warning and intervention of gastric cancer.This article comprehensively review the role of gastric microbiota in the progression of gastric cancer.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To investigate the risk factor for adverse events(AEs)after non-sedated esophagogastroduodenoscopy(EGD).Methods The data on clinical manifestations,adverse events after non-sedated EGD and common risk factors were collected and retrospectively analyzed with statistical methods in patients who underwent non-sedated EGD from May 2018 to June 2019.These patients were divided into AEs group and non-AEs group.Results Of 2 384 patients,57.67%(1 375/2 384)presented with nausea,12.79%(305/2 384)vomiting,and 5.79%(138/2 384)presented with pharyngalgia.Multivariate Logistic regression analysis was performed.Advanced age(≥65 years old)(OR=0.683,95%CI:0.506-0.921)was protective factors for nausea after non-sedated EGD.Hypertension(OR=1.361,95%CI:1.026-1.806),overweight(OR=1.399,95%CI:1.154-1.695),obesity(OR=2.594,95%CI:1.760-3.823)and inspection duration>15 min(OR=3.107,95%CI:2.296-4.206)were independent risk factors for nausea after non-sedated EGD.Advanced age(OR=0.393,95%CI:0.221-0.699)and imported equipment(OR=0.697,95%CI:0.546-0.890)were protective factors for vomiting after non-sedated EGD.Moreover,inspection duration>15 min(OR=1.641,95%CI:1.008-2.699)was independent risk factors for vomiting after non-sedated EGD.There was no difference in success rate of non-sedated EGD between two groups(P<0.05).Conclusion Hypertension,overweight and obesity were independent risk factors for nausea after non-sedated EGD.The advanced age and imported equipment were protective factors for vomiting after non-sedated EGD.In addition,inspection duration over 15 min is a risk factor for AEs such as nausea and vomiting after nonsedative EGD.Whether AEs occurred or not is non-related to success rate of non-sedated EGD.

Gastric cancer is a common clinical tumor,and its incidence and mortality rates rank among the top of malignant tumors.Helicobacter pylori(Hp)is an important carcinogenic factor for gastric cancer.Studies have found that in addition to Hp,other microorganisms in the stomach also play a key role in the progression of gastric cancer.The composition and diversity of the gastric microbiota vary significantly under different gastric disease conditions,which may affect the progression of gastric cancer.Hp can induce gastric epithelial inflammation and oxidative stress through various virulence factors,thereby promoting the occurrence of gastric cancer.Non-Hp microorganisms can be involved in the process of gastric cancer through pathways such as metabolic changes and immune regulation.In recent years,with the development of high-throughput sequencing technology,the complexity of gastric microbiota has gradually been revealed,which provides new insights for the early warning and intervention of gastric cancer.This article comprehensively review the role of gastric microbiota in the progression of gastric cancer.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

This article summarized the experience of Professor Xiong Jibai,a national TCM master,in treating pulmonary nodules based on the theory of"body fluids and blood stasis mixing"in Huang Di Nei Jing.Professor Xiong Jibai believes that the basic pathogenesis of pulmonary nodules is that"body fluids and blood stasis mixing"accumulate in lung collaterals,and the fundamental pathological factor is phlegm and blood stasis.Xiong's treatment is based on dissipating phlegm and activating qi,activating blood circulation and resolving masses,paying attention to syndrome differentiation and treatment,examining syndromes and seeking causes,flexibly selecting prescriptions and treating both symptoms and root causes;attaching importance to maintaining healthy qi,preventing both illness and change,and preventing recovery after illness.Clinical medical records were attached to prove the clinical thinking and medication characteristics.