ObjectiveTo establish a risk recognition model for coronary artery stenosis by using a machine learning method and to identify the key causative factors. MethodsPatients aged ≥18 years，diagnosed with coronary heart disease through coronary angiography from January 2013 to May 2020 in two prominent hospitals in Shanxi Province， were continuously enrolled. Logistic regression，back propagation neural network （BPNN）， and random forest（RF）algorithms were used to construct models for detecting the causative factors of coronary artery stenosis. Sensitivity （TPR）， specificity （TNR）， accuracy （ACC）， positive predictive value （PV+）， negative predictive value （PV-）， area under subject operating characteristic curve （AUC）， and calibration curve were used to compare the discrimination and calibration performance of the models. The best model was then employed to predict the main risk variables associated with coronary stenosis. ResultsThe RF model exhibited superior comprehensive performance compared to logistic regression and BPNN models. The TPR values for logistic regression，BPNN，and RF models were 75.76%， 74.30%， and 93.70%， while ACC values were 74.05%， 72.30%， and 79.49%， respectively. The AUC values were：logistic regression 0.739 9； BPNN 0.723 1； RF 0.752 2. Manifestations such as chest pains，abnormal ST segments on ECG，ventricular premature beats with hypertension， atrial fibrillation， regional wall motion abnormalities（RWMA） by color echocardiography， aortic regurgitation（AR）， pulmonary insufficiency （PI）， family history of cardiovascular diseases，and body mass index（BMI）were identified as top ten important variables affecting coronary stenosis according to the RF model. ConclusionsRandom forest model shows the best comprehensive performance in identification and accurate assessment of coronary artery stenosis. The prediction of risk factors affecting coronary artery stenosis can provide a scientific basis for clinical intervention and help to formulate further diagnosis and treatment strategies so as to delay the disease progression.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective:To develop and verify a nomogram to predict disease-free survival(DFS)and overall survival(OS)for patients undergoing cervical cancer surgery,which may provide reference for evaluating the prognosis of cervical cancer patients undergoing surgery.Methods:The clinical,pathological and follow-up data of patients who underwent radical operation for cervical cancer in Xijing Hospital,Air Force Medical University from March 2013 to October 2018 were analyzed retrospectively.Based on Cox regression analysis,Bayesian Informa-tion Criterion(BIC)backward stepwise selection method and R square screening variables,Net Reclassification Index(NRI)and Integrated Discrimination Improvement(IDI)were used to compare the predictive efficiency of the model,and a nomogram with better predictive efficiency was selected.The consistency index(C-index)and the receiver operating characteristic curve(ROC)were used to test the efficiency of the nomogram.Results:A total of 950 patients with cervical cancer were enrolled in this study.The risk factors for constructing the DFS nomogram were FIGO stage(2018),parametrium invasion,invasion depth,and maximum tumor diameter.The C-index for DFS in the training cohort and the verification cohort were 0.754 and 0.720,respectively.The area under ROC of the training cohort for 1-,3-and 5-years was 0.74(95%CI 0.65-0.82),0.77(95%CI 0.71-0.83)and 0.79(95%CI0.74-0.85),and the areas under ROC of verification cohort 1-,3-and 5-years were 0.72(95%CI 0.58-0.87),0.75(95%CI 0.64-0.86)and 0.72(95%CI 0.61-0.84),respectively.The risk factors for con-structing the OS nomogram were FIGO stage(2018),histological type,LVSI,parametrium invasion,surgical mar-gin,and invasion depth.The C-index for OS in the training cohort and the verification cohort were 0.737 and 0.759,respectively.The area under ROC of the 3-and 5-year training cohort were 0.76(95%CI 0.69-0.83)and 0.78(95%CI 0.72-0.84),and the areas under ROC of verification cohort 3-and 5-years were 0.76(95%CI 0.65-0.87)and 0.79(95%CI 0.69-0.88),respectively.Conclusions:This study is based on real-world big data to construct nomogram of DFS for 1,3,and 5 years and OS for 3,and 5 years for cervical cancer,which have ideal predictive effects and help clinical physicians correctly evaluate the prognosis of cervical cancer surgery patients.It provides strong reference basis for diagnosis,treatment,and prognosis evaluation.

With the advent of an aging society,Alzheimer's disease(AD)has gradually become a major ailment affecting the elderly.AD is a neurodegenerative disorder associated with cognitive impairments.In AD patients,brain network connections are disrupted,and their topological properties are also affected,leading to the disintegration of anatomical and functional connections.Anatomical connections can be tracked and evaluated using structural magnetic imaging(MRI)and diffusion tensor imaging(DTI),while functional connections are detected through functional MRI to assess their connectivity status.This review incorporates the findings of previous scholars and summarizes the current research of AD.It mainly discusses the imaging characteristics of large-scale brain network changes in AD patients,so as to provide researchers with scientific and objective imaging markers for AD prediction and early diagnosis,as well as future research.

Due to the complexity of traditional Chinese medicine （TCM） interventions and the diversity of herbal components， single-omics technologies such as genomics， transcriptomics， proteomics， and metabolomics often cannot comprehensively elucidate the scientific connotations of TCM. Multi-omics technologies driven by system biology can analyze the theoretical connotations and application mechanisms of TCM from different levels such as genes， gene expression， proteins， and metabolites， in line with the holistic view of TCM， which helps to promote the modernization of TCM. By reviewing the literature on the application of omics technologies in the field of TCM， it is found that multi-omics technologies have been widely used in TCM for syndrome differentiation， evaluation of herbal quality， elucidation of pharmacological mechanisms， and drug toxicity assessment， providing comprehensive explanations of the mechanisms of action of TCM and overcoming the limitations of single-omics technologies， and having obtained significant achievements. However， multi-omics technologies also face challenges such as high cost， difficulties in data analysis due to large data volumes， and insufficient translation of research results. In the future， it is expected that through strengthening interdisciplinary cooperation， conducting long-term and dynamic clinical research， standardizing and normalizing data analysis processes， adopting appropriate and reasonable multi-omics integration patterns， establishing multi-omics databases for TCM， revealing the individualized characteristics， therapeutic mechanisms， and disease regulatory networks of TCM， the modernization of TCM will be promoted.

Transcatheter adrenal artery embolization(TAAE)is a minimally-invasive interventional treatment for adrenal diseases.Perioperative pain occurs mainly during and after operation.The standardized management of perioperative pain can reduce the occurrence of intraoperative adverse reactions,improve the degree of intraoperative cooperation,better the postoperative quality of life,thus,to improve patient satisfaction with interventional surgery.However,current researches among the healthcare staff mainly focus on the safety and efficacy of TAAE while neglect the standardized pain management during the perioperative period.Therefore,in order to improve the surgical efficacy of TAAE and optimize the perioperative surgical experience of patients,it is necessary to formulate a standardized pain management strategy and to apply this strategy in clinical practice.This paper aims to make a comprehensive review about the current situation of perioperative pain occurrence and pain management strategies in TAAE therapy,so as to provide reference for standardizing the perioperative pain management of interventional surgery,and improving the success rate of interventional surgery as well as the patient satisfaction with the treatment.(J Intervent Radiol,2024,33:679-683)

The gut microbiota is a complex ecosystem composed of many bacteria and their metabolites.It plays an irreplaceable role in human digestion,nutrient absorption,energy supply,fat metabolism,im-mune regulation,and many other aspects.Exploring the structure and function of the gut microbiota,as well as their key genes and metabolites,will enable the early diagnosis and auxiliary diagnosis of disea-ses,new treatment methods,better effects of drug treatments,and better guidance in the use of antibiot-ics.The identification of gut microbiota plays an important role in clinical diagnosis and treatment,as well as in drug research and development.Therefore,it is necessary to conduct a comprehensive review of this rapidly evolving topic.Traditional identification methods cannot comprehensively capture the di-versity of gut microbiota.Currently,with the rapid development of molecular biology,the classification and identification methods for gut microbiota have evolved from the initial phenotypic and chemical identi-fication to identification at the molecular level.This review integrates the main methods of gut microbiota identification and evaluates their application.We pay special attention to the research progress on molec-ular biological methods and focus on the application of high-throughput sequencing technology in the iden-tification of gut microbiota.This revolutionary method for intestinal flora identification heralds a new chapter in our understanding of the microbial world.

Objective To explore the influencing factors of gastric cancer and construct the clinical prediction model.Methods From December 2020 to October 2023,the clinical data of 1000 patients with stomach neoplasm admitted to Putuo Hospital,Shanghai U-niversity of Traditional Chinese Medicine and Shuguang Hospital,Shanghai University of Traditional Chinese Medicine were collected.Af-ter data cleaning and eliminating abnormal values,the patients were divided into gastric polyps group(n=487)and gastric cancer group(n=479).Non-parametric test was used to screen out meaningful indicators,Lasso regression to screen out the characteristic factors re-lated to gastric cancer with non-zero coefficient,and stepwise Logistic regression analysis to screen out the factors with significant correla-tion,and Lasso-Logistic regression model was constructed.The receiver operator characteristic(ROC)curve was plotted to calculate the area under the curve(AUC)and the confusion matrix to evaluate the model efficiency.Results The results of multivariate Logistic re-gression analysis showed that age,white blood cell(WBC)count,monocyte(M)count,alanine amiontransferase(ALT),cancer anti-gen 724(CA724),cancer antigen 242(CA242),cancer antigen 50(CA50)and carcinoembryonic antigen(CEA)were independent factors affecting gastric cancer.Based on the results of multivariate Logistic regression analysis,the risk prediction nomogram model of gas-tric cancer was constructed.The AUC of test set was 0.91,the accuracy rate was 100％,and the recall rate was 100％;the AUC of valida-tion set was 0.93,the accuracy rate was 93.63％,and the recall rate was 74.1％.The model has good prediction efficiency.Conclusion In this study,8 common predictors of gastric cancer were constructed,and the Lasso-logistic regression prediction model had good differen-tiation,which could be used to complete the early screening of gastric cancer based on the physical examination reports of patients.