Objective To analyze the characteristics of viral hepatitis mortality and life loss among residents in Pudong New Area from 2003 to 2023, and to provide a basis for related prevention and control work. Methods Viral hepatitis mortality data were obtained from the Pudong New Area mortality monitoring system. The crude mortality rate (CMR), standardized mortality rate (SMR), potential years of life lost (PYLL), average years of life lost (AYLL), and standardized potential years of life lost (SPYLL) were calculated to analyze viral hepatitis deaths. The average annual change (AAPC) and annual percentage change (APC) of the mortality rate were calculated by Joinpoint regression analysis to analyze the trend of mortality. Results The CMR and SMR of viral hepatitis among residents in Pudong New Area from 2003 to 2023 were 3.89/100000 and 1.98/100000, respectively. Both CMR and SMR of viral hepatitis showed a decreasing trend over time (CMR:APC=-5.476, t=-13.581, P<0.001; SMR:APC=- 7.624, t= -21.253, P<0.001). The CMR for males was 4.75/100000 and the SMR for males was 2.65/100000; the CMR for females was 3.04/100000 and the SMR for females was 1.32/100000, with a higher mortality rate for males than for females（Z_CME=12.094，P<0.001; Z_SMR=-14.718，P<0.001). Deaths were concentrated in the age groups of 45-64 years old and 65 years old and above, accounting for 91.62% of the total deaths. The PYLL of deaths due to viral hepatitis among residents in Pudong New Area from 2003 to 2023 was 26912 person-years, with a PYLLR of 0.45% and an AYLL of 8.88 years per person. Conclusion The mortality rate of viral hepatitis among the residents of Pudong New Area in 2003-2023 shows a decreasing trend over time. The mortality rate of males is higher than that of females, and the deaths of middle-aged and elderly people account for a large proportion of the total deaths. Chronic hepatitis B is the main cause of death.

BACKGROUND: Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS. METHODS: This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide. RESULTS: Eighty-two patients were assigned to variant ( n  = 42) and wild type groups ( n  = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] μg/mL; wild type, 49.1 [32.0] μg/mL) and area under plasma concentration-time curve over a dosing interval (AUC tau ) (variant, 1731.3 [769.0] μg∙h/mL; wild type, 1564.5 [1053.0] μg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study. CONCLUSION: ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients. REGISTRATION NCT04410965, https://clinicaltrials.gov .
Humans ; Crotonates/adverse effects* ; Toluidines/adverse effects* ; Nitriles ; Hydroxybutyrates ; Female ; Male ; Adult ; ATP Binding Cassette Transporter, Subfamily G, Member 2/genetics* ; Middle Aged ; Multiple Sclerosis, Relapsing-Remitting/genetics* ; Prospective Studies ; Young Adult ; Neoplasm Proteins/genetics* ; East Asian People

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective:To explore the effect of ezetimibe combined with atorvastatin on the efficacy,blood lipids,ca-rotid ultrasound indicators in patients with coronary heart disease(CHD)and its safety.Methods:This randomized controlled study enrolled 98 CHD patients admitted to 945th Hospital of the PLA Joint Logistic Support Force be-tween June 2021 and June 2023.Patients were divided into intervention group and control group with 49 cases in each group.Patients in the control group was treated with atorvastatin-bascd routine medication comparing to those in intervention group receiving additional ezetimibe,both groups were treated for 90 d.Clinical efficacy,blood lipids,carotid ultrasound indicators,endothelial function indicators,and incidence of adverse reactions were compared between two groups.Results:Compared with patients in the control group,those in the intervention group had significant higher total effective rate(91.83％vs.73.47％,P=0.016).Compared with patients in the control group after treatment,those in intervention group had significant lower levels of low density lipoprotein cho-lesterol(LDL-C)[(2.74±0.61)mmol/L vs.(3.42±0.66)mmol/L],total cholesterol(TC)[(3.80±0.89)mmol/L vs.(4.69±1.02)mmol/L],triglyceride(TG)[(1.79±0.53)mmol/L vs.(2.35±0.62)mmol/L],re-sistance index(RI)[(52.02±6.32)％vs.(57.95±6.02)％],carotid intima-media thickness(IMT)[(0.91±0.17)mm vs.(1.08±0.24)mm],von Willebrand factor(vWF)[(19.03±3.76)mg/L vs.(23.41±4.42)mg/L],angiotensin Ⅱ(Ang Ⅱ)[(45.83±5.87)ng/L vs.(52.87±6.01)ng/L](P＜0.001 all);and significant high-er high density lipoprotein cholesterol(HDL-C)[(1.63±0.32)mmol/L vs.(1.35±0.27)mmol/L],peak systol-ic velocity(PSV)[(47.93±5.26)cm/s vs.(41.32±4.98)cm/s],end-diastolic velocity(EDV)[(36.14±5.10)cm/s vs.(30.73±4.48)cm/s],pulse index(PI)[(85.98±9.03)％vs.(78.42±8.82)％],vascular endothelial growth factor receptor 1(VEGFR1)[(289.14±32.98)ng/L vs.(258.34±29.32)ng/L](P＜0.001 all).There was no significant difference in the incidence of adverse reactions between the two groups(P=0.538).Conclusion:Ezetimibe combined with atorvastatin possesses significant therapeutic effect on CHD patients,which could signifi-cantly reduce blood lipids,improve the carotid blood flow velocity and vascular endothelial function with good safety.

[Purpose]To analyze the trends in pancreatic cancer mortality and disease burden among residents in Shanghai Pudong New Area from 2002 to 2022,and to investigate the effects of age,period,and birth cohort on mortality risk.[Methods]Data on pancreatic cancer deaths among residents of Pudong New Area from 2002 to 2022 were collected through the Shanghai Population Cause of Death Registration System.The crude mortality rate,age-standardized mortality rate by Chinese standard population(ASMRC),potential years of life lost(PYLL),potential years of life lost rate(PYLLR),and average years of life lost(AYLL)were calculated.Joinpoint regression was applied to calculate the average annual percentage change(AAPC)for analyzing the changing trend of the mortality rate of pancreatic cancer.The age-period-cohort model was applied with R 4.4.1 to analyze the age,period,and cohort effects on the mortality risk of pancreatic cancer.[Results]The crude mortality rate of pancreatic cancer among residents in Pudong New Area increased from 10.42/105 in 2002 to 18.73/105 in 2022,showing a significant upward trend(AAPC=2.90％,P＜0.001);the ASMRC was generally stable(AAPC=-0.05％,P=0.775).The crude mortality rate of males(17.09/105)was higher than that of females(13.75/105),and both showed an upward trend(AAPC=3.05％and 2.75％respectively,both P＜0.001).After the age of 40,the mortality rate of pancreatic cancer increased significantly with the growth of age in both sexes.The PYLL was 31 347 person-years,showing an upward trend(AAPC=1.83％,P＜0.001),and the AYLL was 3.59 years,showing a downward trend(AAPC=-2.45％,P＜0.001).The age effect showed that the mortality risk of pan-creatic cancer was increased with age;the period effect showed that the mortality risk decreased from 2002 to 2016 and then increased;the cohort effect showed that the mortality risk increased with the advancement of the birth cohort.[Conclusion]From 2002 to 2022,the crude mortality rate of pancreatic cancer in Pudong New Area showed an upward trend,and the mortality rate of males was higher than that of females.The mortality risk of pancreatic cancer increases with age,and the later the birth year of the residents,the higher the mortality risk.Early screening should be strengthened for men and the elderly,environmental and lifestyle risk factors should be paid attention to in combination with the characteristics of cohort effect,and the prevention and control strategy for the whole population should be optimized.

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

OBJECTIVE: This study investigated the antidepression mechanisms of Xiaoyaosan (XYS), a classic Chinese prescription, from the perspective of energy metabolism in the brain and intestinal tissues. METHODS: Chronic unpredictable mild stress model-a classic depression rat model-was established. Effects of XYS on behaviors and gastrointestinal motility of depressed rats were investigated. Effects of XYS on energetic charge (EC), adenosine triphosphate-related enzymes, and key enzymes of energy metabolism in both hippocampus and jejunum tissues of depressed rats were investigated using high-performance liquid chromatography, biochemical analysis, and real-time quantitative polymerase chain reaction, respectively. Spearman correlation analysis was conducted to construct a correlation network of "behavior-brain energy metabolism-intestinal energy metabolism" of depression. RESULTS: XYS significantly reduced the abnormal behaviors that observed in depressed rats and increased the EC and the activity of Na⁺-K⁺-adenosine triphosphatase (ATPase) and Ca²⁺-Mg²⁺-ATPase in hippocampus and jejunum tissues of depressed rats. XYS restored the key energetic pathways that had been interrupted by depression, including glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation. Furthermore, XYS exhibited antidepressive effects in terms of regulating energy metabolism in tissues of both brain and intestine. CONCLUSION XYS significantly corrected the disturbances in EC and energy metabolism-related enzymes of both brain and intestinal tissues, alleviating both core and concomitant symptoms of depression. The current findings underscore the role of energy metabolism in the antidepressive activity of XYS, providing a fresh perspective on depression, and novel research strategies for revealing the mechanism of actions of traditional Chinese medicines on multi-site and multi-symptom diseases. Please cite this article as: Xiao MT, Wang SY, Wu XL, Zhao ZY, Wang HM, Liu HM, Qin XM, Liu XJ. Antidepressant mechanism of Xiaoyaosan: A perspective from energy metabolism of the brain and intestine. J Integr Med. 2025; 23(6):706-720.
Animals ; Energy Metabolism/drug effects* ; Antidepressive Agents/therapeutic use* ; Drugs, Chinese Herbal/therapeutic use* ; Brain/drug effects* ; Male ; Depression/metabolism* ; Rats ; Rats, Sprague-Dawley ; Intestines/drug effects* ; Hippocampus/drug effects*

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.