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.

Objective To quantitatively assess the association of short-term exposure to polycyclic aromatic hydrocarbons (PAHs) in ambient fine particulate matter (PM2.5) with residents mortality. Methods A time-stratified case-crossover study was conducted from 2020 to 2022 among 10606 non-accidental residents by using the Guangzhou Cause of Death Surveillance System in Conghua District, Guangzhou. Exposure levels of PAHs in PM_2.5 and meteorological data during the study period were obtained from the Center for Disease Control and Prevention in Conghua District and the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), respectively. Conditional Poisson regression model was used to estimate the exposure-response association between PAHs and the mortality risk. Results Fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene were significantly associated with an increased risk of mortality. For every one interquartile range increase in exposure levels, the non-accidental mortality risks increased by 8.33% (95% CI: 1.80%, 15.27%), 4.67% (95% CI: 1.86%, 7.57%), 6.07% (95% CI: 2.08%, 10.21%), 4.62% (95% CI: 1.85%, 7.47%), and 4.70% (95% CI: 0.53%, 9.03%), respectively. The estimated non accidental deaths attributable to exposure to fluoranthene, chrysene, benzo[k]fluorine, benzo[a]pyrene and indine[1,2,3-cd]pyrene were 5.91%, 6.08%, 6.51%, 6.46%, and 4.21%, respectively. Conclusions Short-term exposure to PAHs in PM_2.5, including fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene and indine[1,2,3-cd]pyrene, was significantly associated with an increased risk of mortality among residents.

OBJECTIVE To develop a population pharmacokinetic （PPK） model for mycophenolate mofetil active metabolite mycophenolic acid （MPA） in children with primary IgA nephropathy， explore the factors affecting the pharmacokinetic parameters of MPA， and provide a basis for clinical individualized therapy. METHODS Retrospective collection was conducted on 636 concentrations and clinical data from 47 pediatric patients with primary IgA nephropathy. PPK analysis was carried out by using the nonlinear mixed-effects model； the covariates were tested with a stepwise method. Goodness-of-fit plots， Bootstrap and visual predictive check were employed to evaluate the final model. RESULTS The pharmacokinetics of MPA in children with IgA nephropathy in vivo conformed to the first-order absorption and elimination two-compartment model （objective function value of 3 276.31）. Covariate analysis suggested that body weight and albumin （ALB） levels were significant influencing factors on apparent clearance rate and apparent distribution volume. The typical values of PPK parameters of MPA in the final model were as follows： the central room had a distributed volume of 5.79 L， the clearance rate was 4.06 L/h， the volume of peripheral ventricular distribution was 430.93 L， the clearance rate between compartments was 15.40 L/h， the oral absorption rate constant was 1.29 h－1. After verification， most of the predicted corrected observed concentration points were within the 90% confidence interval of the predicted corrected simulated concentration， indicating that the MPA final model had good predictive performance. CONCLUSIONS The PPK model of MPA in children with primary IgA nephropathy is established in this study， identifying body weight and ALB levels are significant factors affecting MPA metabolism.

Over the years of exploration and development, the surgical techniques and prognosis of liver transplantation in China have been significantly improved, resulting in a notable decrease in the prevalence of postoperative complications. However, ischemic-type biliary lesion remain a non-negligible issue. The Third Affiliated Hospital of Sun Yat-sen University formulated and published the "Expert Consensus on the Diagnosis and Treatment of Ischemic-Type Biliary Lesions after Liver Transplantation in Mainland China" in 2015, which has now been updated into a guideline based on current conditions and literature reports. This guideline elaborates in detail on the definition, incidence, pathogenesis, diagnosis, prevention of high-risk factors, and treatment of ischemic-type biliary lesion, aiming to provide standardized and normative guidance for the diagnosis and treatment of ischemic-type biliary lesion after liver transplantation, thereby reducing the rate of re-transplantation and fatality, and to improve the overall quality of life of liver transplant recipients.

In environmental epidemiological research, extensive non-random environmental exposures and complex confounding biases pose significant challenges when attempting causal inference. In recent years, the introduction of causal inference methods into observational studies has provided a broader range of statistical tools for causal inference research in environmental epidemiology. The instrumental variable (IV) approach, as a causal inference technique for effectively controlling unmeasured confounding factors, has gradually found application in the field of environmental epidemiological research. This article reviewed the basic principles of IV and summarized the current research progress and limitations of applying IV for causal inference in environmental epidemiology. IV application in the field of environmental epidemiology is still in the initial stage. Rational use of IV and effective integration with other causal inference methods will become the focus of the development of causal inference in environmental epidemiology. The aim of this paper is to provide a methodological reference and basis for future studies involving causal inference to target population health effects of environmental exposures in China.

ObjectiveTo construct and validate a clinical prediction model for diabetic kidney disease （DKD） based on optical coherence tomography angiography （OCTA）. MethodsThis study enrolled 567 diabetes patients. The random forest algorithm as well as logistic regression analysis were applied to construct the prediction model. The model discrimination and clinical usefulness were evaluated by receiver operating characteristic curve （ROC） and decision curve analysis （DCA）， respectively. ResultsThe clinical prediction model for DKD based on OCTA was constructed with area under the curve （AUC） of 0.878 and Brier score of 0.11. ConclusionsThrough multidimensional verification， the clinical prediction nomogram model based on OCTA allowed for early warning and advanced intervention of DKD.

Bioactive compounds derived from herbal medicinal plants modulate various therapeutic targets and signaling pathways associated with cardiovascular diseases (CVDs), the world's primary cause of death. Ginkgo biloba, a well-known traditional Chinese medicine with notable cardiovascular actions, has been used as a cardio- and cerebrovascular therapeutic drug and nutraceutical in Asian countries for centuries. Preclinical studies have shown that ginkgolide B, a bioactive component in Ginkgo biloba, can ameliorate atherosclerosis in cultured vascular cells and disease models. Of clinical relevance, several clinical trials are ongoing or being completed to examine the efficacy and safety of ginkgolide B-related drug preparations in the prevention of cerebrovascular diseases, such as ischemia stroke. Here, we present a comprehensive review of the pharmacological activities, pharmacokinetic characteristics, and mechanisms of action of ginkgolide B in atherosclerosis prevention and therapy. We highlight new molecular targets of ginkgolide B, including nicotinamide adenine dinucleotide phosphate oxidases (NADPH oxidase), lectin-like oxidized LDL receptor-1 (LOX-1), sirtuin 1 (SIRT1), platelet-activating factor (PAF), proprotein convertase subtilisin/kexin type 9 (PCSK9) and others. Finally, we provide an overview and discussion of the therapeutic potential of ginkgolide B and highlight the future perspective of developing ginkgolide B as an effective therapeutic agent for treating atherosclerosis.

OBJECTIVE To evaluate the economics of trastuzumab deruxtecan versus the physician-selected chemotherapy （TPC） regimen in the second-line treatment of advanced breast cancer with epidermal growth factor receptor 2 （HER-2） low expression from the perspective of the Chinese healthcare system. METHODS Based on the data of DESTINY-Breast04 clinical trial， the dynamic Markov model was constructed. The time frame of the model simulation was 10 years， and the cycle was 3 weeks. Taking cost， quality-adjusted life year （QALY） and incremental cost-effectiveness ratio （ICER） as the model output indicators， the discount rate of 5% was applied， and 3 times China’s per capita gross domestic product （GDP） in 2023 was taken as the willingness-to-pay （WTP） threshold value. Cost-utility analysis was used to evaluate the economics of the two treatment regiments in the hormone receptor-positive cohort and all patient cohorts， and uncertainty analysis was used to verify the robustness of the basic analysis result. RESULTS The results of the basic analysis showed that compared with the TPC regimen， the ICER value of trastuzumab deruxtecan regimen edu.cn were 1 045 655.76 and 906 404.99 yuan/QALY in the hormone receptor-positive cohort and all patients， respectively， both exceeding the WTP threshold （268 074 yuan/QALY）. The results of single factor sensitivity analysis showed that progression-free survival utility value， the price of trastuzumab deruxtecan and progression disease utility had a significant influence on the model results. The results of probability sensitivity analysis showed that when the WTP threshold was 3 times China’s per capita GDP in 2023， the probability of economic viability of trastuzumab deruxtecan was 0. The results of scenario analysis showed that when the patient assistance program for trastuzumab deruxtecan was considered， the probability of trastuzumab deruxtecan regimen being economical was 0. However， when the price of trastuzumab deruxtecan was reduced by 70%， the probability of its being cost-effective was significantly increased to 82.80%. CONCLUSIONS At a WTP threshold of 3 times China’s per capita GDP in 2023， the trastuzumab deruxtecan regimen is not cost-effective compared to TPC regimen for the second-line treatment of advanced breast cancer with HER-2 low expression. Reducing the price of trastuzumab deruxtecan by region can improve its cost-effectiveness.

Objective To observe the clinical efficacy of acupuncture combined with rehabilitation training under the guidance of Chinese medicine-enhanced recovery after surgery(CMERAS)in postoperative rehabilitation of tibial plateau fracture.Methods Sixty patients with tibial plateau fracture in postoperative rehabilitation period were randomly divided into observation group and control group,30 cases in each group.The control group was given routine postoperative rehabilitation training,and the observation group was treated with combined acupuncture treatment on the basis of the rehabilitation training of the control group.Both groups were treated for 3 consecutive months.After 3 months of treatment,the clinical efficacy of the two groups was evaluated,and the changes in the Visual Analogue Scale(VAS)scores of pain were observed before and after treatment,and the changes in the knee scores of the Hospital for Special Surgery knee score(HSS)of the United States were compared before and after treatment between the two groups.As well as the time of fracture healing on the operative side of the two groups of patients,and the fracture healing rate within 3 months of the two groups of patients were compared.Results(1)After 1 week,1 month and 3 months of postoperative treatment,the VAS scores of patients in the two groups were significantly improved compared with the pre-treatment period(P＜0.05),and the observation group was significantly superior to the control group in improving the VAS scores,with a statistically significant difference(P＜0.05).(2)After treatment,the HSS scores of patients in the two groups were significantly improved compared with those before treatment(P＜0.05),and the observation group was significantly superior to the control group in improving the HSS scores,and the difference was statistically significant(P＜0.05).(3)After 3 months of treatment,the fracture healing rate was 56.67%(17/30)in the observation group and 30.00%(9/30)in the control group.The fracture healing rate of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(4)After 3 months of acupuncture treatment,the total effective rate was 96.67%(29/30)in the observation group and 73.33%(22/30)in the control group.The efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).Conclusion The clinical efficacy of acupuncture in postoperative rehabilitation of tibial plateau fractures under the guidance of CMERAS is significant,which reduces the clinical symptom of postoperative pain of the patients,shortens the healing time of fracture breaks,and significantly improves the patients'knee joint function.

Objective:To determine the correlation of tumor volume and weight with biochemical parameters in patients with parathyroid adenoma (PA) .Methods:A prospective electronic database collected clinical data on 208 patients with PA treated for the first time by surgery at department of general surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College.The relationship between biochemical parameters and tumor volume and weight was analyzed with Spearman’s correlation.Results:Tumor volume and weight were positively correlated with parathyroid hormone (PTH) ( r=0.33, P<0.001; r=0.39, P<0.001), calcium ( r=0.16, P=0.018; r=0.18, P=0.007) and alkaline phosphatase levels ( r=0.24, P<0.001; r=0.27, P<0.001), respectively. Clinical correlates affecting serum PTH were age, serum calcium and tumor weight ( F=30.325, P<0.001) . Conclusions:Tumor burden in patients with PA correlates with some laboratory biochemical parameters. Age and cystic lesions of the tumor may influence the actual serum PTH levels.