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.

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.

Objective To evaluate the efficacy and safety of brexpiprazole in treating acute schizophrenia.Methods Patients with schizophrenia were randomly divided into treatment group and control group.The treatment group was given brexpiprozole 2-4 mg·d-1 orally and the control group was given aripiprazole 10-20 mg·d-1orally,both were treated for 6 weeks.Clinical efficacy of the two groups,the response rate at endpoint,the changes from baseline to endpoint of Positive and Negative Syndrome Scale(PANSS),Clinical Global Impression-Improvement(CGI-S),Personal and Social Performance scale(PSP),PANSS Positive syndrome subscale,PANSS negative syndrome subscale were compared.The incidence of treatment-related adverse events in two groups were compared.Results There were 184 patients in treatment group and 186 patients in control group.After treatment,the response rates of treatment group and control group were 79.50％(140 cases/184 cases)and 82.40％(150 cases/186 cases),the scores of CGI-I of treatment group and control group were(2.00±1.20)and(1.90±1.01),with no significant difference(all P＞0.05).From baseline to Week 6,the mean change of PANSS total score wese(-30.70±16.96)points in treatment group and(-32.20±17.00)points in control group,with no significant difference(P＞0.05).The changes of CGI-S scores in treatment group and control group were(-2.00±1.27)and(-1.90±1.22)points,PSP scores were(18.80±14.77)and(19.20±14.55)points,PANSS positive syndrome scores were(-10.30±5.93)and(-10.80±5.81)points,PANSS negative syndrome scores were(-6.80±5.98)and(-7.30±5.15)points,with no significant difference(P＞0.05).There was no significant difference in the incidence of treatment-related adverse events between the two group(69.00％vs.64.50％,P＞0.05).Conclusion The non-inferiority of Brexpiprazole to aripiprazole was established,with comparable efficacy and acceptability.

Objective:To investigate the prognosis of patients with initially resectable gastric cancer liver metastasis (GCLM) who were treated by different modalities, and analyze the influencing factors for prognosis of patients.Methods:The retrospective cohort study was conducted. The clinicopathological data of 327 patients with initially resectable GCLM who were included in the database of a nationwide multicenter retrospective cohort study on GCLM based on real-world data from January 2010 to December 2019 were collected. There were 267 males and 60 females, aged 61(54,68)years. According to the specific situations of patients, treatment modalities included radical surgery combined with systemic treatment, palliative surgery combined with systemic treatment, and systemic treatment alone. Observation indicators: (1) clinical characteristics of patients who were treated by different modalities; (2) prognostic outcomes of patients who were treated by different modalities; (3) analysis of influencing factors for prognosis of patients with initially resectable GCLM; (4) screening of potential beneficiaries in patients who were treated by radical surgery plus systemic treatment and patients who were treated by palliative surgery plus systemic treatment. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the rank sum test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate survival rate and draw survival curve, and Log-Rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the COX proportional hazard regression model. The propensity score matching was employed by the 1:1 nearest neighbor matching method with a caliper value of 0.1. The forest plots were utilized to evaluate potential benefits of diverse surgical combined with systemic treatments within the population. Results:(1) Clinical characteristics of patients who were treated by different modalities. Of 327 patients, there were 118 cases undergoing radical surgery plus systemic treatment, 164 cases undergoing palliative surgery plus systemic treatment, and 45 cases undergoing systemic treatment alone. There were significant differences in smoking, drinking, site of primary gastric tumor, diameter of primary gastric tumor, site of liver metastasis, and metastatic interval among the three groups of patients ( P<0.05). (2) Prognostic outcomes of patients who were treated by different modalities. The median overall survival time of the 327 pati-ents was 19.9 months (95% confidence interval as 14.9-24.9 months), with 1-, 3-year overall survival rate of 61.3%, 32.7%, respectively. The 1-year overall survival rates of patients undergoing radical surgery plus systemic treatment, palliative surgery plus systemic treatment and systemic treatment alone were 68.3%, 63.1%, 30.6%, and the 3-year overall survival rates were 41.1%, 29.9%, 11.9%, showing a significant difference in overall survival rate among the three groups of patients ( χ2=19.46, P<0.05). Results of further analysis showed that there was a significant difference in overall survival rate between patients undergoing radical surgery plus systemic treatment and patients undergoing systemic treatment alone ( hazard ratio=0.40, 95% confidence interval as 0.26-0.61, P<0.05), between patients undergoing palliative surgery plus systemic treatment and patients under-going systemic treatment alone ( hazard ratio=0.47, 95% confidence interval as 0.32-0.71, P<0.05). (3) Analysis of influencing factors for prognosis of patients with initially resectable GCLM. Results of multivariate analysis showed that the larger primary gastric tumor, poorly differentiated tumor, larger liver metastasis, multiple hepatic metastases were independent risk factors for prognosis of patients with initially resectable GCLM ( hazard ratio=1.20, 1.70, 1.20, 2.06, 95% confidence interval as 1.14-1.27, 1.25-2.31, 1.04-1.42, 1.45-2.92, P<0.05) and immunotherapy or targeted therapy, the treatment modality of radical or palliative surgery plus systemic therapy were independent protective factors for prognosis of patients with initially resectable GCLM ( hazard ratio=0.60, 0.39, 0.46, 95% confidence interval as 0.42-0.87, 0.25-0.60, 0.30-0.70, P<0.05). (4) Screening of potentinal beneficiaries in patients who were treated by radical surgery plus systemic treatment and patients who were treated by palliative surgery plus systemic treatment. Results of forest plots analysis showed that for patients with high-moderate differentiated GCLM and patients with liver metastasis located in the left liver, the overall survival rate of patients undergoing radical surgery plus systemic treatment was better than patients undergoing palliative surgery plus systemic treatment ( hazard ratio=0.21, 0.42, 95% confidence interval as 0.09-0.48, 0.23-0.78, P<0.05). Conclusions:Compared to systemic therapy alone, both radical and palliative surgery plus systemic therapy can improve the pro-gnosis of patients with initially resectable GCLM. The larger primary gastric tumor, poorly differen-tiated tumor, larger liver metastasis, multiple hepatic metastases are independent risk factors for prognosis of patients with initial resectable GCLM and immunotherapy or targeted therapy, the treatment modality of radical or palliative surgery plus systemic therapy are independent protective factors for prognosis of patients with initially resectable GCLM.

The current status of exoskeletons was introduced in enhancing individual soldier's battlefield rescue capabilities,promoting the integrated use of battlefield rescue equipment,protecting medical personnel on the battlefield and assisting injured soldiers in rehabilitation training.The challenges of exoskeletons faced in human-machine interaction,power supply endurance,heavy overall structure,restricted movement and high cost were analyzed when applied to medical service support,and some suggestions were proposed accordingly including enhancing technology research and development,integrated application,communication and cooperation and personnel training.References were provided for the application of exoskeletons in China's medical service support.[Chinese Medical Equipment Journal,2024,45(3):71-75]

Objective To study the ability of radial basis function neural network(RBFNN)with different implementations for electrical impedance tomography(EIT)under real brain shapes,to evaluate the advantages and disadvantages of different approaches,and to provide a reference for the selection of practical imaging methods.Methods COMSOL Multiphysics was used to establish a multilayer 2D model with real structure based on brain CT and an EIT simulation dataset.The effects of the exact RBFNN,the orthogonal least squares-based RBFNN(OLS RBFNN)and the K-Means-based BRFNN(K-Means RBFNN)on the image reconstruction result were explored with the dataset constructed.The root mean square error(RMSE)and image correlation coefficient(ICC)were adopted to evaluate the imaging results.Results EIT could be completed with all the three RBFNNs without noise,and the exact RBFNN had the best results with average ICC and RMSE of 0.784 and 0.467,respectively,in the test set.The OLS RBFNN had the best imaging results at a hidden node of 50,with an average ICC and RMSE of 0.788 and 0.462,respectively.The K-Means RBFNN achieved the best imaging results at noise levels of 30,40,50,60,70 and 80 dB with stable ICC and RMSE and high robustness.Conclusion All the three RBFNNs can be used for brain EIT image reconstruction with their own advantages and disadvantages,and the RBFNN has to be selected for EIT reconstruc-tion based on considerations on actual conditions.[Chinese Medical Equipment Journal,2024,45(10):1-6]