Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To analyze the relationship between parental psychological control and Internet Gaming Disorder (IGD) among junior high school students, so as to provide evidence for preventing IGD development in adolescents. Methods: From August 2019 to February 2020, a survey was conducted among 1 169 junior high school students from three middle schools in Xian using stratified cluster sampling. The Parental Psychological Control Scale and IGD Scale were administered to assess parental psychological control and IGD prevalence. Univariate and binary Logistic regression analyses were used to explore IGD risk factors and their correlation with parental psychological control. Results: The detection rate of IGD in middle school students was 19.9%(184/1 169). Multivariate Logistic regression revealed that compared to those with lower parental psychological control scores(≤21 points), students with higher parental psychological control scores (>21 points) had a higher risk of IGD (OR=1.82, 95%CI=1.21-2.74), a 1.58fold higher risk of selfperceived gaming addiction (95%CI=1.07-2.30), as well as reduced likelihood of seeking external help to reduce gaming time (OR=0.66, 95%CI=0.47-0.94) (P<0.05). Conclusions Parental psychological control may elevate the risks of IGD and selfperceived addiction while diminishing proactive helpseeking behaviors to reduce gaming time. Parents should enhance communication with adolescents and provide positive guidance to mitigate potential gamingrelated harms.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

ObjectiveTo explore the mechanism of Huanglian Wendantang on damp-heat type diabetes enteropathy rats based on the G protein coupled bile acid receptor 5/glucagon like peptide-1 （TGR5/GLP-1） signaling pathway and intestinal flora. MethodsA total of 72 male Sprague Dawley （SD） rats were adaptively fed for one week. Twelve SD rats were randomly selected as a blank group and fed with an ordinary diet. The rest of the SD rats were fasted for 12 hours without water. A rat model with damp-heat type diabetes enteropathy was made by left intraperitoneal injection of streptozotocin （55 mg·kg^-1） and high sugar and high fat diet （20% sucrose solution + high fat diet） in a humid and hot environment （artificial climate box： temperature 30-34 ℃， relative humidity： 85%-95%）. After successful modeling， the rats were randomly divided into a model group， a metformin group （200 mg·kg^-1）， low-dose， medium-dose， and high-dose Huanglian Wendantang groups （7.10， 14.20， 28.39 g·kg^-1）， with 12 rats in each group. The normal group and the model group were orally administered with physiological saline once a day for 6 consecutive weeks. During the observation period， the weight and blood glucose levels of rats were measured and recorded weekly. After the administration， fresh feces were collected from rats， and 16S rRNA sequencing technology was used to study the differences and changes in intestinal flora among different groups. The levels of tumor necrosis factor-α （TNF-α） and interleukin-6 （IL-6） in the serum of rats were detected by enzyme-linked immunosorbent assay （ELISA）， and the pathological morphological changes of colon tissue were examined. The expression of TGR5 and GLP-1 in colon tissue was detected by immunohistochemistry， and the expression of TGR5 and GLP-1 proteins in colon tissue was measured by Western blot. ResultsCompared with the blank group， the model group showed a decrease in body weight， an increase in blood glucose， and significant damp-heat symptoms. The levels of IL-6 and TNF-α in serum were significantly increased （P<0.01）. The expression of TGR5 and GLP-1 was decreased （P<0.01）， and the pathogenic bacteria were increased. Compared with the model group， the treatment groups exhibited improvements in body weight， blood glucose levels， and damp-heat syndrome in rats. Among them， the high-dose group of Huanglian Wendantang displayed the most significant improvement effect， with significantly reduced inflammation levels （P<0.01） and elevated expression of TGR5 and GLP-1 （P<0.01）. Colonic pathological sections showed that Huanglian Wendantang could effectively ameliorate colonic pathological changes. The 16S rRNA sequencing result indicated a significant increase in beneficial bacteria in the treatment groups. ConclusionHuanglian Wendantang can effectively ameliorate the damp-heat symptoms and blood glucose levels in rats with damp-heat type diabetes enteropathy， and it may exert an effect by regulating the TGR5/GLP-1 signaling pathway and intestinal flora disorder.

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.

One patient with gallbladder mass had transient jaundice and was diagnosed with gallbladder carcinoma by abdominal ultrasonography， contrast-enhanced CT， MRCP， and PET-CT. Surgical exploration showed enlarged gallbladder and a mass in the neck of the gallbladder pressing against the hilum of the liver， with no manifestation of tumor invasion， and there were no signs of liver metastasis. Only cholecystectomy was performed for the patient. The pathological diagnosis was tubular adenoma of the gallbladder without carcinogenesis. This case is characterized by a large gallbladder tumor， without marginal infiltration on imaging or malignant transformation based on pathology.

BACKGROUND:Compared with traditional two-dimensional culture,three-dimensional microtissue culture can show greater advantages.However,more favorable cultivation methods in three-dimensional culture still need to be further explored. OBJECTIVE:To evaluate the cell behavior of microtissue and its ability to promote cartilage formation under two three-dimensional culture methods. METHODS:Cartilage-derived microcarriers were prepared by chemical decellularization and tissue crushing.DNA quantification and nuclear staining were used to verify the success of decellularization,and histological staining was used to observe the matrix retention before and after decellularization.The microcarriers were characterized by scanning electron microscopy and CCK-8 assay.Cartilage-derived microtissues were constructed by combining cartilage-derived microcarriers with human adipose mesenchymal stem cells through three-dimensional static culture and three-dimensional dynamic culture methods.The cell viability and chondrogenic ability of the two groups of microtissues were detected by scanning electron microscopy,live and dead staining,and RT-qPCR. RESULTS AND CONCLUSION:(1)Cartilage-derived microcarriers were successfully prepared.Compared with before decellularization,the DNA content significantly decreased after decellularization(P<0.001).Scanning electron microscope observation showed that the surface of the microcarrier was surrounded by collagen,maintaining the characteristics of the natural extracellular matrix of cartilage cells.CCK-8 assay indicated that microcarriers had no cytotoxicity and could promote cell proliferation.(2)Scanning electron microscopy and live and dead staining results showed that compared with the three-dimensional static group,the three-dimensional dynamic group had a more extended morphology of microtissue cells,and extensive connections between cells and cells,between cells and matrix,and between matrix.(3)The results of RT-qPCR showed that the expressions of SOX9,proteoglycan,and type Ⅱ collagen in microtissues of both groups were increased at 7 or 14 days.The relative expression levels of each gene in the three-dimensional dynamic group were significantly higher than those in the three-dimensional static group at 14 days(P<0.05).At 21 days,the three-dimensional static group had significantly higher gene expression compared with the three-diomensional dynamic group(P<0.001).(4)The results showed that compared with three-dimensional static culture microtissue,three-dimensional dynamic culture microtissue could achieve higher expression of chondrogen-related genes in a shorter time,showing better cell viability and chondrogenic ability.