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.

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 factors related to early allograft dysfunction(EAD)after liver transplantation and to construct a predictive model.Methods A total of 375 patients who underwent liver transplantation in our hospital from December 2008 to December 2021 were collected,including 90 patients with EAD and 266 patients without EAD.Thirty items of baseline data for the 2 groups were compared and analyzed.Aftergrouping in a ratio of 7∶3,univariate and multivariate logistic regression analyses were used in the training set to evaluate the factors related to EAD and construct a nomogram.Receiver operating characteristic(ROC)curve,decision curve analysis(DCA),sensitivity,specificity,positive predictive value,negative predictive value,Kappa value and other indicators were used to evaluate the model performance.Results The incidence of EAD after liver transplantation was 24%.Multivariate logistic regression analysis showed that preoperative tumor recurrence history(OR=3.15,95%CI:1.28～7.77,P=0.013)and operation time(OR=1.22,95%CI:1.04～1.42,P=0.015)were related to the occurrence of EAD after surgery.After predicting the outcome according to the cut-off point of 0.519 identified by the Youden index,the model performance in the both training set and validation set was acceptable.DCA suggested the model has good clinical applicability.Conclusion The risk factors for EAD after liver transplantation are preoperative tumor recurrence history and operation time,and the established model has predictive effect on prognosis.

Objective:To explore the clinical application pathway of the CT generative adversarial networks (CTGANs) algorithm in mandibular reconstruction surgery, aiming to provide a valuable reference for this procedure.Methods:A clinical exploratory study was conducted, 27 patients who visited the Department of Oral and Maxillofacial Surgery, Xiangya Hospital of Central South University between January 2022 and January 2024 and required mandibular reconstruction were selected. The cohort included 16 males and 11 females, with the age of (46.6±11.5) years; among them, 7 cases involved mandibular defects crossing the midline. The CTGANs generator produced 100 images, and the mean squared error (MSE) was calculated for differences between any two generated images. Preoperative cone-beam CT data from 5 patients were used to construct a labeled test database, divided into groups: normal maxilla, normal mandible, diseased mandible, and noise (each group containing 70 cross-sectional images). The CTGANs discriminator was used to evaluate the loss values for each group, and one-way ANOVA and intergroup comparisons were performed. Using the self-developed KuYe multioutcome-option-network generation system (KMG) software, the three-dimensional (3D) completion area of the mandible under cone-beam CT was defined for the 27 patients. The CTGANs algorithm was applied to obtain a reference model for the mandible. Virtual surgery was then performed, utilizing the fibular segment to reconstruct the mandible and design the surgical expectation model. The second-generation combined bone-cutting and prebent reconstruction plate positioning method was used to design and 3D print surgical guides, which were subsequently applied in mandibular reconstruction surgery for the 27 patients. Postoperative cone-beam CT was used to compare the morphology of the reconstructed mandible with the surgical expectation model and the mandibular reference model to assess the three-dimensional deviation.Results:The MSE for the CTGANs generator was 2 411.9±833.6 (95% CI: 2 388.7-2 435.1). No significant difference in loss values was found between the normal mandible and diseased mandible groups ( P>0.05), while both groups demonstrated significantly lower loss values than the maxilla and noise groups ( P<0.001). All 27 patients successfully obtained mandibular reference models and surgical expectation models. In total, 14 162 negative deviation points and 15 346 positive deviation points were observed when comparing the reconstructed mandible morphology with the surgical expectation model, with mean deviations of -1.32 mm (95% CI:-1.33- -1.31 mm) and 1.90 mm (95% CI: 1.04-1.06 mm), respectively. Conclusions:The CTGANs algorithm is capable of generating diverse mandibular reference models that reflect the natural anatomical characteristics of the mandible and closely match individual patient morphology, thereby facilitating the design of surgical expectation models. This method shows promise for application in patients with mandibular defects crossing the midline.

Objective Studies on the relationship between iodine,vitamin A(VA),and vitamin D(VD)and thyroid function are limited.This study aimed to analyze iodine and thyroid-stimulating hormone(TSH)status and their possible relationships with VA,VD,and other factors in postpartum women. Methods A total of 1,311 mothers(896 lactating and 415 non-lactating)from Hebei,Zhejiang,and Guangxi provinces were included in this study.The urinary iodine concentration(UIC),TSH,VA,and VD were measured. Results The median UIC of total and lactating participants were 142.00 μg/L and 139.95 μg/L,respectively.The median TSH,VA,and VD levels in all the participants were 1.89 mIU/L,0.44 μg/mL,and 24.04 ng/mL,respectively.No differences in the UIC were found between lactating and non-lactating mothers.UIC and TSH levels were significantly different among the three provinces.The rural UIC was higher than the urban UIC.Obese mothers had a higher UIC and a higher prevalence of excessive TSH.Higher UICs and TSHs levels were observed in both the VD deficiency and insufficiency groups than in the VD-sufficient group.After adjustment,no linear correlation was observed between UIC and VA/VD.No interaction was found between vitamins A/D and UIC on TSH levels. Conclusion The mothers in the present study had no iodine deficiency.Region,area type,BMI,and VD may be related to the iodine status or TSH levels.

Tooth absorption can be divided into physiological absorption and pathological absorption.Root absorp-tion of mature deciduous teeth is physiological absorption.Pathological absorption includes internal absorption and ex-ternal absorption.Internal absorption,also known as intramedullary absorption,includes inflammatory absorption and al-ternative absorption.External tooth absorption originates from the outer surface of the root or the neck of the tooth and can be divided into inflammatory absorption,alternative absorption,pressure resorption and invasive cervical resorption.Invasive cervical resorption(ICR)is pathological damage caused by many factors,which usually begins in the cemento-enamel junction and extends peripherally or horizontally in the dentin.It hardly invades the pulp.Orthodontic devices,trauma,bleaching,systemic diseases,and the use of certain medications can all lead to invasive cervical resorption.The clinical manifestations of ICR are usually asymptomatic or not obvious,and most of which are found in imaging examina-tions.Because caries and internal absorption are often misdiagnosed through plain apical radiography,cone beam com-puted tomography(CBCT)can help to better understand the situation of invasive cervical resorption.Because the patho-genesis and etiology of invasive cervical resorption are not fully understood,clinical negligence and inadequate treat-ment of invasive cervical resorption can even cause unnecessary tooth loss.This article reviews the latest research prog-ress on the histopathologic features,pathogenic mechanism,susceptibility factors,diagnosis and treatment of ICR,with special emphasis on susceptibility factors and their mechanisms.

ObjectiveTo classify subtypes among middle-aged and elderly type 2 diabetes mellitus (T2DM) patients aged between 35‒74 years in Shanghai suburbs, to compare their characteristics and analyze incidence risk for cerebrovascular disease among these subtypes, so as to promote personalized and precise treatment of T2DM. MethodsA total of 7 792 patients with T2DM who completed a baseline survey from 2016 and 2019 were selected as the research subjects, based on the data from a natural population cohort and biobank in Shanghai suburbs. Patients were stratified by gender and clustered into subtypes using k-means method based on baseline parameters including the age at T2DM diagnosis, body mass index (BMI), fasting blood glucose, and triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C). Patients were followed up until March 31, 2023. Multivariate Cox regression models were used to analyze the association between subtypes and incidence risk for cerebrovascular disease, and those with cerebrovascular disease within 1 year of follow-up survey were excluded from sensitivity analysis. ResultsAmong the 7 792 patients with T2DM, 3 615 were males and 4 177 were females. Stratified by gender, 4 subgroups were identified through k-means clustering analysis, namely poor blood glucose control subgroup, severe insulin-resistant subgroup, younger onset subgroup, and older onset subgroup. The median follow-up time was 4.30 years, during which 1 960 cerebrovascular disease events were observed (844 in males, 1 116 in females). After adjusting for smoking, alcohol consumption, weekly exercise, family history of diabetes mellitus, and duration of diabetes mellitus, among male patients, the incidence risk for cerebrovascular disease was lower in the younger onset subgroup (HR=0.59, 95%CI: 0.48‒0.73, P<0.001), poor blood glucose control subgroup (HR=0.81, 95%CI: 0.65‒1.00, P=0.046), and severe insulin-resistant subgroup (HR=0.61, 95%CI: 0.50‒0.75, P<0.001), compared to the older onset subgroup. While among female patients, the incidence risk for cerebrovascular disease was also lower in the younger onset subgroup (HR=0.68, 95%CI: 0.57‒0.80, P<0.001), poor blood glucose control subgroup (HR=0.73, 95%CI: 0.60‒0.89, P=0.002), and severe insulin-resistant subgroup (HR=0.72, 95%CI: 0.61‒0.85, P<0.001), compared to the older onset subgroup. Results of the sensitivity analysis were consistent with the main findings. ConclusionAmong middle-aged and elderly T2DM patients in suburban Shanghai, both male and female patients have the highest incidence risk for cerebrovascular disease in the older onset subgroup. Subtyping of T2DM patients can help to identify the high-risk populations of cerebrovascular disease.