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 compare the desalination effects of five desalination methods and their effects on the components for human coagulation factor Ⅷ(FⅧ), and provide reference for selection of protein desalination methods. 【Methods】 Sephadex G-25 Medium gel, Fractogel EMD BioSEC gel, ultrafiltration, room temperature dialysis and 4℃ dialysis were used to desalt human FⅧ. The desalination effect was evaluated by the removal rate of Na +, citrate ion and glycine. FⅧ protein recovery, FⅧ activity (FⅧ∶C), VWF antigen (VWF∶Ag), VWF activity(VWF∶Ac), VWF polymers and SDS-PAGE analysis before and after desalination were compared to evaluate the effect of desalination on FⅧ components. 【Results】 In terms of desalination effect, the removal rate of Na⁺ was the lowest in ultrafiltration desalination, while that of Fractogel EMD BioSEC gel was the highest [(97.90±0.06) % vs (99.82±0.07) %]. Except that there was no statistical significance between Sephadex G-25 Medium gel desalination and Fractogel EMD BioSEC gel desalination (P=0.90), the removal rates of the other four methods were statistically significant. The removal rate of glycine was the lowest in ultrafiltration desalination, wihle that of Fractogel EMD BioSEC gel desalination was the highest [(95.78±0.42) % vs (99.81±0.08) %]. Significant difference in glycine removal was noticed in ultrafiltration desalination, but not among the other four desalination methods. There was no significant difference in the removal rate of citrate ions among the five methods (P=0.85). For the effect of FⅧ components, FⅧ∶C, VWF∶Ag, VWF∶Ac and protein recovery rates of ultrafiltration desalination were the highest, with (18.34±1.99) IU/mL, (11.81±0.33) IU/mL, (12.26±0.58) IU/mL and (97.13±1.37) %, respectively. There was no significant change in VWF∶Ac/VWF∶Ag before and after desalination by the five methods. SDS-PAGE and VWF polymer analysis showed that different desalination methods had no significant impact on protein composition. 【Conclusion】 Although different desalination methods had no significant effect on the composition of FⅧ protein, the desalination effect was different. Moreover, different desalination methods had significant effects on protein recovery, FⅧ∶C, VWF∶Ag and VWF∶Ac. The selection of desalination methods should be more considered during protein processing,

Objective:To analyze the clinical features of postpartum hepatitis flares in pregnant women with hepatitis B virus (HBV) infection.Methods:A retrospective study was conducted. Patients who met the enrollment criteria were included. Liver function and HBV virology tests were collected from pregnant women with chronic HBV infection at delivery, 6, 24, 36, and 48 weeks after delivery through the hospital information and test system. Additionally, antiviral therapy types and drug withdrawal times were collected. Statistical analysis was performed on all the resulting data.Results:A total of 533 pregnant women who met the inclusion criteria were included, with all patients aged (29.5±3.7) years old. A total of 408 cases received antiviral drugs during pregnancy to interrupt mother-to-child transmission. There was no significant difference in the levels of alanine aminotransferase (ALT, z ?=?-1.981, P ?=?0.048), aspartate aminotransferase (AST, z ?=?-3.956, P ?

Objective:To explore the research hotspots and development trends of mobile health technology applications among family caregivers using bibliometric methods, providing a reference for related studies.Methods:The Web of Science Core Collection was selected as the data source, and research on mobile health technology in family caregivers published from January 1, 2005, to August 31, 2023, was retrieved. Excel, Scimago Graphica, and CiteSpace software were used to create network maps of countries, institutions, authors, and keywords, exploring research hotspots and development trends in this field.Results:A total of 475 relevant articles were included, showing an overall upward trend in annual publication volume. The United States had the highest publication volume (173 articles), with the most influential author and the institution with the highest publication volume being Sin and the University of Washington, respectively. Current research hotspots included chronic diseases, mental health, and social support, while future research trends focus on in-depth studies, artificial intelligence, and enhancing network information security.Conclusions:Research on the application of mobile health technology among family caregivers is on the rise, mainly focusing on assisting caregivers in managing chronic diseases, improving their mental health, and enhancing their social support. Future research frontiers may include optimizing and refining digital health technologies, using artificial intelligence to predict and alleviate caregiver burden, and enhancing industry regulation and data security.

In order to understand the prevalence and genetic variation of H9N2 subtype avian influ-enza virus in Shandong,a total 492 tracheal and lung tissue samples collected from chicken farms with respiratory symptoms in partial areas in Shandong were detected by H9 subtype AIV real-time RT-PCR,and the positive samples were inoculated with chicken embryos for two generations.Whole genome sequences of the positive strains by applying Illumina Miaseq platform,and genetic evolution and mutation at positions associating with viral pathogenicity and transmissibility were analyzed.The results showed that there were 72 samples were positive for H9 subtype AIV among the 492 samples,with a positive rate of 14.63％.Thirty-four strains of H9 subtype AIV were ob-tained from the positive samples after passing through chicken embryo,meanwhile,the 34 isolates were all H9N2 subtype AIV by whole genome sequencing analysis.By analyzing the evolutionary tree of HA and NA genes,HA and NA genes of the 34 H9N2 AIV strains belonged to Y280-like branch and F/98-like branch,respectively.Meanwhile,based on above branches,there were obvious time node subbranch,which one was"isolates before 2013",another one was"isolates after 2013".The HA cleavage sites of thirty-four H9N2 strains were all 325PSRSSR↓GLF333,which met the se-quence characteristics of the lowly pathogenic avian influenza virus,and the HA receptor binding site 226 amino acid was leucine,which had the characteristics of blinding to a-2,6 mammalian sialic acid receptors.Among the internal amino acid sites that are key to mammalian adaptation,all strains had an I368V mutation in the PB1 gene that enhanced viral transmissibility in mammals and the PB2 genes of some strains were mutated to enhance the mammalian adaptation of I292 V and A588 V.The above results illustrated that the H9N2 subtype AIV gene segments in Shandong have different degrees of recombination and gene variation,so it is necessary to strengthen the monito-ring of virus variation.

OBJECTIVES: The magnesium depletion score (MDS) is considered more reliable than traditional approaches for predicting magnesium deficiency in humans. We explored the associations of MDS and dietary magnesium intake with diabetes. METHODS: We obtained data from 18,853 participants in the National Health and Nutrition Examination Survey 2011-2018. Using multivariate regression and stratified analysis, we investigated the relationships of both MDS and magnesium intake with diabetes. To compute prevalence ratios (PRs), we employed modified Poisson or log-binomial regression. We characterized the non-linear association between magnesium intake and diabetes using restricted cubic spline analysis. RESULTS: Participants with MDS ≥2 exhibited a PR of 1.26 (95% confidence interval [CI], 1.19 to 1.34) for diabetes. Per-standard deviation (SD) increase in dietary magnesium intake was associated with a lower prevalence of diabetes (PR, 0.91; 95% CI, 0.87 to 0.96). Subgroup analyses revealed a positive association between MDS ≥2 and diabetes across all levels of dietary magnesium intake, including the lowest (PR, 1.35; 95% CI, 1.18 to 1.55), middle (PR, 1.23; 95% CI, 1.12 to 1.35), and highest tertiles (PR, 1.25; 95% CI, 1.13 to 1.37; pinteraction<0.001). Per-SD increase in magnesium intake was associated with lower diabetes prevalence in participants with MDS <2 (PR, 0.92; 95% CI, 0.87 to 0.98) and those with MDS ≥2 (PR, 0.91; 95% CI, 0.84 to 0.98; pinteraction=0.030). CONCLUSIONS MDS is associated with diabetes, particularly among individuals with low magnesium intake. Adequate dietary magnesium intake may reduce diabetes risk, especially in those with high MDS.