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.

Loss-of-function variants in CSDE1 have been strongly linked to neuropsychiatric disorders, yet the precise role of CSDE1 in neurogenesis remains elusive. In this study, we demonstrate that knockout of Csde1 during cortical development in mice results in impaired neural progenitor proliferation, leading to abnormal cortical lamination and embryonic lethality. Transcriptomic analysis revealed that Csde1 upregulates the transcription of genes involved in the cell cycle network. Applying a dual thymidine-labelling approach, we further revealed prolonged cell cycle durations of neuronal progenitors in Csde1-knockout mice, with a notable extension of the G1 phase. Intersection with CLIP-seq data demonstrated that Csde1 binds to the 3' untranslated region (UTR) of mRNA transcripts encoding cell cycle genes. Particularly, we uncovered that Csde1 directly binds to the 3' UTR of mRNA transcripts encoding Cdk6, a pivotal gene in regulating the transition from the G1 to S phases of the cell cycle, thereby maintaining its stability. Collectively, this study elucidates Csde1 as a novel regulator of Cdk6, sheds new light on its critical roles in orchestrating brain development, and underscores how mutations in Csde1 may contribute to the pathogenesis of neuropsychiatric disorders.
Animals ; Neurogenesis/genetics* ; Cell Cycle/genetics* ; Mice, Knockout ; Mice ; Neural Stem Cells/metabolism* ; DNA-Binding Proteins/metabolism* ; Cyclin-Dependent Kinase 6/genetics* ; Cell Proliferation ; 3' Untranslated Regions ; Cerebral Cortex/embryology* ; RNA-Binding Proteins ; Mice, Inbred C57BL

Objective To explore the correlation between serum glycosylated hemoglobin(HbA1c),lym-phocyte activation gene-3(LAG-3)and thyroid nodules in patients with type 2 diabetes mellitus(T2DM).Methods A total of 120 T2DM patients with thyroid nodules admitted to the Third Hospital of Hebei Medi-cal University from July 2021 to July 2022 were included as the study group,and 100 simple T2DM patients(without thyroid nodules)were included as the control group during the same period.According to the patho-logical examination results of thyroid nodules,the study group was grouped into a benign nodule group(85 ca-ses)and a malignant nodule group(35 cases).Enzyme linked immunosorbent assay was applied to detect the serum LAG-3 level of all study subjects.Fully automated glycated hemoglobin analyzer was applied to detect HbA1c level in all study subjects.Spearman method was applied to analyze the correlation between serum HbA1c,LAG-3,and thyroid imaging report and data system(TI-RADS)scores in patients with T2DM and thyroid nodules.Multivariate Logistic regression was applied to analyze the influencing factors of T2DM with thyroid nodules.Receiver operating characteristic(ROC)curve was applied to analyze the diagnostic value of HbA1c and LAG-3 levels in T2DM with thyroid nodules.Results Compared with the control group,the level of HbA1c in the study group was obviously higher(P<0.05),while the level of LAG-3 was obviously lower(P<0.05).Compared with the benign nodule group,the serum LAG-3 level in the malignant nodule group was obviously lower(P<0.05),while the level of LAG-3 was obviously lower(P<0.05).Spearman analysis showed a positive correlation between HbA1c level and TI-RADS score in T2DM patients with thyroid nod-ules(r=0.378,P<0.001),while the serum LAG-3 level was negatively correlated with TI-RADS score(r=-0.472,P<0.001).The results of multivariate Logistic regression analysis showed that HbA1c was a risk factor for the occurrence of thyroid nodules in T2DM patients(P<0.05),and LAG-3 was a protective factor for the occurrence of thyroid nodules in T2DM patients(P<0.05).The combination of HbA1c and LAG-3 in the diagnosis of T2DM with thyroid nodules was superior to their individual diagnosis(Zcombination-HbA1c=2.542,P=0.011;Zcombination-LAG-3=3.098,P=0.002).Conclusion Patients with T2DM combined with thyroid nodules have obviously lower serum LAG-3 level and higher HbA1c level,and the two are related to the malignancy of thyroid nodules.

Objective:To explore the potential categories of post-traumatic stress disorder (PTSD) trajectories in women with multiple in vitro fertilization-embryo transfer (IVF-ET) failures, and to analyze the effects of different demographic characteristics and psychological factors on the potential categories of PTSD trajectories.Methods:This was a prospective empirical research, from May 2021 to October 2022, women with IVF-ET failure ≥ 2 times in the reproductive department of Shanghai First People′s Hospital from May 2021 to October 2022 were selected as the research objects. Post-traumatic stress disorder civilian version scale was used for 4 follow-ups at 3 d (T1), 10 d (T2), 20 d (T3) after the last transplantation failure and 3 d before the next transplantation cycle (T4). Telephone follow-up and online follow-up were combined to obtain the PTSD level at 4 time points. Potential categories of PTSD score trajectories at four time points were identified using a latent category growth model, and analyze influencing factors using unordered multi classification logistic analysis.Results:Totally 196 IVF-ET women were admitted, aged (29.42 ± 4.13) years. Three PTSD trajectories were fitted in this study, including 82 cases (42%) in non-PTSD group, 61 cases (31%) in mild PTSD group and 53 cases (27%) in elevated PTSD group. Logistic regression analysis showed that age, education level, fertility pressure and marital adjustment level were the predictors of PTSD trajectory in women with multiple IVF-ET failures. Compared with the non-PTSD group, women aged ≥35 years, with lower education level and marital adjustment level were more likely to enter the elevated PTSD group ( OR=4.570, 8.540, 0.949, all P<0.05). Women aged 35 years and with greater reproductive pressure were more likely to enter the mild PTSD group ( OR=3.871, 1.063, both P<0.05). Conclusions:There is group heterogeneity in the trajectories of PTSD in women with multiple IVF-ET failures in the next transplantation cycle. Old age, low education level, high fertility pressure and poor marital adjustment can predict the trajectories of PTSD. Fertility stress and marriage adjustment are changeable variables. Medical staff can relieve women′s fertility pressure through health education and mindfulness intervention, promote a good state of marriage adjustment, and minimize the adverse effects of PTSD on the next cycle of conception.

Objective:To establish an HPLC method for benzyl benzoate related substances and propose revision suggestions for the quality standard of this excipient.Methods:Phenylsilane bond and silica gel were used as fill-ers,and methanol-1％acetic acid(60:40)was used as the mobile phase at the flow rate of 1.0 mL·min-1.The detection wavelength was 254 nm and the injection amount was 20 μL.Results:Based on the results of the de-struction test and taking into account the separation requirements,the chromatographic conditions for benzyl benzo-ate related substances were confirmed and methodological validation was conducted.The detection limits for benzoic acid and benzaldehyde in related substances were 0.2 μg·mL-1 and 0.01 μg·mL-1.The correction factors for benzoic acid and benzaldehyde were 1.0 and 0.1,respectively.According to the newly established method,six batches of benzyl benzoate samples from four enterprises were inspected for related substances,and benzaldehyde was detected with content from 0.02％to 0.1％.The total amount of impurities was from 0.02％to 0.9％.Conclusion:The newly established method can be added to the quality standards of benzyl benzoate,and different impurity limits can be formulated according to the different administration routes in order to better control drug quality.

Objective:To explore the factors affecting and improvement recommendations about the congealing temperature of benzyl benzoate as pharmaceutical excipient by the method in the Chinese pharmacopoeia.Methods:The influence of seven factors such as measuring device,sample size,melting temperature,condensation tempera-ture,stop stirring time,stirring frequency and reading of congealing temperature on the determination of congealing temperature of benzyl benzoate was investigated.Results:The key factor for determination of benzyl benzoate con-gealing temperature was the stop stirring time,which was more reasonable when the sample temperature drops to constant or starts to rise slightly was reasonable.Conclusion:The improved method has better accuracy and repeat-ability,and is suitable for the measuring the congealing temperature of pharmaceutical excipient benzyl benzoate.