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 establish the analysis methods of 33 banned pesticides in Ginkgo Biloba leaves and the extracts, and conduct the risk assessment study. METHODS One hundred and thirty-six batches of Ginkgo Biloba leaves and 58 batches of Ginkgo Biloba leaves extract were detected by UPLC-MS and GC-MS. The acute and chronic intake risk of pesticide residues in samples were calculated by point assessment method, and the risk scores of the pesticides were calculated by the Britain veterinary residues matrix ranking. RESULTS Six kinds of banned pesticides were detected in 136 batches of Ginkgo Biloba leaves, the total detection rate was 35.29%, and the detection amount was 0.002−0.210 mg·kg−1. The chronic dietary intake risk was 0.018%−0.620%, and the acute dietary intake risk was 0.000 1%−0.014 0%, indicated that the dietary exposure risk of pesticides in Ginkgo biloba leaves was at a low level. Two kinds of banned pesticides were detected in 58 batches of Ginkgo Biloba leaves extract, the detection rate was 55.17%, and the detection amount was 0.002−1.788 mg·kg−1. The percentage of acceptable daily intake was 0.003%−0.143%, and the percentage of acute reference dose was 0.002 4%, which was also at a low level. Risk ranking results indicated that the risk of phorate was the highest and should be focused on the production and safety supervision. CONCLUSION The method has good stability, high precision and promising repeatability, which can be used for the detection of 33 prohibited pesticides in Ginkgo biloba leaves and their extracts. The results show that the residual amounts of 33 banned pesticides in Ginkgo Biloba leaves and its extracts were extremely low, and there is no significant health risks.

Objective:To analyze the clinical manifestations, genetic and hematological test results of patients with sitosteronism (STSL) complicated with cardiovascular and cerebrovascular events.Methods:Clinical data were collected from 11 STSL patients at the outpatient department of Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine between November 2020 to June 2023. The whole exome sequencing technology was used to detect gene mutations associated with lipid metabolism, the serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) were tested by the enzyme endpoint method; serum phytosterol levels by high-performance liquid chromatography; serum concentration of C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor (TNF-α) by enzyme-linked immunosorbent assay, concentration of fibrinogen, the activity of protein C and coagulation factor Ⅷ (FⅧ) by the coagulation method; the antigen and activity of von Willebrand factor (vWF) by immunoturbidimetric assay; and the activity of antithrombin Ⅲ (ATⅢ) by chromogenic substrate assay.Results:There were 3 cases of coronary heart disease, 6 cases of cerebral infarction, 2 cases of coronary heart disease combined with cerebral infarction, 4 cases of eyelid melasma, and 2 cases of arthritis. Gene mutation was as follows: ABCG5 gene mutation including exon9: c G1166A: p R389H, exon9: c T1195C: p F399L, exon12: c.1762+1G>A, and ABCG8 gene mutation including exon 11 c.1720G>A: p.Gly574Arg, exon4:c.445_453del:p.A149_V151del, exon13 c.1949T>G: p.Leu650Arg. The percent of stomatocytes in the peripheral blood swears was (11.3±8.6)%. The concentrations of TC, LDL-C and sitosterol was (6.8±2.4), (4.4±2.0) mmol/L and 40.0 (22.0, 203.7) μmol/L. The level of CRP, interleukin IL-6, and TNF-α was 15.5 (7.2, 29.6)mg/L, (4.2±2.0) pg/ml and (6.7±1.5) pg/ml, respectively.The activity of PC, FⅧ and ATⅢ was (114±51)%, (110±41)% and (83±33)%. The values of FIB was (3.2±1.4)g/L.vWF antigen and vWF activity was (305±168)% and (275±112)%.Conclusions:STSL patients combined with cardiovascular and cerebrovascular events not only had complex dysfunctional lipid metabolism related gene defects, but also had significantly increased hematological indicators such as inflammatory mediator CRP, coagulation parameter vWF.

Objective:To investigate the clinical, imaging and gene variation characteristics of hereditary spastic paraplegia type 74 caused by mutations in IBA57 gene. Methods:A retrospective analysis was performed on 2 cases of autosomal recessive spastic paraplegia caused by mutations in IBA57 gene who visited the Department of Neurology, the Affiliated Wuxi Children′s Hospital of Nanjing Medical University in 2010 and 2021, and the patients′ clinical data were collected. Results:The 2 patients were siblings with onset age of 4 years and 7 months, 1 year and 3 months, respectively. The same compound heterozygous mutations in IBA57 gene were found in the sibling patients [c.473G>C (p.R158P) and c.697C>T (p.R233X)]. Both patients were diagnosed as spastic paraplegia type 74. They had mild to moderate gait abnormalities, optic atrophy, decreased vision, and leukodystrophy with periventricular white matter abnormality, but no obvious growth and mental retardation in developmental assessment. Conclusions:Cases of spastic paraplegia type 74 caused by compound heterozygous mutations in IBA57 gene mainly manifested as childhood onset and slowly progressive inferior spasmodic weakness. The patients did not display significant cognitive impairment, and imaging examinations showed obvious leukodystrophy.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

Ginkgo biloba preparation is widely used as natural extract preparation both domestically and internationally, for myocardial infarction and cerebral infarction diseases in China; and mainly used to improve memory and treat dementia symptoms abroad. In recent years, there have been a large amount of reports on the treatment of ophthalmic diseases, mental diseases, kidney diseases and so on, however some of which have not uniform literature result. Therefore, it is necessary to summarize the reports on off-label use of Ginkgo biloba preparation. Among them, Ginkgo biloba preparation has a lot of controversy on brain diseases such as dementia and autism, mainly due to the mild effect, the difficulty in tracking long-term medication data for chronic diseases, and the subjective evaluation results of cognitive level, so more high-level research is needed to provide data support. In addition, Ginkgo biloba preparation has obvious protective effects on peripheral nerves and microvascular, and as an adjuvant drug in related diseases, it can effectively accelerate the recovery process of patients. This article summarizes various reports on Ginkgo biloba preparations, which can better clarify the product positioning and provide a reference for the selection of relevant treatment options.