Objective:To increase secretory expression of SARS-CoV-2 S-EABR protein in 293T cell line by optimizing promoter, PolyA signals, signal peptide and terminal amino acids of intracellular sequences.Methods:First, four PCDNA3.1 (-) eukaryotic vector plasmids (Mb, MS, Ab, AS) containing different combinations of elements (promoter and PolyA signals) were constructed, and the S-EABR-1 target sequence optimized according to human codons was inserted. 293T-cells were transiently transfected. After 48 hours, cell culture supernatants and cell lysates were collected, and the expression level of S protein in supernatant was detected by Western blotting and ELISA. Then, the vector with the best expression element combination was selected, and the target sequences of S-EABR-1 and S-EABR-2 (4 amino acids-HSLP were added to the tail of S-EABR-1) were inserted to compare the expression level of S protein in the supernatant. Finally, based on the combination of the above elements with the best expression effect and the insertion of the target sequence, five vector plasmids (tPA, AZ, IFNα2, HSA, GLUC) were constructed to replace the original signal peptide of SARS-CoV-2 S protein, and the expression level of S protein in the supernatant was compared. At the same time, a computer was used to simulate the molecular docking of the SRP54 subunit and the signal peptide nucleic acid sequence, and the Docking Score was used as the docking evaluation criterion to predict the binding of the two.Results:In 293T cells, the Ab combination vector secreted the highest level of S-EABR, and the yield increased by 125% compared with Mb. Based on the Ab combination vector, the level of S-EABR-2 sequence expression and secretion of S-EABR increased by about 50% compared with S-EABR-1. After further replacement with the HSA signal peptide, the level of S-EABR expression and secretion increased by about 83% compared with the original signal peptide of the S protein. In addition, computer simulation result showed that the docking score between HSA and SRP54 subunit was the highest, at 1 505.861.Conclusions:The secretory expression of codon-optimized S-EABR in 293T cells can be further improved by optimizing eukaryotic expression elements (promoter, terminator and signal peptide) and intracellular sequences. The calculated simulated docking score of the affinity between the signal peptide and the SRP54 subunit is basically consistent with the secreted expression level of S-EABR also provides a design idea and screening strategy for subsequent screening of signal peptides to improve the secreted expression of the target gene.

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.

ObjectiveTo investigate the mechanism of modified Si Junzitang and Shashen Maidong Tang ［Yiqi Yangyin Jiedu prescription （YQYYJD）］ in enhancing the sensitivity of cisplatin in epidermal growth factor receptor tyrosine kinase inhibitor （EGFR-TKI）-resistant lung adenocarcinoma cells based on aerobic glycolysis. MethodsThe effects of different concentrations of YQYYJD （0， 2， 3， 4， 5， 6， 7， 8 g·L^-1） and cisplatin （0， 3， 6， 9， 12， 15， 18， 21， 24， 27 mg·L^-1） on the proliferation and activity of PC9/GR cells were detected by the cell counting kit-8 （CCK-8） assay after 24 hours of intervention. The half-maximal inhibitory concentration （IC₅₀） for PC9/GR cells was calculated to determine the concentrations used in subsequent experiments. PC9/GR cells were divided into blank group （complete medium）， YQYYJD group （5 g·L^-1）， cisplatin group （12 mg·L^-1）， and combined group （YQYYJD 5 g·L^-1 + cisplatin 12 mg·L^-1）. After 24 hours of intervention， cell viability was measured using CCK-8 assay. Cell proliferation was assessed by colony formation assay， and cell migration was evaluated by scratch and Transwell assays. Glucose consumption， lactate production， and adenosine triphosphate （ATP） levels were measured by colorimetric assays. The expression levels of glycolysis-related proteins， including hexokinase 2 （HK2）， phosphofructokinase P （PFKP）， pyruvate kinase M2 （PKM2）， lactate dehydrogenase A （LDHA）， glucose transporter 1 （GLUT1）， and monocarboxylate transporter 4 （MCT4）， were determined by Western blot. ResultsBoth YQYYJD and cisplatin inhibited the viability of PC9/GR cells in a concentration-dependent manner. The IC₅₀ of PC9/GR cells for YQYYJD and cisplatin were 5.15 g·L^-1 and 12.91 mg·L^-1， respectively. In terms of cell proliferation， compared with the blank group， the cell survival rate and the number of colonies formed in the YQYYJD group， cisplatin group， and combined group were significantly decreased （P<0.01）. Compared with the YQYYJD and cisplatin groups， the combined group showed a further significant reduction in cell survival rate and colony formation （P<0.01）. In terms of cell migration， compared with the blank group， the cell migration rate and the number of cells passing through the Transwell membrane in the YQYYJD group， cisplatin group， and combined group were significantly decreased （P<0.01）. Compared with the YQYYJD and cisplatin groups， the combined group exhibited a further significant reduction in cell migration rate and the number of cells passing through the Transwell membrane （P<0.01）. In terms of glycolysis， compared with the blank group， glucose consumption， lactate production， and ATP levels in the YQYYJD group， cisplatin group， and combined group were significantly decreased （P<0.01）. Compared with the YQYYJD and cisplatin groups， the combined group showed a further significant reduction in glucose consumption， lactate production， and ATP levels （P<0.05）. Compared with the blank group， the protein expression levels of HK2， PFKP， PKM2， and LDHA in the YQYYJD， cisplatin， and combined groups were significantly decreased （P<0.01）. The combined group showed a further significant reduction in the expression levels of these proteins compared with the YQYYJD and cisplatin groups （P<0.01）. No significant differences were observed in the protein expression levels of GLUT1 and MCT4 among the groups. ConclusionYQYYJD can synergistically inhibit the proliferation and migration of PC9/GR cells and enhance their sensitivity to cisplatin. The mechanism may be related to the downregulation of the expression of glycolysis-related rate-limiting enzymes， including HK2， PFKP， PKM2， and LDHA， thereby inhibiting glycolysis.