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 reveal the molecular biological mechanism of a rare Dc-variant individual using PacBio third-generation sequencing technology. Methods: ABO and Rh blood type identification, DAT, unexpected antibody screening and D antigen enhancement test were conducted by serological testing. The absorption-elution test was used to detect the e antigen. RHCE gene typing was performed by PCR-SSP, and the 1-10 exons of RHCE were sequenced by Sanger sequencing. The full-length sequences of RHCE, RHD and RHAG were detected by PacBio third-generation sequencing technology. Results: Serological findings: Blood type O, Dc-phenotype, DAT negative, unexpected antibody screening negative; enhanced D antigen expression; no detection of e antigen in the absorption-elution test. PCR-SSP genotyping indicated the presence of only the RHCE c allele. Sanger sequencing results: Exons 5-9 of RHCE were deleted, exon 1 had a heterozygous mutation at c. 48G/C, and exon 2 had five heterozygous mutations at c. 150C/T, c. 178C/A, c. 201A/G, c. 203A/G and c. 307C/T. Third-generation sequencing results: RHCE genotype was RHCE 02N. 08/RHCE-D(5-9)-CE; RHD genotype was RHD 01/RHD 01; RHAG genotype was RHAG 01/RHAG 01 (c. 808G>A and c. 861G>A). Conclusion: This Dc-individual carries the allele RHCE 02N. 08 and the novel allele RHCE-D(5-9)-CE. The findings of this study provide data support and a theoretical basis for elucidating the molecular mechanisms underlying RhCE deficiency phenotypes.

Blood turbidity and collateral disease are closely related to each other as the important component parts of the theoretical system of modern traditional Chinese medicine (TCM). The former focuses on blood and the latter on blood vessels and collaterals. By integrating these two theories, a theoretical basis for TCM syndrome differentiation and treatment of modern diseases can be provided. This article summarizes the correlation of origin, concept, treatment method and representative drugs of two theories, and points out that both blood turbidity and collateral disease prospers and develops through the integration of TCM classical theory and modern medical achievements. Theoretically, blood turbidity is the cause of collateral disease, and collateral disease is the result of aggravated blood turbidity. In many metabolic diseases, blood turbidity and collateral disease actually correspond to the main features of the early and late stages of the same disease, respectively. In treatment, clearing blood turbidity is consistent with dredging collaterals. When clearing blood turbidity, it is necessary to dredge the collaterals, and when dredging the collaterals, it is necessary to clear the blood turbidity. In terms of prescription and medication, Huazhuo Xingxue Decoction is the representative prescription of blood turbidity, which can be combined with Ramulus Cinnamomi, Sichuan lovage rhizome, earthworm, and other dredging collateral drugs. The representative prescription for collateral disease is Tongxinluo Capsule, which can be combined with lotus leaf, Fructus Crataegi, cassia seed, and other turbid-clearing drugs to enhance the curative effect.

Acute kidney injury(AKI)is a global public health problem with high morbidity,high mortality and costly treatment cost.The pathogenesis of AKI is very complex,and the treatment strategies for AKI are lim-ited,then it is very matter to explore the pathophysiological mechanism and potential therapeutic targets of acute kidney injury.N6-methyladenosine(m6A)is the most abundant and extremely conservative epigenetic modification in eukaryotic,which is a dynamic and reversible process involving in splicing,nuclear export,translation,stabil-ity,and higher structure of RNA,and regulated by three regulatory factors:methyltransferase,demethylase and methylated reading protein.Current studies have found that m6A plays an important regulatory role in AKI and can be a potential therapeutic target for AKI.In this review,we provide a brief description of m6A and summarize the impact of m6A on AKI and possible future study directions for this research.

ObjectiveTo observe the clinical efficacy of Shengmaisan combined with polymyxin B in the treatment of carbapenem-resistant gram-negative bacillus infection with sepsis complicated with severe acute respiratory distress syndrome. MethodA total of 90 patients suffering from carbapenem-resistant gram-negative bacillus infection with sepsis complicated with severe acute respiratory distress syndrome were randomly divided into a control group and an observation group， with 45 cases in each group. The control group was treated with polymyxin B， and the observation group was treated with Shengmaisan combined with polymyxin B. The treatment course of both groups was seven days. The infection-related indicators ［white blood cell （WBC） count， procalcitonin （PCT）， neutrophil apolipoprotein （HNL）］， inflammatory factors ［interleukin-6 （IL-6）， serum chemokine ligand 2 （CXCL2）］， and T lymphocyte subpopulations （CD3⁺， CD4⁺， CD8⁺， and CD4⁺/ CD8⁺ value）， acute physiological and chronic health Ⅱ （APACHE Ⅱ） score before and after treatment， as well as bacterial clearance rate and 28-day survival rate after treatment were observed. Result① The experiment was completed， and 81 cases were included， including 41 cases in the observation group and 40 cases in the control group. The general data of the two groups were comparable. ② The bacterial clearance rate of the observation group and the control group was 75.6% （31/41） and 52.5% （21/40）， respectively， and the observation group was higher than the control group （χ²=4.7， P<0.05）. ③ The WBC count， PCT， HNL， IL-6， CXCL2， and APACHE Ⅱ scores of the observation group and the control group all decreased after treatment （P<0.05）. Except for the WBC count， the PCT， HNL， IL-6， CXCL2， and APACHE Ⅱ scores of the observation group were lower than those of the control group （P<0.05）. ④ The values of CD3⁺， CD4⁺， and CD4⁺/CD8⁺ in the observation group were increased after treatment （P<0.05）， and CD8⁺ was decreased （P<0.05）. In the control group， only CD3⁺ value was increased （P<0.05）. The values of CD3⁺， CD4⁺， and CD4⁺/CD8⁺ in the observation group were higher than those in the control group， and the value of CD8⁺ was lower than that in the control group （P<0.05）. ⑤ The 28-day survival rate in the observation group was higher than that in the control group （χ²=4.3， P<0.05）. ConclusionShengmaisan combined with polymyxin B in the treatment of carbapenem-resistant gram-negative bacillus infection with sepsis complicated with severe acute respiratory distress syndrome can better clear bacteria， control infection， reduce the level of inflammatory factors， regulate the immune state of the body， and improve the short-term prognosis.

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.