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 observe the value of propofol combined with etomidate in bariatric surgery based on hemodynamics and stress response.Methods Patients admitted to Shanxi Bethune Hospital who underwent bariatric surgery from January 2020 to January 2021 were retrospectively selected and divided into an experimental group (propofol combined with etomidate) and a control group (propofol) according to the intraoperative anesthesia regimen.The patient's surgical time,awakening time,extubation time,hemodynamic indicators[heart rate (HR),mean arterial pressure (MAP),blood oxygen saturation (SpO2)],stress indicators[adrenocorticotropic hormone (ACTH),adrenaline (ADR),cortisol (COR)],Mini Mental State Examination (MMSE) score,and adverse reactions were recorded.Results A total of 80 patients were included in the study,among which 47 cases were in the experimental group,and 33 cases were in the control group.There was no statistically significant difference in operation time,awakening time and extubation time between the two groups (P>0.05).Immediately after intubation,HR and MAP of the experimental group were lower than those of the control group,and SpO2 was higher than that of the control group (P<0.05).Serum ACTH,ADR and COR levels in the experimental group were higher than those in the control group immediately after intubation and 5 min after extubation (P<0.05).The MMSE score of the experimental group 10 min after awakening was higher than that of the control group (P<0.05).The difference in the incidence of adverse reactions between the two groups was not statistically significant (P>0.05).Conclusion The use of propofol combined with etomidate in weight loss surgery can reduce hemodynamic fluctuations,alleviate cognitive impairment,alleviate stress reactions,and does not significantly increase anesthesia adverse reactions,which is with good safety.

Objective To investigate the effect of down-regulating adenosine deaminase acting on RNA-1(AD AR1)on the radiosensitivity of lung adenocarcinoma cells.Methods Lentiviral transfection was used to establish an ADAR1 knockdown cell line based on A549 cells.Then the cells were divided into negative control(shNC)and ADAR1 knockdown(shADAR1)groups,which were followed by a single-dose irradiation of 0 Gy and 6 Gy X-rays.Western blotting and RT-PCR were utilized to detect the expression of AD AR1 at protein and mRNA levels,respectively.CCK-8 assay,wound healing assay and Transwell migration assay were applied to measure cell proliferation and migration abilities.Meanwhile,clone formation assay was performed to detect the effect of down-regulating ADAR1 on the radiosensitivity of A549 cells.Flow cytometry and Western blotting were conducted to detect the expression levels of apoptosis and apoptosis-related proteins Bax and Bcl-2.Immunofluorescence assay and Western blotting were used to detect the expression level of γ-H2AX.Comet assay was performed to detect the level of cellular DNA damage.Twelve female nude mice(4～6 weeks old,weighing 16～18 g)were divided into shNC group,shADAR1 group,shNC+ionizing radiation(IR)group and shADAR1+IR group,with 3 mice in each group.The growth of tumor of different groups was observed with subcutaneous tumorigenesis assay.Results Western blotting and RT-qPCR showed that the protein and mRNA expression of ADAR1 were significantly reduced in A549 shADAR1 cells(P＜0.05).CCK-8 assay,wound healing assay and Transwell migration assay indicated that down-regulation of ADAR 1 inhibited the proliferation and migration abilities of A549 cells,and this inhibition trend became more obvious(P＜0.01)after IR.Cell clone formation assay showed that the clone formation rate of both groups was decreased,with the increase of radiation dose.But the number of formed clones was lower in the shADAR1 group than the shNC group.Flow cytometry and Western blotting displayed that down-regulation of AD AR1 increased the apoptotic rate and Bax expression in A549 cells(P＜0.01)and decreased Bcl-2 expression(P＜0.05),and the apoptotic rate and Bax protein level were further increased in A549 shADAR1 cells after IR(P＜0.01),and the Bcl-2 protein level was further decreased(P＜0.01).The number of γ-H2AX foci and protein level in A549 shADAR1 cells were significantly increased after IR(P＜0.05),and the results of comet assay showed that the DNA damage was more obvious in A549 shADAR1 cells after IR(P＜0.01).Subcutaneous tumorigenesis assay in nude mice showed that the growth of subcutaneous tumour of A549 shADAR1 cells was significantly inhibited after IR(P＜0.01).Conclusion Down-regulation of ADAR1 significantly inhibits the proliferation and migration of A549 cells after IR and promotes apoptosis and DNA damage,and thereby increases the radiosensitivity of lung adenocarcinoma cells.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.

The human oral microbiome harbors one of the most diverse microbial communities in the human body,playing critical roles in oral and systemic health.Recent technological innovations are propelling the characterization and manipulation of oral microbiota.High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes.New long-read platforms improve genome assembly from complex samples.Single-cell genomics provides insights into uncultured taxa.Advanced imaging modalities including fluorescence,mass spectrometry,and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution.Fluorescence techniques link phylogenetic identity with localization.Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification.Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches.Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly,gene expression,metabolites,microenvironments,virulence mechanisms,and microbe-host interfaces in the context of health and disease.However,significant knowledge gaps persist regarding community origins,developmental trajectories,homeostasis versus dysbiosis triggers,functional biomarkers,and strategies to deliberately reshape the oral microbiome for therapeutic benefit.The convergence of sequencing,imaging,cultureomics,synthetic systems,and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict,prevent,diagnose,and treat associated oral diseases.