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.

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.
Humans ; Phylogeny ; Biomimetics ; Dysbiosis ; Homeostasis ; Mass Spectrometry

【Objective】 To compare the desalination effects of five desalination methods and their effects on the components for human coagulation factor Ⅷ(FⅧ), and provide reference for selection of protein desalination methods. 【Methods】 Sephadex G-25 Medium gel, Fractogel EMD BioSEC gel, ultrafiltration, room temperature dialysis and 4℃ dialysis were used to desalt human FⅧ. The desalination effect was evaluated by the removal rate of Na +, citrate ion and glycine. FⅧ protein recovery, FⅧ activity (FⅧ∶C), VWF antigen (VWF∶Ag), VWF activity(VWF∶Ac), VWF polymers and SDS-PAGE analysis before and after desalination were compared to evaluate the effect of desalination on FⅧ components. 【Results】 In terms of desalination effect, the removal rate of Na⁺ was the lowest in ultrafiltration desalination, while that of Fractogel EMD BioSEC gel was the highest [(97.90±0.06) % vs (99.82±0.07) %]. Except that there was no statistical significance between Sephadex G-25 Medium gel desalination and Fractogel EMD BioSEC gel desalination (P=0.90), the removal rates of the other four methods were statistically significant. The removal rate of glycine was the lowest in ultrafiltration desalination, wihle that of Fractogel EMD BioSEC gel desalination was the highest [(95.78±0.42) % vs (99.81±0.08) %]. Significant difference in glycine removal was noticed in ultrafiltration desalination, but not among the other four desalination methods. There was no significant difference in the removal rate of citrate ions among the five methods (P=0.85). For the effect of FⅧ components, FⅧ∶C, VWF∶Ag, VWF∶Ac and protein recovery rates of ultrafiltration desalination were the highest, with (18.34±1.99) IU/mL, (11.81±0.33) IU/mL, (12.26±0.58) IU/mL and (97.13±1.37) %, respectively. There was no significant change in VWF∶Ac/VWF∶Ag before and after desalination by the five methods. SDS-PAGE and VWF polymer analysis showed that different desalination methods had no significant impact on protein composition. 【Conclusion】 Although different desalination methods had no significant effect on the composition of FⅧ protein, the desalination effect was different. Moreover, different desalination methods had significant effects on protein recovery, FⅧ∶C, VWF∶Ag and VWF∶Ac. The selection of desalination methods should be more considered during protein processing,

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.