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 explore changes of acid-base metabolism in the brain tissue of patients with chronic ischemic cerebrovascular disease (CICVD) using MRI amide proton transfer-weighted (APTw) imaging.Methods:This was a cross-sectional study. From January 2021 to July 2022, thirty-nine patients with CICVD at West China Hospital, Sichuan University were retrospectively included. All patients received CT perfusion (CTP) and APTw imaging. NeuBrainCARE brain perfusion software was used to analyze the impaired perfusion sites and measure the mean transit time (MTT) and time to peak (TTP). Standard spatial matching between CTP and APTw images was performed to measure the APTw values of the same sites. For comparison with normal tissue, APTw values were measured for normal-appearing white matter (NAWM) in the ipsilateral cerebral hemisphere, the contralateral cerebral hemisphere, and the ipsilateral cerebellar hemisphere in areas of impaired perfusion. ANOVA was used to compare the APTw values of impaired perfusion brain tissue, ipsilateral cerebral NAWM, contralateral cerebral NAWM, and ipsilateral cerebellar NAWM. The Bonferroni method was used to correct for multiple comparisons. Pearson correlation coefficient was used to analyze the correlation between APTw values and MTT and TTP in the cerebral tissue with impaired perfusion.Results:In 39 patients with CICVD, both the mean and minimum APTw values of cerebral tissue with impaired perfusion were significantly lower than those in the NAWM of the ipsilateral cerebral hemisphere, the contralateral cerebral hemisphere, and the ipsilateral cerebellar hemisphere ( P<0.001). In the NAWM of the cerebellar hemispheres with unimpaired perfusion, both the mean and minimum APTw values were significantly higher than those in the ipsilateral cerebral hemispheres and the contralateral cerebral hemisphere ( P<0.001). Correlation analysis showed that MTT was significantly negatively correlated with both the mean APTw and the minimum APTw ( r values were -0.90 and -0.82, P<0.001). TTP was significantly negatively correlated with both the mean APTw and the minimum APTw ( r values were -0.86 and -0.78, P<0.001). Conclusion:APTw value can reflect acidosis in cerebral tissue with impaired perfusion in patients with CICVD.

{L-End}Objective To estimate the number of workers with occupational noise-induced hearing loss (ONHL) in the manufacturing industry of China in 2020. {L-End}Methods Multiple data sources were integrated to collect information on workers in the manufacturing industry from the Occupational Diseases and Hazards Monitoring Information System under China Disease Prevention and Control Information System. The total number of workers was split based on age from the 2018 Fourth National Economic Census and the 2020 National Survey of Occupational Disease Hazards. Additionally, data on the prevalence of hearing loss in the general population was adjusted based on the age composition of Jilin Province, and noise prevalence was standardized based on the age composition of the employed population in the Seventh National Population Censu. Attribution fractions (AF) was estimated. The prevalence ratio (PR) was calculated by the prevalence of hearing loss in the occupational noise-exposed workers and general population. The proportion and attributable cases (AC) of ONHL cases in all hearing loss cases were estimated for occupational noise-exposed workers. The number of ONHL was estimated based on AF and the total number of workers with hearing loss. {L-End}Results In 2020, a total of 30 059 525 workers were exposed to occupational noise in the manufacturing industry in China, with noise-exposed prevalence and standardized noise-exposed prevalence of 28.94% and 28.52%, respectively. The prevalence of hearing loss among occupational noise-exposed workers increased with age, and a total of 8 054 789 workers suffered from hearing loss. Most of the cases were at the age between 45-<55 years old. The total PR and 95% uncertainty interval (UI) was 2.83 (2.58-3.06) and the total AF and 95%UI was 64.63% (61.22%-67.30%), and both peaks were in the age of 30-<45 years. The AC and 95%UI was 5 205 980 (4 930 620-5 420 345) persons, and most of the cases were at the age between 40-<55 years. {L-End}Conclusion Occupational noise poses a serious threat to the hearing health of workers in the manufacturing industry of China, especially among middle-aged and young adults.

Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.

Objective:To explore the contents and methods of clinical comprehensive evaluation of microecologics, with the example of Bifidobacterium quadruple viable tablet, in order to provide evidence for clinical rational use of microecologics, and microecologics research, development and related decision-making, and to promote rational use of medications. Methods:Based on the research data collected from systematic literature search, health technology assessment methods such as evidence-based medicine and pharmacoeconomics evaluation were used to estimate the safety, efficacy, economics, suitability, accessibility and innovation of Bifidobacterium quadruple viable tablet. Results:In terms of efficacy, Bifidobacterium quadruple viable tablet showed significant therapeutic effects in the treatment of pediatric diarrhea, antibiotic-related diarrhea, diarrhea-predominant irritable bowel syndrome, and secondary diarrhea caused by diseases such as ulcerative colitis, as well as constipation and functional dyspepsia. It can also be used in the treatment of various diseases such as Helicobacter pylori related gastritis, liver cirrhosis, non-alcoholic fatty liver disease. In terms of safety, the incidence of adverse effects of this medication was low, and most were mild to moderate and transient symptoms. In terms of economics, compared with mesalazine alone in the treatment of ulcerative colitis, the incremental cost-effectiveness ratio of combination of Bifidobacterium quadruple viable tablet and mesalazine was 1 743.2. Besides, the daily treatment cost of Bifidobacterium quadruple viable tablet was lower than that of combination of Bifidobacterium triple viable and Bacillus licheniformis (1.87 to 2.80 yuan vs. 2.08 to 5.78 yuan). In terms of innovation, this medication had multiple patents and had been identified as a high-tech product in Zhejiang Province. In terms of suitability, the overall suitability of use and technical characteristics of medication were good. It could be further improved in the aspects of dosage form and system. In terms of accessibility, the price of the medication was stable, affordable and accessible to the general public. Conclusions:Based on the existing evidence, Bifidobacterium quadruple viable tablet presented effective with supported evidences, good safety, accessibility and innovation. The suitability can be further optimized. However, more in-depth and targeted research is needed in terms of economics and innovation in different clinical applications, and there is space for optimization in medication suitability.

Objectives:To investigate the prevalence,clinical characteristics and risk factors of coronavirus disease-2019(COVID-19)in patients with pulmonary hypertension(PH). Methods:A questionnaire survey was conducted from December 30,2022 to January 6,2023 through the WeChat official account of the PH Patients Mutual Aid Organization.PH patients aged≥18 years from 26 province(municipality/autonomous region)were recruited to fill in the electronic survey questionnaire. Results:A total of 293 valid questionnaires were collected from PH patients.The mean age of patients was(40.6±12.7)years,and 226 patients(77.1%)of them were female.The vaccination rate was 59.7%(175/293),117 patients(39.9%)received three or more doses of vaccine,145 patients(49.5%)received inactivated vaccine.242 patients(82.6%)had COVID-19.The most common symptoms during infection were fever(85.5%),cough(77.7%),and fatigue(66.5%).10.7%of the patients had severe or critical COVID-19.Age(OR =1.057,95%CI:1.027-1.087,P<0.001)and comorbid pulmonary disease(OR=3.341,95%CI:1.215-9.184,P=0.019)were associated with severe or critical COVID-19.After adjusting for confounding factors,age was an independent risk factor for severe or critical COVID-19(OR=1.049,95%CI:1.019-1.080,P=0.001).Severe or critical COVID-19 was an independent risk factor for worsening heart failure in PH patients during COVID-19 pandemic(OR=10.522,95%CI:4.311-25.682,P<0.001). Conclusions:The immunization coverage of PH patients is insufficient.PH patients have a higher risk of developing severe or critical COVID-19 than general population.Ageing is an independent risk factor for severe or critical COVID-19,and the risk of worsening heart failure in PH patients with severe or critical COVID-19 is significantly increased during COVID-19 pandemic.