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 the clinical characteristics of organ donors in the intensive care unit (ICU), analyze the impact of comprehensive ICU treatment on organ function maintenance and donation efficiency, and provide data support for optimizing organ donation management strategies. Methods A retrospective analysis was conducted on the data of 263 donors who underwent organ donation after ineffective active treatment in the ICU of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 2020 to January 2024. The clinical characteristics, main therapeutic measures in the ICU, and organ donation situations were analyzed. Results The 263 organ donors had an out-of-hospital hospitalization duration of 2 (1, 5) days and an in-hospital hospitalization duration of 4 (3, 6) d. The acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score at admission was (21±5). Among them, 16.7% had a history of cardiopulmonary resuscitation, 30.4% had a history of hypertension, and 48.7% had a history of cranial surgery. The duration of enteral nutrition provided in the ICU was 18 (8, 32) h, with daily energy provision of 160 (0, 320) kcal, parenteral nutrition provided non-protein energy of 877 (710, 1 058) kcal daily. Fiberoptic bronchoscopy was performed 0.25 (0, 0.50) times a day. Continuous renal replacement therapy (CRRT) was performed in 90.1% of the cases, with an average daily duration of 10 (6, 16) h. The daily dosage of human albumin was 40 (30, 50) g, and the daily dosage of methylprednisolone was 120 (80, 160) mg. The most commonly used empirical anti-infection regimens included cefoperazone-sulbactam in 59 cases (22.4%), meropenem combined with vancomycin in 31 cases (11.8%), and piperacillin-tazobactam in 29 cases (11.0%). The most commonly used goal-directed anti-infection adjustment regimen was meropenem combined with vancomycin in 21 cases (8.0%). After comprehensive treatment in the ICU, cardiac function, some liver functions, some coagulation functions, renal function, electrolytes, and infection indicators improved. A total of 981 organs were donated by the 263 organ donors, with 23 organs discarded. The average organ yield rate was 3.64, and the organ utilization rate was 97.7%. Conclusions Comprehensive ICU treatment may significantly improve the cardiac function, some liver functions, coagulation functions, and infection indicators of organ donors, enhance the effect of organ function maintenance, and provide an effective guarantee for optimizing organ donation management in the ICU and improving organ utilization rates.

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 microbial contamination status of commercially available ready-to-eat (RTE) foods in Songjiang District of Shanghai, to verify the potential food safety hazards, so as to provide a scientific basis for the local regulation of RTE foods and the prevention of foodborne diseases. MethodsA total of 882 food samples from 9 categories of RTE foods were collected in Shanghai from 2017 to 2022. Salmonella, Listeria monocytogenes, Vibrio parahaemolyticus, Staphyloccus aureus and Bacillus cereus were detected according to the standard operating procedures in the Manual for China National Food Contamination and Harmful Factors Risk Monitoring, and which were determined with reference to relevant standards. ResultsA total of 77 samples were found to carry foodborne pathogens, with a total detection rate of 8.73%. Among the 5 foodborne pathogens, Bacillus cereus had the highest detection rate (8.35%), followed by Vibrio parahaemolyticus (5.05%), and the difference of detection rates among the 5 foodborne pathogens was statistically significant (χ²=112.307, P<0.001). Among the 9 categories of RTE food products, the detection rate of foodborne pathogens in RTE fermented/non-fermented soybean products was the highest (27.50%), followed by cold noodles (17.33%) and raw edible aquatic products (9.50%), and the difference in the detection rates of foodborne pathogens among different RTE food categories was statistically significant (χ²=45.909, P<0.001). The detection rate of foodborne pathogens in the third quarter of the year was 10.99%, significantly higher than that in other quarters (χ²=10.329, P=0.016). The detection rate of foodborne pathogens in samples from supermarkets was highest (15.08%), followed by snack bars (11.58%), with a statistically significant difference (χ²=14.108, P=0.015). ConclusionFoodborne pathogens contamination is found in commercially available RTE foods in Songjiang District of Shanghai, especially in RTE fermented/non-fermented soybean products, RTE cold noodles and raw edible aquatic products from supermarkets, snack bars and farm products markets during summer and autumn. Therefore, it is crucial to strengthen supervision and management to avoid the foodborne diseases.

AIM: To evaluate the accuracy of the formulas, including Haigis, SRK/T, Holladay 1, and Holladay 2, in predicting the diopter of the intraocular lens implanted in high myopia cataract patients.METHODS: Prospective study. A total of 168 cases(168 eyes)of age-related cataract with an axial length(AL)≥26 mm who were treated in our hospital from August 2017 to November 2021 were selected. According to the preoperative AL measured by IOL Master 700, the patients were divided into five groups, including 37 cases(37 eyes)in group A with 26 mm≤AL<27 mm, 34 cases(34 eyes)in group B with 27 mm≤AL<28 mm, 42 cases(42 eyes)in group C with 28 mm≤AL<29 mm, 28 cases(28 eyes)in group D with 29 mm≤AL<30 mm, and 27 patients(27 eyes)in group E with AL ≥ 30 mm. Subjective refraction was performed at 3 mo postoperatively, and the mean numerical error(MNE)and mean absolute error(MAE)of each formula for predicting diopters were calculated.RESULTS: The MNE and MAE of the Haigis and Holladay 2 formulas were relatively less in each group, and MNE and MAE did not significantly increase with the growth of the axial length. However, the MAE and MNE of the SRK/T and Holladay 1 formulas significantly increased with the growth of the axial length, with the MNE and MAE of the Holladay 1 formula increasing more significantly in groups C, D, and E.CONCLUSION: For patients with age-related cataract, with an axial length of ≥26 mm, the accuracy of predicting the diopter of the intraocular lens using the Haigis and Holladay 2 formulas were higher.

School is a densely populated place, which can easily lead to tuberculosis clusters, then affect the physical and mental health of students and the normal teaching order of school. Tuberculosis latent infection (LTBI) screening for new students and close contacts of tuberculosis patients has become important parts of school tuberculosis prevention and control strategies. Previous studies have shown that the LTBI rate of Chinese in-school students is about 5.74% to 11.67%, and there are differences in gender, studying phase and urban-rural distributions. Preventive treatment is an effective measure to prevent LTBI from developing into active tuberculosis, but the proportion of LTBI preventive treatment for students in most areas is low. The difficulties in implementing preventive treatment may be attributed to concerns about adverse reactions to medication and sense of shame towards illness among students, and lack of awareness about preventive treatment among medical staff. This review searches the research literature published from 2016 to 2023, and summarizes the prevalence of LTBI in Chinese students and progress on preventive treatment, so as to provide insights into prevention and control of tuberculosis among students.

Objective To analyze the factors related to early allograft dysfunction(EAD)after liver transplantation and to construct a predictive model.Methods A total of 375 patients who underwent liver transplantation in our hospital from December 2008 to December 2021 were collected,including 90 patients with EAD and 266 patients without EAD.Thirty items of baseline data for the 2 groups were compared and analyzed.Aftergrouping in a ratio of 7∶3,univariate and multivariate logistic regression analyses were used in the training set to evaluate the factors related to EAD and construct a nomogram.Receiver operating characteristic(ROC)curve,decision curve analysis(DCA),sensitivity,specificity,positive predictive value,negative predictive value,Kappa value and other indicators were used to evaluate the model performance.Results The incidence of EAD after liver transplantation was 24%.Multivariate logistic regression analysis showed that preoperative tumor recurrence history(OR=3.15,95%CI:1.28～7.77,P=0.013)and operation time(OR=1.22,95%CI:1.04～1.42,P=0.015)were related to the occurrence of EAD after surgery.After predicting the outcome according to the cut-off point of 0.519 identified by the Youden index,the model performance in the both training set and validation set was acceptable.DCA suggested the model has good clinical applicability.Conclusion The risk factors for EAD after liver transplantation are preoperative tumor recurrence history and operation time,and the established model has predictive effect on prognosis.