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.

Background Kurtosis reflecting noise's temporal structure is an effective metric for evaluating noise-induced hearing loss (NIHL), and its threshold is still unclear. Objective To explore the energy range of kurtosis and the threshold of NIHL induced by kurtosis in this energy rangeMethods Using cross-sectional design, 4631 noise-exposed workers in manufacturing industry were selected. The hearing loss and noise exposure data of each worker were collected. Logistic regression model was used to establish the dose-response relationship between noise exposure and hearing loss and estimate the range of energy effects. Restricted cubic spline model was utilized to analyze the dose-response relationship curves of kurtosis to noise-induced hearing loss (NIHI) and high-frequency noise-induced hearing loss (HFNIHL) under different 8 h equivalent sound levels (L_{EX,8 h}), as well as to estimate the kurtosis effect threshold. Results The logistic regression model analysis showed that the prevalence of NIHI and HFNIHL increased significantly with increase of L_{EX,8 h} for steady noise; when L_{EX,8 h} of non-steady noise was 80-100 dB(A), the risk of hearing loss increased with an increase in kurtosis (P<0.05). The restricted cubic spline model showed that when L_{EX,8 h} of non-steady noise was 0-80 dB(A) or >100 dB(A), there was no significant relationship between kurtosis and NIHI or HFNIHL. When L_{EX,8 h} was 80-100 dB(A), there was a significant nonlinear dose-response relationship between kurtosis and NIHL or HFNIHL (P <0.001), and kurtosis > 28.08 exerted effect in elevating NIHI or HFNIHL. Conclusion The effect threshold of kurtosis on NIHL is 28.08 when non-steady noise levels are in the range of 80-100 dB(A). The effect threshold of kurtosis needs further verification.

Background Temporal kurtosis (without frequency weighting, i.e., Z-weighted kurtosis) can evaluate noise-induced hearing loss (NIHL). However, few studies have considered the function of frequency weighting (A- or C-weighted) kurtosis on NIHL. Objective To study the significance of A- and C-weighted kurtosis adjustment for equivalent sound level (L'_{EX,8 h}) in evaluating occupational hearing loss. Methods A cross-sectional survey was used to select 973 noise-exposed workers in seven industries as the subjects. The noise exposure of all workers was assessed by distributions of A-, C-, and Z-weighted kurtosis (e.g., K_A, K_C, and K_Z) and respective adjusted equivalent sound level (e.g., L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z). The significance of A- and C-weighted kurtosis in evaluating NIHL was evaluated by correlations between three types of L'_{EX,8 h} and NIHL, and improvement of noise-induced permanent threshold shift (NIPTS) underestimation predicted by the ISO prediction model (Acoustics—Estimation of noise-induced hearing loss, ISO 1999-2013). Results The median K_A, K_C, and K_Z were 68.33, 28.22, and 19.82, respectively. The binary logistic regression showed that L_{EX, 8 h}-K_A, L_{EX, 8 h}-K_C, and L'_{EX, 8 h}-K_Z were risk factors for NIHL (OR>1, P<0.001). The receiver operating characteristic (ROC) curve showed that when the outcome variable was noise-induced hearing impairment (NIHI), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX,8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.625, 0.628, and 0.625, respectively. When the outcome variable was high-frequency noise-induced hearing loss (HFNIHL), the areas under the curves corresponding to L'_{EX,8 h}-K_A, L'_{EX, 8 h}-K_C, and L'_{EX,8 h}-K_Z were 0.624, 0.623, and 0.622, respectively (P<0.05). The order of underestimation improvement values predicted by L'_{EX,8 h} for NIPTS₁₂₃₄ was: L'_{EX,8 h}-K_A (4.68 dB HL)>L'_{EX,8 h}-K_C (4.38 dB HL)>L'_{EX,8 h}-K_Z (4.28 dB HL) (P<0.001). The order of underestimation improvement values predicted by L'_{EX,8 h}-K for NIPTS₃₄₆ was: L'_{EX,8 h}-K_A (7.20 dB HL)>L'_{EX,8 h}-K_C (6.83 dB HL)>L'_{EX,8 h}-K_Z (6.71 dB HL) (P<0.001). Conclusion The adjustment of A- and C-weighted kurtosis to equivalent sound level L_{EX,8 h} can effectively improve the accuracy of the ISO 1999 prediction model in NIPTS prediction, and compared with the C-weighted, the A-weighted kurtosis can improve the result of the ISO 1999 prediction model in terms of underestimating NIPTS.

background Current assessment of noise-induced hearing loss relies on the hearing threshold statistical distribution table of ISO 7029-2017 standard (ISO 7029), which is based on foreign population data and lacks a hearing threshold distribution table derived from pure-tone audiometry data of the Chinese population, hindering accurate evaluation of hearing loss in this group. Objective To establish a statistical distribution table of hearing threshold level (HTL) for otologically normal Chinese adults and to provide a scientific basis for revising the diagnostic criteria of occupational noise-induced deafness in China. Methods A total of 1271 otologically normal Chinese adults aged 18-60 years were divided into four groups with 10-year age intervals. Based on the pure tone audiometry data and power function operation formula, the relevant parameters of the median and percentile calculation formula of ISO 7029 were adjusted. Based on the adjusted parameters, a statistical distribution table of HTL of normal Chinese adults in otology was preliminarily constructed according to the calculation formula of ISO 7029. A nonparametric rank sum test was used to compare the difference between the adjusted median deviation value and the ISO 7029 calculation. Results Except for a few frequencies (2000, 3000, and 8000 Hz), the value of parameter α adjusted for the auditory threshold deviation of the Chinese population (α_CHN-md) increased with the increase of frequency, while the value of parameter β adjusted for the auditory threshold deviation of the Chinese population (β_CHN-md) decreased with the increase of frequency. The preliminary distribution table of otologically normal Chinese adults' median hearing threshold deviation (ΔH_CHN-md) showed that the hearing threshold deviation increased with age and frequency. At 8000 Hz, the increase in ΔH_CHN-md with age was the greatest for both men and women, from 0 dB to 23 dB and 21 dB respectively. The minimal threshold elevation was noted at 500, 1000, and 2000 Hz, which increased from 0 dB to 2 dB. In otologically normal Chinese adults, the maximum increase of ΔH_CHN-md with frequency was observed at age of 50 years, which increased from 2 dB at 500 Hz to 23 dB at 8000 Hz in men and from 2 dB at 500 Hz to 21 dB at 8000 Hz in women. The comparison results with the ISO 7029 calculation showed that the trend was consistent. The median hearing threshold deviation value of the adjusted Chinese population was lower than that of the ISO 7029 calculation (ΔH_md). However, they had no significant statistical difference (P>0.05). For otologically normal normal Chinese men and women, the maximum difference of ΔH_md between the 50-year-old group and the ISO calculation at 8000 Hz was 7 dB and 9 dB, respectively. Conclusion This preliminary analysis of hearing threshold deviations in otologically normal Chinese adults suggests that current standards may potentially underestimate hearing loss at frequencies below 8000 Hz while possibly overestimating it at 8000 Hz. These findings require further validation through expanded sample sizes to more accurately assess the characteristics of hearing thresholds in the Chinese population and their discrepancies with existing standards.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

ObjectiveTo analyze the current status of per⁃ and polyfluoroalkyl substances (PFASs) contamination in animal⁃derived foods in Shanghai and to assess the risk of dietary exposure among local residents, thereby providing a scientific basis for future dietary safety and risk management. MethodsA total of 300 commonly consumed animal⁃derived food samples were collected in Shanghai in 2023 and tested for 17 types of PFASs. Based on local dietary consumption data, the weekly exposure intake (EWI) of four representative PFASs was calculated using the exposure assessment model recommended by Food and Agriculture Organization (FAO) and the World Health Organization (WHO). The calculated EWI was compared with the tolerable weekly intake (TWI) set by the European Food Safety Authority (EFSA) to assess the health risks associated with dietary intake of PFASs. ResultsThe predominant PFASs detected in aquatic products were PFUnDA (98.67%), PFTrDA (98.00%), and PFOS (95.33%), with PFUnDA having the highest median mass fraction (0.378 ng·g^-1). In meat samples, PFBA (54.17%), PFOA (20.83%), and PFOS (18.33%) were mainly detected, with PFBA showing the highest median mass fraction (0.027 ng·g^-1). In egg samples, the most frequently detected compounds were PFBA (90.00%), PFOA (63.33%), PFOS (50.00%), PFDA (50.00%), PFNA (50.00%), and PFHxDA (50.00%), with PFBA again having the highest median mass fraction (0.068 ng·g^-1). The median mass fractions of the four key PFASs (PFOA, PFOS, PFNA, and PFHxS) in aquatic products were significantly higher than those in meat products and eggs (all P<0.001), and there were statistically significant differences in the median mass fractions of the four PFASs among different types of aquatic products (all P<0.05). The EWI of the four PFASs was 2.970 ng·kg^-1 for the mean consumption group and 6.676 ng·kg^-1 for the high consumption group (P₉₅), with females having higher EWI values than males. ConclusionPFOS, PFOA, and PFBA are the major PFAS contaminants in animal⁃derived foods consumed by Shanghai residents, with aquatic products having the highest levels of contamination. The EWIs of four PFASs among Shanghai residents did not exceed EFSA’s recommended TWI at average consumption levels. However, the EWIs for high⁃consumption populations exceeded the EFSA⁃recommended TWI, posing potential health risks to these populations. Aquatic products contribute the most to dietary PFAS exposure. It is necessary to strengthen PFAS monitoring in aquatic products and conduct targeted risk assessments for high⁃consumption groups.