Objectives In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures. Method 127 SVD patients were recruited consecutively from a stroke clinic, comprising 76 individuals with mild cognitive impairment (MCI) and 51 with no cognitive impairment (NCI). Detailed neuropsychological assessments and multimodal MRI were performed. SVD scores were calculated on a standard scale, and structural brain network measures were analyzed by diffusion tensor imaging (DTI). Between-group differences were analyzed, and logistic regression was applied to determine the predictive value of SVD and network measures for cognitive status. Mediation analysis with structural equation modeling (SEM) was used to better understand the interactions of SVD burden, brain networks and cognitive deficits. Results Group difference was found on all global brain network measures. After adjustment for age, gender, education and depression, significant correlations were found between global brain network measures and diverse neuropsychological tests, including TMT-B ( r = −0.209, p < .05), DSST ( r = 0.206, p < .05), AVLT short term free recall ( r = 0.233, p < .05), AVLT long term free recall ( r = 0.264, p < .05) and Rey-O copy ( r = 0.272, p < .05). SVD score showed no group difference and was not correlated with cognition tests. Network global efficiency (E Global ) was significantly related to cognitive state ( p < .01) but not the SVD score. Mediation analysis showed that the standardized total effect ( p = .013) and the standardized indirect effect ( p = .016) of SVD score on cognition was significant, but the direct effect was not. Conclusions Brain network measures, but not the SVD score, are significantly correlated with cognition in post-stroke SVD patients. Mediation analysis showed that the cerebral vascular lesions produce cognitive dysfunction by interfering with the structural brain network in SVD patients. The brain network measures may be regarded as direct and independent surrogate markers of cognitive impairment in SVD.
We conducted a cross-sectional study to characterize the relationship between total and modified small vessel disease (SVD) score with vascular cognitive impairment (VCI). Patients (n = 157) between the ages of 50 and 85 years old who had suffered their first lacunar infarction were analyzed prospectively. Brain magnetic resonance imaging was performed to identify SVD manifestations, which were used to calculate total or modified SVD scores. Neuropsychological assessments measured cognitive function. Spearman correlation analysis demonstrated that the total and modified SVD scores were associated with overall cognition as well as with function in the executive and visuospatial domains. The associations remained significant in linear regression after adjusting for age, sex, education and vascular risk factors. Binary logistic regression and chi-squared trend tests revealed that VCI risk increased significantly with SVD burden based on the modified SVD score. Subsequent chi-squared testing demonstrated that the VCI rate was significantly higher in patients with a modified SVD score of 5-6 than in patients without any SVD burden. Our results suggest that both the total and modified SVD scores show a negative association with cognitive function, but the modified SVD score may be better at identifying patients at high VCI risk.
Aims Accumulating evidence has suggested that airborne fine particulate matter (PM2.5) exposure is associated with an increased risk of ischemic stroke. However, the underlying mechanisms have not been fully elucidated. In this study, we aim to investigate the role and mechanisms of NLRP3 inflammasome and pyroptosis in ischemic stroke after PM2.5 exposure. Methods The BV‐2 and HMC‐3 microglial cell lines were established and subjected to oxygen–glucose deprivation and reoxygenation (OGD/R) with or without PM2.5 exposure. We used the CCK‐8 assay to explore the effects of PM2.5 on cell viability of BV‐2 and HMC‐3 cells. Then, the effects of PM2.5 exposure on NLRP3 inflammasome and pyroptosis following OGD/R were detected by western blotting, ELISA, and the confocal immunofluorescence staining. Afterwards, NLRP3 was knocked down to further validate the effects of PM2.5 on cell viability, NLRP3 inflammasome activation, and pyroptosis after OGD/R in HMC‐3 cells. Finally, the intracellular reactive oxygen species (ROS) was measured and the ROS inhibitor N‐acetyl‐L‐cysteine (NAC) was used to investigate whether ROS was required for PM2.5‐induced NLRP3 inflammasome activation and pyroptosis under ischemic conditions. Results We found that PM2.5 exposure decreased the viability of BV‐2 and HMC‐3 cells in a dose‐ and time‐dependent manner under ischemic conditions. Furthermore, PM2.5 exposure aggravated NLRP3 inflammasome activation and pyroptosis after OGD/R, as indicated by an increased expression of NLRP3, ASC, pro‐caspase‐1, Caspase‐1, GSDMD, and GSDMD‐N; increased production of IL‐1β and IL‐18; and enhanced Caspase‐1 activity and SYTOX green uptake. However, shRNA NLRP3 treatment attenuated the effects of PM2.5 on cell viability, NLRP3 inflammasome activation, and pyroptosis. Moreover, we observed that PM2.5 exposure increased the production of intracellular ROS following OGD/R, while inhibiting ROS production with NAC partially attenuated PM2.5‐induced NLRP3 inflammasome activation and pyroptosis under ischemic conditions. Conclusion These results suggested that PM2.5 exposure triggered the activation of NLRP3 inflammasome and pyroptosis under ischemic conditions, which may be mediated by increased ROS production after ischemic stroke. These findings may provide a more enhanced understanding of the interplay between PM2.5 and neuroinflammation and cell death, and reveal a novel mechanism of PM2.5‐mediated toxic effects after ischemic stroke.
ObjectiveThe objective of the study was to explore the value of MRI texture features based on T1WI, T2-FS and diffusion-weighted imaging (DWI) in differentiation of renal changes in patients with stage III type 2 diabetic nephropathy (DN) and normal subjects.Materials and MethodsA retrospective analysis was performed to analyze 44 healthy volunteers (group A) and 40 patients with stage III type 2 diabetic nephropathy (group B) with microalbuminuria. Urinary albumin to creatinine ratio (ACR) <30 mg/g, estimated glomerular filtration rate (eGFR) in the range of 60–120 ml/(min 1.73 m2), and randomly divided into primary cohort and test cohort. Conventional MRI and DWI of kidney were performed using 1.5 T magnetic resonance imaging (MRI). The outline of the renal parenchyma was manually labeled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method.ResultsThere was a significant difference in sex and body mass index (BMI) (P <0.05) in the primary cohort, with no significant difference in age. In the final results, the wavelet and Laplacian–Gaussian filtering are used to extract 1,892 image features from the original T1WI image, and the LASSO algorithm is used for selection. One first-order feature and six texture features are selected through 10 cross-validations. In the mass, 1,638 imaging extracts features from the original T2WI image.1 first-order feature and 5 texture features were selected. A total of 1,241 imaging features were extracted from the original ADC images, and 5 texture features were selected. Using LASSO-Logistic regression analysis, 10 features were selected for modeling, and a combined diagnosis model of diabetic nephropathy based on texture features was established. The average unit cost in the logistic regression model was 0.98, the 95% confidence interval for the predictive efficacy was 0.9486–1.0, specificity 0.97 and precision 0.93, particularly. ROC curves also revealed that the model could distinguish with high sensitivity of at least 92%.ConclusionIn consequence, the texture features based on MR have broad application prospects in the early detection of DN as a relatively simple and noninvasive tool without contrast media administration.
Alzheimer's disease (AD) is the most common cause of dementia [1].Currently there are 44 million people living with dementia worldwide, which will more than triple by 2050 [2]. AD can be classified as familial or sporadic AD, according to family history. AD can also be classified according to age, as either early-onset AD (EOAD; age of onset <65 years) or late-onset AD (LOAD; age of onset >65 years) [3].The identification of genes associated with AD will help us better understand the pathogenesis of AD. Several genes have been discovered; for example, amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) have been shown to be associated
This study aims to investigate the effectiveness and safety of Qingfeijianpi therapy for persistent allergic rhinitis (AR).A total of 101 patients with persistent AR were included into the study. These patients were randomly divided into 2 groups: treatment group, in which patients were given Qingfeijianpi decoction (1 dose/day for 4 weeks); control group, in which patients were given an oral dose of loratadine (tablet, 10 mg/time, once per day for 4 weeks). A total of 96 patients completed the 16-week course of treatment. The nasal symptom and traditional Chinese medicine syndrome were scored to evaluate symptom improvement, and the Rhinoconjunctivitis Quality of Life Questionnaire was used to assess the quality of life of patients. Furthermore, the 4-week clinical treatment effect and 16-week follow up were evaluated.After the 4-week treatment, the mean difference in symptom score in the treatment group was similar to that in the control group. However, during the follow-up, the decrease in symptom score was better with the Qingfeijianpi therapy than with loratadine.Compared to loratadine, the Qingfeijianpi decoction exhibited a significant benefit in symptom improvement of persistent AR.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.