Novel inflammatory and insulin resistance indices provide a clue in cerebral amyloid angiopathy.

This study investigated the correlation of newly identified inflammatory and insulin resistance indices with cerebral amyloid angiopathy (CAA), and explored their potential to differentiate CAA from hypertensive arteriopathy (HA). We retrospectively analyzed 514 consecutive patients with cerebral small vessel disease (CSVD)-related haemorrhage, comparing the differences in novel inflammatory and insulin resistance indices between patients with CAA and HA. Univariate regression, LASSO and multivariate regression were used to screen variables and construct a classification diagnosis nomogram. Additionally, these biomarkers were explored in patients with mixed haemorrhagic CSVD. Inflammatory indices were higher in CAA patients, whereas insulin resistance indices were higher in HA patients. Further analysis identified neutrophil-to-lymphocyte ratio (NLR, OR 1.17, 95% CI 1.07-1.30, P < 0.001), and triglyceride-glucose index (TyG, OR = 0.56, 95% CI 0.36-0.83, P = 0.005) as independent factors for CAA. Therefore, we constructed a CAA prediction nomogram without haemorrhagic imaging markers. The nomogram yielded an area under the curve (AUC) of 0.811 (95% CI 0.764-0.865) in the training set and 0.830 (95% CI 0.718-0.887) in the test set, indicating an ability to identify high-risk CAA patients. These results show that CSVD patients can be phenotyped using novel inflammatory and insulin resistance indices, potentially allowing identification of high-risk CAA patients without haemorrhagic imaging markers.

Correlation between neutrophil gelatinase phase lipocalin and cerebral small vessel disease.

Background: Cerebral small vessel disease (CSVD) is common in the elderly population. Neutrophil gelatinase-associated lipocalin (NGAL) is closely related to cardiovascular and cerebrovascular diseases. NGAL causes pathological changes, such as damage to the vascular endothelium, by causing inflammation, which results in other related diseases. The purpose of this study was to investigate whether serum NGAL levels could predict disease severity in patients with CSVD. Methods: The patients with CSVD who visited the Department of Neurology at the First Affiliated Hospital of Zhengzhou University between January 2018 and June 2022 were prospectively included. The total CSVD burden score was calculated using whole-brain magnetic resonance imaging (MRI), and the patients were divided into a mild group (total CSVD burden score < 2 points) and a severe group (total CSVD burden score ≥ 2 points). Age, sex, height, smoking and alcohol consumption history, medical history, and serological results of patients were collected to perform the univariate analysis. Multivariate logistic regression was used to analyze the risk factors that affect CSVD severity. The multiple linear regression method was used to analyze which individual CSVD markers (periventricular white matter hyperintensities, deep white matter hyperintensities, lacune, and cerebral microbleed) play a role in the association between total CSVD burden score and NGAL. Results: A total of 427 patients with CSVD (140 in the mild group and 287 in the severe group) were included in the study. A multivariate logistic regression analysis showed that the following factors were significantly associated with CSVD severity: male sex [odds ratio(OR), 1.912; 95% confidence interval (CI), 1.150-3.179], age (OR, 1.046; 95% CI, 1.022-1.070), history of cerebrovascular disease (OR, 3.050; 95% CI, 1.764-5.274), serum NGAL level (OR, 1.005; 95% CI, 1.002-1.008), and diabetes (OR, 2.593; 95% CI, 1.424-4.722). A multivariate linear regression shows that periventricular white matter hyperintensities and cerebral microbleed are associated with serum NGAL concentrations (P < 0.05). Conclusion: Serum NGAL level is closely related to CSVD severity and is a risk factor for the burden of CSVD brain damage. Serum NGAL has high specificity in reflecting the severity of CSVD.

Ultra-Broadband, Omnidirectional, High-Efficiency Metamaterial Absorber for Capturing Solar Energy.

In this study, we investigated an absorber based on a center-aligned tandem nanopillar array for ultra-broadband solar energy harvesting theoretically. A high-efficiency, omnidirectional absorber was obtained by introducing the center-aligned tandem nanopillar array embedded in an Al2O3 dielectric layer. The multi-coupling modes at different wavelengths were interpreted. The strong absorption can be adjusted by changing the radii and heights of nanopillars. According to the simulation results, the average absorptance of the absorber exceeded 94% in the wavelength range from 300 nm to 2000 nm. In addition, the high-efficiency absorption was insensitive to the incident angle and polarization state. The research not only proposed an absorber which possesses a huge potential value for application areas, such as thermal photovoltaic systems, infrared detection, and isotropic absorption sensors, but also pointed out a new way to design an absorber with high efficiency in an ultrabroad wavelength range.