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Objective: This study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM)., Methods: Prospectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram., Result: Univariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility., Conclusion: The nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions., (© 2024. The Author(s).)

Structural colors are highly valued for their eco-friendliness and long-term color stability, deriving from the interaction of structural units with incident light. However, traditional methods for adjusting structural colors typically involve altering the size of structural units, a labor-intensive process necessitating specific diameters for each desired color. Moreover, colors exhibited by photonic crystal materials are monochromatic colors with a narrow wavelength range, failing to exhibit polychromatic colors. This restricts their practical applications, as they do not accurately represent the actual color of objects themselves. Hence, this study focuses on fabricating binary supraballs can display polychromatic colors. These supraballs consist of two types of structural units with distinct diameter differences. By adjusting the mass ratio between these units within the supraballs, fine color tuning is achievable. Utilizing three different diameters of silica nanospheres, this method enables the fabrication of supraballs with a diverse range of colors spanning nearly the entire visible spectrum. The adjustable colors of these binary supraballs not only enhance their ability to replicate the colors of objects, but also reduce the significant workload involved in preparing the original structural units. The synthesized supraballs are in powder form, directly applicable as coatings, inks, and other materials., Competing Interests: Declaration of Competing Interest We declare that we have no financial or personal relationships with other individuals or organizations that could inappropriately influence our work. Furthermore, we have no professional or personal interests in any product, service, or company that could be perceived as influencing the content or review of the manuscript entitled., (Copyright © 2024 Elsevier B.V. All rights reserved.)