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Objective: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI)., Materials and Methods: A total of 202 patients (87 males, 115 females) with 439 brain metastases were retrospectively included, divided into training sets (brain metastases of lung cancer [BMLC] n  = 194, brain metastases of breast cancer [BMBC] n  = 108, brain metastases of gastrointestinal tumor [BMGiT] n  = 48) and test sets (BMLC n  = 50, BMBC n  = 27, BMGiT n  = 12). A total of 3,404 quantitative image features were obtained through semi-automatic segmentation from MRI images (T1WI, T2WI, FLAIR, and T1-CE). Intra-class correlation coefficient (ICC) was used to examine segmentation stability between two radiologists. Radiomics features were selected using analysis of variance (ANOVA), recursive feature elimination (RFE), and Kruskal-Wallis test. Three machine learning classifiers were used to build the radiomics model, which was validated using five-fold cross-validation on the training set. A comprehensive model combining radiomics and clinical features was established, and the diagnostic performance was compared by area under the curve (AUC) and evaluated in an independent test set., Results: The radiomics model differentiated BMGiT from BMLC (13 features, AUC = 0.915 ± 0.071) or BMBC (20 features, AUC = 0.954 ± 0.064) with high accuracy, while the classification between BMLC and BMBC was unsatisfactory (11 features, AUC = 0.729 ± 0.114). However, the combined model incorporating radiomics and clinical features improved the predictive performance, with AUC values of 0.965 for BMLC vs. BMBC, 0.991 for BMLC vs. BMGiT, and 0.935 for BMBC vs. BMGiT., Conclusion: The machine learning-based radiomics model demonstrates significant potential in distinguishing the primary sites of brain metastases, and may assist screening of primary tumor when brain metastasis is suspected whereas history of primary tumor is absent., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Yang, Su, Li, Zhao and Yue.)

Carbon based materials are widely used in the preparation of microwave absorption materials due to their low density, high attenuation loss and large specific surface area. However, their high conductivity usually leads to high reflection loss. In this study, multi-layer heterogeneous interfaces were constructed in liquid metal graphite hybrid powder to reduce reflection loss and enhance microwave absorption performance. Gallium oxide (Ga 2 O 3 ) layer was formed in Ga coated graphite powder to improve impedance matching and attenuation constant via an annealing treatment. Specifically, the hybrid particles with 50 wt% Ga and being annealed at 120 °C for 2 h have a minimum reflection loss (RL min ) value of -42.68 dB and a maximum effective absorption bandwidth (EAB) of 4.11 GHz at a thickness of 3.3 mm. The hybrid particles not only have multi-layer structures with different electrical conductivity, but also form heterojunctions between different interfaces, which can further enhance dipole and interfacial polarization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)