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Using a melt-mixing technique, ethylene–vinyl acetate (EVA) copolymers based composites containing Multi-Wall Carbon nanotubes (MWCNTs), aluminum trihydrate (ATH), zinc borate (ZB), and gadolinium oxide (Gd2O3) were fabricated. Then, the as-prepared samples were irradiated with an electron beam (EB) at the dose range varied from 0 to 275 kGy. Moreover, we investigated the properties of irradiated and unirradiated samples. Morphological observation showed good dispersion of MWCNTs and other additives in EVA. Furthermore, the gel content, mechanical properties, the limit oxygen index (LOI) values of irradiated composites increased to 28.4% after 250 kGy after irradiated. All the conclusions indicated that the comprehensive properties of the WMCNTs/EVA composites were improved by using high energy electron beam irradiation.

Rationale and Objectives To develop an ultrasomics model for preoperative pathological grading of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). Material and Methods A total of 235 HCCs were retrospectively enrolled, including 65 high-grade and 170 low-grade HCCs. Representative images of four-phase CEUS were selected from the baseline sonography, arterial, portal venous, and delayed phase images. Tumor ultrasomics features were automatically extracted using Ultrasomics-Platform software. Models were built via the classifier support vector machine, including an ultrasomics model using the ultrasomics features, a clinical model using the clinical factors, and a combined model using them both. Model performances were tested in the independent validation cohort considering efficiency and clinical usefulness. Results A total of 1502 features were extracted from each image. After the reproducibility test and dimensionality reduction, 25 ultrasomics features and 3 clinical factors were selected to build the models. In the validation cohort, the combined model showed the best predictive power, with an area under the curve value of 0.785 (95% confidence interval [CI] 0.662-0.909), compared to the ultrasomics model of 0.720 (95% CI 0.576-0.864) and the clinical model of 0.665 (95% CI 0.537-0.793). Decision curve analysis suggested that the combined model was clinically useful, with a corresponding net benefit of 0.760 compared to the other two models. Conclusion We presented an ultrasomics-clinical model based on multiphase CEUS imaging and clinical factors, which showed potential value for the preoperative discrimination of HCC pathological grades.

Objective To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning. Materials and Methods This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics—high-throughput quantitative data from ultrasound imaging of liver fibrosis—were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-learning algorithms by calculating the area under the receiver-operator characteristic curve (AUC). Results ORF and CEMF features had better predictive power than conventional radiomics for liver fibrosis stage (both p < 0.01). CEMF features exhibited the highest diagnostic value for activity stage (both p < 0.05), and ORF had the best diagnostic value for steatosis stage (both p < 0.01). The machine-learning classifiers of adaptive boosting, random forest and support vector machine were found to be optimal algorithms with better (all mean AUCs = 0.85) and more stable performance (coefficient of variation = 0.01–0.02) for fibrosis staging than decision tree, logistic regression and neural network (mean AUC = 0.61–0.72, CV = 0.07–0.08). The multiparametric ultrasomics model achieved much better performance (mean AUC values of 0.78–0.85) than the features from a single modality in discriminating significant fibrosis (≥ F2). Conclusion Machine-learning-based analysis of multiparametric ultrasomics can help improve the discrimination of significant fibrosis compared with mono or dual modalities. Key Points • Multiparametric ultrasomics has achieved much better performance in the discrimination of significant fibrosis (≥ F2) than the single modality of conventional radiomics, original radiofrequency and contrast-enhanced micro-flow. • Adaptive boosting, random forest and support vector machine are the optimal algorithms for machine learning. Electronic supplementary material The online version of this article (10.1007/s00330-018-5680-z) contains supplementary material, which is available to authorized users.

Microwave-enhanced degradation is an effective method to remove organic contaminants from aqueous solutions. In this study, we prepared a novel iron sulfide/talc composite. Under microwave irradiation, the hydroxyl and sulfate radicals are rapidly released from the as-prepared FeSx/talc composite (TF), which could remove 106.8 mg/(min·g) of 2,4,6-TCP in 4 min. The maximum chemical oxygen demand (COD) removal was more than 90.1% when the concentration of TF is 1 g/L and the irradiation time of microwave radiation was 5 min, significantly higher than that treated by FeSx particles or talc. It is attributed to the synergetic effect between talc layers and FeSx particles, which greatly enhances the catalytic degradation performance of the composite for organic pollutants. With the advantages of low cost, high physico-chemical stability, and high reusability, this novel FeSx/talc composite could be a potential treating material for rapidly remove organic pollutants from wastewater.