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Objectives: To investigate the effects of slice thickness on CT radiomics features and models for staging liver fibrosis. Methods: A total of 108 pathologically confirmed liver fibrosis patients from a single center were retrospectively collected and divided into different groups. Both thick (5- or 7-mm) and thin slices (1.3- or 2-mm) were analyzed. A fivefold cross-validation with 100 repeats was conducted. The minimum redundancy–maximum relevance algorithm was used to reduce the radiomics features, and the top 10 ranking features were included for further analysis for each loop. The random forest was used for model establishment. The models with median AUC were selected for the assessment of the discriminative performance for both datasets. Mutual features selected by the models with AUC > 0.8 were searched and considered as the most predictive ones. Results: A total of 162 and 643 radiomics features with excellent reliability were selected from thick- and thin-slice datasets, respectively. The overall discriminative performance of the 500 AUCs from the thin-slice dataset was better than the thick slice. The median AUC values of the thick-sliced datasets were significantly lower than those of the thin-sliced datasets (0.78 and 0.90 for differentiating F1 vs. F2–4, 0.72 and 0.85 for differentiating F1–2 vs. F3–4, both P = 0.03). For differentiating F1–3 vs. F4, no significant difference was found (0.85 vs 0.94, P = 0.15). Six mutual predictive features across all the datasets were found. Conclusions: The radiomics features extracted from thin-slice images and their corresponding models were better and more stable for staging liver fibrosis. [ABSTRACT FROM AUTHOR]

Ag@FeNi-MOF/PVDF multilayer polymer nanocomposites with high dielectric properties were designed and prepared in this paper, and their theoretical simulation was carried out through COMSOL Multiphysics 5.4, which verified the advantages of multilayer structure design. Ag@FeNi-MOF two-dimensional hybrid nanosheets were prepared by hydrothermal reaction in the experiment, which were doped into PVDF matrix as fillers to obtain monolayer films, and then combined with pure PVDF film by hot pressing to achieve multilayer structure. The results showed that the multilayer structure could effectively improve the energy storage density and breakdown field strength. When the number of film layers were 1, 3, 5 and 7, the energy storage density were 8.01, 8.482, 8.822 and 9.467 J/cm3, and the breakdown field strength were 2239, 2500, 2700 and 2850 kV/cm. Compared with layer 1, the energy storage density of layer 7 increased by 18.1%, and the breakdown field strength increased by 27.3%. This study proved that the multilayer structure design had positive significance in improving the dielectric properties of polymer nanocomposites. [ABSTRACT FROM AUTHOR]

Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5+ DM-ILD) is a life-threatening disease. This study aimed to develop a novel pulmonary CT visual scoring method for assessing the prognosis of the disease, and an artificial intelligence (AI) algorithm-based analysis and an idiopathic pulmonary fibrosis (IPF)-based scoring were conducted as comparators. A retrospective cohort of hospitalized patients with MDA5+ DM-ILD was analyzed. Since most fatalities occur within the first half year of the disease course, the primary outcome was the six-month all-cause mortality since the time of admission. A ground glass opacity (GGO) and consolidation-weighted CT visual scoring model for MDA5+ DM-ILD, namely 'MDA5 score', was then developed with C-index values of 0.80 (95%CI 0.75–0.86) in the derivation dataset (n = 116) and 0.84 (95%CI 0.71–0.97) in the validation dataset (n = 57), respectively. While, the AI algorithm-based analysis, namely 'AI score', yielded C-index 0.78 (95%CI 0.72–0.84) for the derivation dataset and 0.77 (95%CI 0.64–0.90) for the validation dataset. These findings suggest that the newly derived 'MDA5 score' may serve as an applicable prognostic predictor for MDA5+ DM-ILD and facilitate further clinical trial design. The AI based CT quantitative analysis provided a promising solution for ILD evaluation. [ABSTRACT FROM AUTHOR]