Lysozyme amyloid fibril-chitosan double network hydrogel: Preparation, characterization, and application on inhibition of Nε-(carboxyethyl)lysine.

Nε-(carboxyethyl)lysine (CML), a typical advanced glycosylation end product produced during the processing of meat under high temperature, poses health risks. Active substances like polyphenols are known to inhibit the formation of harmful products during the processing of food. In this study, our objective was to prepare a double network hydrogel (DN) loaded with gallic acid using amyloid fibers and chitosan as a rigid and flexible network, respectively. The network as well as the interactions between the two networks were observed and analyzed. Chitosan concentration was the key factor regulating the structure and properties of the DN. At a chitosan concentration of 0.7%wt, the structure of DN became dense and its mechanical properties were improved, with the loading capacity and loading efficiency being increased by 143.79 % and 128.21 %, compared with those of amyloid fibril alone. Furthermore, the digestibility of gallic acid in simulated intestinal fluid was increased by 215.10 %. Moreover, adding DN to the beef patties effectively inhibited the formation of CML in a dose-response dependent manner. Addition of 3 wt% DN resulted in the inhibitory rate of CML in roast beef patties reaching a high 73.09 %. The quality and palatability of beef patties were improved. These findings suggest that DN shows great potential as an application that may be utilized to deliver active substances aimed at inhibiting CML in the meat processing industry.

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.

BACKGROUND: There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen. METHODS: We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method. RESULTS: All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979. CONCLUSIONS: The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.