Engineering an enzyme-like catalytic sensor for on-site dual-mode evaluation of total antioxidant capacity.

A novel Fe-MoOx nanozyme, engineered with enhanced peroxidase (POD)-like activity through strategic doping and the creation of oxygen vacancies, is introduced to catalyze the oxidation of TMB with high efficiency. Furthermore, Fe-MoOx is responsive to single electron transfer (SET) and hydrogen atom transfer (HAT) mechanisms related to antioxidants and can serve as a desirable nanozyme for total antioxidant capacity (TAC) determination. The TAC colorimetric platform can reach a low LOD of 0.512 µM in solution and 24.316 µM in the smartphone-mediated RGB hydrogel (AA as the standard). As proof of concept, the practical application in real samples was explored. The work paves a promising avenue to design diverse nanozymes for visual on-site inspection of food quality.

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation.

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.