The Value of Convolutional Neural Network-Based Magnetic Resonance Imaging Image Segmentation Algorithm to Guide Targeted Controlled Release of Doxorubicin Nanopreparation.

There was an investigation of the auxiliary role of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image segmentation algorithm in MRI image-guided targeted drug therapy of doxorubicin nanomaterials so that the value of drug-controlled release in liver cancer patients was evaluated. In this study, 80 patients with liver cancer were selected as the research objects. It was hoped that the CNN-based MRI image segmentation algorithm could be applied to the guided analysis of MRI images of the targeted controlled release of doxorubicin nanopreparation to analyze the imaging analysis effect of this algorithm on the targeted treatment of liver cancer with doxorubicin nanopreparation. The results of this study showed that the upgraded three-dimensional (3D) CNN-based MRI image segmentation had a better effect compared with the traditional CNN-based MRI image segmentation, with significant improvement in indicators such as accuracy, precision, sensitivity, and specificity, and the differences were all statistically marked (p < 0.05). In the monitoring of the targeted drug therapy of doxorubicin nanopreparation for liver cancer patients, it was found that the MRI images of liver cancer patients processed by 3D CNN-based MRI image segmentation neural algorithm could be observed more intuitively and guided to accurately reach the target of liver cancer. The accuracy of targeted release determination of nanopreparation reached 80 ± 6.25%, which was higher markedly than that of the control group (66.6 ± 5.32%) (p < 0.05). In a word, the MRI image segmentation algorithm based on CNN had good application potential in guiding patients with liver cancer for targeted therapy with doxorubicin nanopreparation, which was worth promoting in the adjuvant treatment of targeted drugs for cancer.

Zearalenone Removal from Corn Oil by an Enzymatic Strategy.

The estrogen-like mycotoxin zearalenone (ZEN) is one of the most widely distributed contaminants especially in maize and its commodities, such as corn oil. ZEN degrading enzymes possess the potential for counteracting the negative effect of ZEN and its associated high safety risk in corn oil. Herein, we targeted enhancing the secretion of ZEN degrading enzyme by Pichia pastoris through constructing an expression plasmid containing three optimized expression cassettes of zlhy-6 codon and signal peptides. Further, we explored various parameters of enzymatic detoxification in neutralized oil and analyzed tocopherols and sterols losses in the corn oil. In addition, the distribution of degraded products was demonstrated as well by Agilent 6510 Quadrupole Time-of-Flight mass spectrometry. P. pastoris GSZ with the glucoamylase signal was observed with the highest ZLHY-6 secretion yield of 0.39 mg/mL. During the refining of corn oil, ZEN in the crude oil was reduced from 1257.3 to 13 µg/kg (3.69% residual) after neutralization and enzymatic detoxification. Compared with the neutralized oil, no significant difference in the total tocopherols and sterols contents was detected after enzymatic detoxification. Finally, the degraded products were found to be entirely eliminated by washing. This study presents an enzymatic strategy for efficient and safe ZEN removal with relatively low nutrient loss, which provides an important basis for further application of enzymatic ZEN elimination in the industrial process of corn oil production.