1. The current experiment was conducted to investigate the effect of zinc sulphate (ZnSO4) and zinc methionine (Zn-Met) on growth and their effect on plasma growth hormone (GH) concentration, growth hormone receptor (GHR) and insulin-like growth factor I (IGF-I) mRNA expression in mice. 2. Ninety male KunMing (KM) mice were randomly divided into three treatments. The control group was fed on a basal diet containing 11.67 mg/kg of zinc. The ZnSO4 group and Zn-Met group were fed on the diets supplemented with ZnSO4 or Zn-Met at 30 mg/kg (containing zinc of 40.05 and 40.75 mg/kg, respectively). The mice were offered the test diets for 10 days. Weight gains and food intake were measured at the end of the experiment, zinc contents in liver and serum were determined using atomic absorption spectrophotometry; GH was determined by radioimmunoassay, the levels of GHR and IGF-I mRNA were determined with reverse transcript polymerase chain reaction. 3. Both ZnSO4 and Zn-Met enhanced weight gain and food intake in the mice, Zn-Met improved the growth and food intake more effectively than ZnSO4 did (P < 0.05). The both forms of zinc had no effect on GH and the level of GHR mRNA expression (P > 0.05) and they up-regulated the expression of IGF-I mRNA (P < 0.05). As compared to ZnSO4, Zn-Met enhanced the level of IGF-I mRNA significantly (P < 0.05). 4. Both ZnSO4 and Zn-Met had no effect on plasma GH and the expression of GHR mRNA, but they enhanced the expression of IGF-I mRNA. Zinc methionine enhanced the weight gain and up-regulated IGF-I mRNA expression more effectively than ZnSO4.

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OBJECTIVE The aim of this study was to build a convolutional neural network (CNN)–based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features. METHODS In total, 222 GBM patients were included in the training set from Sun Yat-sen University Cancer Center (SYSUCC) and 107 GBM patients were included in the validation set from SYSUCC, Xuanwu Hospital Capital Medical University, and the First Hospital of Jilin University. The multimodal model was trained with MR images (pre- and postcontrast T1-weighted images and T2-weighted images), corresponding MRI impression, and clinical patient information. First, the original images were segmented using the Multimodal Brain Tumor Image Segmentation Benchmark toolkit. Convolutional features were extracted using 3D residual deep neural network (ResNet50) and convolutional 3D (C3D). Radiomic features were extracted using pyradiomics. Report texts were converted to word embedding using word2vec. These three types of features were then integrated to train neural networks. Accuracy, precision, recall, and F1-score were used to evaluate the model performance. RESULTS The C3D-based model yielded the highest accuracy of 91.11% in the prediction of IDH1 mutation status. Importantly, the addition of semantics improved precision by 11.21% and recall in MGMT promoter methylation status prediction by 14.28%. The areas under the receiver operating characteristic curves of the C3D-based model in the IDH1, ATRX, MGMT, and 1-year prognosis groups were 0.976, 0.953, 0.955, and 0.976, respectively. In external validation, the C3D-based model showed significant improvement in accuracy in the IDH1, ATRX, MGMT, and 1-year prognosis groups, which were 88.30%, 76.67%, 85.71%, and 85.71%, respectively (compared with 3D ResNet50: 83.51%, 66.67%, 82.14%, and 70.79%, respectively). CONCLUSIONS The authors propose a novel multimodal model integrating C3D, radiomics, and semantics, which had a great performance in predicting IDH1, ATRX, and MGMT molecular subtypes and the 1-year prognosis of GBM.

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