BackgroundBilirubin encephalopathy (BE) is a severe neurologic sequelae induced by hyperbilirubinemia in newborns. However, the pathogenetic mechanisms underlying the clinical syndromes of BE remain ambiguous. Ex vivo1H nuclear magnetic resonance (NMR) spectroscopy was used to measure changes in the concentrations of cerebral metabolites in various brain areas of newborn 9-day-old rats subjected to bilirubin to explore the related mechanisms of BE.ResultsWhen measured 0.5 hr after injection of bilirubin, levels of the amino acid neurotransmitters glutamate (Glu), glutamine (Gln), and γ-aminobutyric acid (GABA) in hippocampus and occipital cortex significantly decreased, by contrast, levels of aspartate (Asp) considerably increased. In the cerebellum, Glu and Gln levels significantly decreased, while GABA, and Asp levels showed no significant differences. In BE 24 hr rats, all of the metabolic changes observed returned to normal in the hippocampus and occipital cortex; however, levels of Glu, Gln, GABA, and glycine significantly increased in the cerebellum.ConclusionsThese metabolic changes for the neurotransmitters are mostly likely the result of a shift in the steady-state equilibrium of the Gln-Glu-GABA metabolic cycle between astrocytes and neurons, in a region-specific manner. Changes in energy metabolism and the tricarboxylic acid cycle may also be involved in the pathogenesis of BE.

BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

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Chinese herbal medicine is an essential part of traditional Chinese medicine and herbalism, and has important significance in the treatment combined with modern medicine. The correct use of Chinese herbal medicine, including identification and classification, is crucial to the life safety of patients. Recently, deep learning has achieved advanced performance in image classification, and researchers have applied this technology to carry out classification work on traditional Chinese medicine and its products. Therefore, this paper uses the improved ConvNeXt network to extract features and classify traditional Chinese medicine. Its structure is to fuse ConvNeXt with ACMix network to improve the performance of ConvNeXt feature extraction. Through using data processing and data augmentation techniques, the sample size is indirectly expanded, the generalization ability is enhanced, and the feature extraction ability is improved. A traditional Chinese medicine classification model is established, and the good recognition results are achieved. Finally, the effectiveness of traditional Chinese medicine identification is verified through the established classification model, and different depth of network models are compared to improve the efficiency and accuracy of the model.

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