Highlights d MscL-G22S expression efficiently sensitized cells to ultrasound stimulation d Non-invasive ultrasound triggered neural activation in MscLexpressing regions d Ultrasound targeted at the cortical M1 region with MscL evoked rapid EMG responses d Ultrasound successfully activated MscL-expressing neurons in the deeper DMS region
PurposeMYCN amplification plays a critical role in defining high-risk subgroup of patients with neuroblastoma. We aimed to develop and validate the CT-based machine learning models for predicting MYCN amplification in pediatric abdominal neuroblastoma.MethodsA total of 172 patients with MYCN amplified (n = 47) and non-amplified (n = 125) were enrolled. The cohort was randomly stratified sampling into training and testing groups. Clinicopathological parameters and radiographic features were selected to construct the clinical predictive model. The regions of interest (ROIs) were segmented on three-phrase CT images to extract first-, second- and higher-order radiomics features. The ICCs, mRMR and LASSO methods were used for dimensionality reduction. The selected features from the training group were used to establish radiomics models using Logistic regression, Support Vector Machine (SVM), Bayes and Random Forest methods. The performance of four different radiomics models was evaluated according to the area under the receiver operator characteristic (ROC) curve (AUC), and then compared by Delong test. The nomogram incorporated of clinicopathological parameters, radiographic features and radiomics signature was developed through multivariate logistic regression. Finally, the predictive performance of the clinical model, radiomics models, and nomogram was evaluated in both training and testing groups.ResultsIn total, 1,218 radiomics features were extracted from the ROIs on three-phrase CT images, and then 14 optimal features, including one original first-order feature and eight wavelet-transformed features and five LoG-transformed features, were identified and selected to construct the radiomics models. In the training group, the AUC of the Logistic, SVM, Bayes and Random Forest model was 0.940, 0.940, 0.780 and 0.927, respectively, and the corresponding AUC in the testing group was 0.909, 0.909, 0.729, 0.851, respectively. There was no significant difference among the Logistic, SVM and Random Forest model, but all better than the Bayes model (p <0.005). The predictive performance of the Logistic radiomics model based on three-phrase is similar to nomogram, but both better than the clinical model and radiomics model based on single venous phase.ConclusionThe CT-based radiomics signature is able to predict MYCN amplification of pediatric abdominal NB with high accuracy based on SVM, Logistic and Random Forest classifiers, while Bayes classifier yields lower predictive performance. When combined with clinical and radiographic qualitative features, the clinics-radiomics nomogram can improve the performance of predicting MYCN amplification.
We report an intensive study on negative magnetization under zero-field-cooled (ZFC) mode in YMnCrO polycrystalline samples. It has been found that the magnetization reversal in ZFC measurements is strongly related to a giant coercivity of the oxide. The giant coercivity may result from the cooperative effect of magnetocrystalline anisotropy and the Dzyaloshinsky-Moriya interaction, especially at temperatures below 10 K. By fitting the high-temperature paramagnetic data under nominal zero field, the value of the trapped field in a superconducting magnet has been derived to be around several Oe, which further demonstrates that the negative ZFC magnetization is an artifact caused by negative trapped field in combination with the giant coercivity. Consequently, we suggest that one has to be cautious of trapped field in superconducting magnets in understanding negative ZFC magnetization, especially in "hard" magnetic samples.
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