Objectives We aimed to develop radiomics with attribute bagging, which leverages multimodal ultrasound (US) images to improve the classification accuracy of breast tumors. Methods A retrospective study was conducted. B‐mode US, shear wave elastographic, and contrast‐enhanced US images of 178 patients with 181 tumors (67 malignant and 114 benign) were included. Radiomics with attribute bagging consisted of extraction of 1226 radiomic features and analysis of them with attribute bagging. Histologic examination results acted as the reference standard. Radiomics with several feature selection algorithms were used for comparison. Cross‐validation and a holdout test were performed to evaluate their performances. Results The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of radiomics with attribute bagging with the multimodal US images were 84.12%, 92.86%, 78.80%, and 0.919, respectively, exceeding all the comparison methods. Conclusions Radiomics with attribute bagging combined with multimodal US images has the potential to be used for accurate diagnosis of breast tumors in the clinic.

Electrocardiogram (ECG) signals contain a great deal of essential information which can be utilized by physicians for the diagnosis of heart diseases. Unfortunately, ECG signals are inevitably corrupted by noise which will severely affect the accuracy of cardiovascular disease diagnosis. Existing ECG signal denoising methods based on wavelet shrinkage, empirical mode decomposition and nonlocal means (NLM) cannot provide sufficient noise reduction or well-detailed preservation, especially with high noise corruption. To address this problem, we have proposed a hybrid ECG signal denoising scheme by combining extreme-point symmetric mode decomposition (ESMD) with NLM. In the proposed method, the noisy ECG signals will first be decomposed into several intrinsic mode functions (IMFs) and adaptive global mean using ESMD. Then, the first several IMFs will be filtered by the NLM method according to the frequency of IMFs while the QRS complex detected from these IMFs as the dominant feature of the ECG signal and the remaining IMFs will be left unprocessed. The denoised IMFs and unprocessed IMFs are combined to produce the final denoised ECG signals. Experiments on both simulated ECG signals and real ECG signals from the MIT-BIH database demonstrate that the proposed method can suppress noise in ECG signals effectively while preserving the details very well, and it outperforms several state-of-the-art ECG signal denoising methods in terms of signal-to-noise ratio (SNR), root mean squared error (RMSE), percent root mean square difference (PRD) and mean opinion score (MOS) error index.

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HOXC cluster antisense RNA 3 (HOXC-AS3) is a long noncoding RNA (lncRNA) that plays a crucial role in various tumors; nevertheless, its role in glioma and its mechanism have not been completely elucidated. In this research, we discovered that HOXC-AS3 was over-expression in glioma cells and tissues and was associated with prognosis. Next, we determined that HOXC-AS3 targeted miR-216 as a sponge and that the F11 receptor (F11R) was the target of miR-216 by online databases analysis, qRT–PCR, and luciferase reporter assay. In addition, the rescue experiments confirmed that HOXC-AS3 regulated the expression of F11R by competitively binding miR-216 and functioning as a competing endogenous RNA (ceRNA). The intracranial glioblastoma mouse model suggested that HOXC-AS3 could promote glioma malignant progression in vivo. In summary, our study shows that the HOXC-AS3/miR-216/F11R axis plays an important role in the malignant progression of glioma, and may provide new ideas for the treatment of glioma.