This study aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted imaging (T2WI) MRI images from 167 early-stage cervical cancer patients (January 2014 - August 2018) were used to train and validate deep learning models. Predictive performances were evaluated using receiver operating characteristic (ROC) curve and confusion matrix analysis, with the DCE-T1 showing more discriminative results than T2WI MRI. By adopting an attention ensemble learning strategy that integrates both MRI sequences, the highest average area was obtained under the ROC curve (AUC) of 0.911 (Sensitivity = 0.881 and Specificity = 0.752). The superior performances in this study, when compared to existing radiomic methods, indicate that a wealth of deep learning-based radiomics could be developed to aid radiologists in preoperatively predicting vessel invasion in cervical cancer patients.

Background: Emotion recognition using EEG signals enables clinicians to assess patients’ emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.Methods: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.Results: Experiments’ results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.Discussion: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

Mg alloys have attracted extensive attention in the biomedical fields owing to their great biocompatibility, suitable mechanical properties, and biodegradability, etc. However, the fast degradation rate restricts the application of Mg alloys. Thus, the surface treatment of Mg alloys is considered as one of the most effective ways to enhance the corrosion resistance of Mg alloys. Nevertheless, simple processing to improve the corrosion resistance can no longer meet the growing biofunctional clinical requirements. Therefore, functionalized processing has become one of the key development directions for surface treatment in the future, such as functionalized Mg alloys with antibacterial property and hydrophobicity. There are few papers that review the functionalized processing of surface treatment. This review summarized and compared the major advances of the surface treatment (anticorrosion processing and functionalized processing) of Mg alloys. Then, some potential research suggestions are proposed, which may provide a reference for the development of Mg alloys.

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to detect abnormal ECG effectively. Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, and outlier detector). The ECG-AAE framework is trained only with normal ECG data. Normal ECG signals could be mapped into latent feature space and then reconstructed as the original ECG signal back in our model, while abnormal ECG signals could not. Here, the TCN is employed to extract features of normal ECG data. Then, our model is evaluated on an MIT-BIH arrhythmia dataset and CMUH dataset, with an accuracy, precision, recall, F1-score, and AUC of 0.9673, 0.9854, 0.9486, 0.9666, and 0.9672 and of 0.9358, 0.9816, 0.8882, 0.9325, and 0.9358, respectively. The result indicates that the ECG-AAE can detect abnormal ECG efficiently, with its performance better than other popular outlier detection methods.

The charge-reversal nano-drug delivery system (CRNDDS) is a promising system for delivering chemotherapy drugs and has gained widespread application in cancer treatment. In this review, we summarize the recent advancements in CRNDDSs in terms of cancer treatment. We also delve into the charge-reversal mechanism of the CRNDDSs, focusing on the acid-responsive, redox-responsive, and enzyme-responsive mechanisms. This study elucidates how these systems undergo charge transitions in response to specific microenvironmental stimuli commonly found in tumor tissues. Furthermore, this review explores the pivotal role of CRNDDSs in tumor diagnosis and treatment, and their potential limitations. By leveraging the unique physiological characteristics of tumors, such as the acidic pH, specific redox potential, and specific enzyme activity, these systems demonstrate enhanced accumulation and penetration at tumor sites, resulting in improved therapeutic efficacy and diagnostic accuracy. The implications of this review highlight the potential of charge-reversal drug delivery systems as a novel and targeted strategy for cancer therapy and diagnosis.