Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h ^-1 on 11 patients from the public CHB-MIT scalp EEG dataset.

Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the “correlation’s correlation” strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.

Poor bone growth remains a challenge for degradable bone implants. Montmorillonite and strontium were selected as the carrier and bone growth promoting elements to prepare strontium-doped montmorillonite coating on Mg–Ca alloy. The surface morphology and composition were characterized by SEM, EDS, XPS, FT-IR and XRD. The hydrogen evolution experiment and electrochemical test results showed that the Mg–Ca alloy coated with Sr-MMT coating possessed optimal corrosion resistance performance. Furthermore, in vitro studies on cell activity, ALP activity, and cell morphology confirmed that Sr-MMT coating had satisfactory biocompatibility, which can significantly avail the proliferation, differentiation, and adhesion of osteoblasts. Moreover, the results of the 90-day implantation experiment in rats indicated that, the preparation of Sr-MMT coating effectively advanced the biocompatibility and bone repair performance of Mg–Ca alloy. In addition, The Osteogenic ability of Sr-MMT coating may be due to the combined effect of the precipitation of Si⁴⁺ and Sr²⁺ in Sr-MMT coating and the dissolution of Mg²⁺ and Ca²⁺ during the degradation of Mg–Ca alloy. By using coating technology, this study provides a late-model strategy for biodegradable Mg alloys with good corrosion resistance, biocompatibility. This new material will bring more possibilities in bone repair.

(1) Background: Traditional dressings can only superficially cover the wound, they have widespread issues with inadequate bacterial isolation and liquid absorption, and it is simple to inflict secondary wound injury when changing dressings. Therefore, it is crucial for wound healing to develop a new kind of antimicrobial colloidal dressing with good antibacterial, hygroscopic, and biocompatible qualities. (2) Methods: Ag-montmorillonite/chitosan (Ag-MMT/CS) colloid, a new type of antibacterial material, was prepared from two eco-friendly materials—namely, montmorillonite and chitosan—as auxiliary materials, wherein these materials were mixed with the natural metal Ag, which is an antibacterial agent. The optimum preparation technology was explored, and Ag-MMT/CS was characterized. Next, Staphylococcus aureus, which is a common skin infection bacterium, was considered as the experimental strain, and the in vitro antibacterial activity and cytocompatibility of the Ag-MMT/CS colloid were investigated through various experiments. Subsequently, a rat skin infection model was established to explore the in vivo antibacterial effect. (3) Results: In vitro studies revealed that the Ag-MMT/CS colloid had a good antibacterial effect on S. aureus, with an inhibition zone diameter of 18 mm and an antibacterial rate of 99.18%. After co-culture with cells for 24 h and 72 h, the cell survival rates were 88% and 94%, respectively. The cells showed normal growth and proliferation, and no evident dead cells were observed under the laser confocal microscope. After applying the colloid to the rat skin infection model, the Ag-MMT/CS treatment group exhibited faster wound healing and better local exudation and absorption in the wound than the control group, suggesting that the Ag-MMT/CS colloid exhibited a better antibacterial effect on the S. aureus. (4) Conclusions: Ag⁺, chitosan, and MMT present in the Ag-MMT/CS colloid dressing exert synergistic effects, and it has good antibacterial effects, cytocompatibility, and hygroscopicity, indicating that this colloid has the potential to become a next-generation clinical antibacterial dressing.

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.