Matrine induces hepatocellular carcinoma apoptosis and represses EMT and stemness through microRNA-299-3p/PGAM1 axis.

This study explored the impacts of matrine on hepatocellular carcinoma (HCC) cell growth, metastasis, epithelial-mesenchymal transition (EMT), and stemness through regulating the microRNA (miR)-299-3p/phosphoglycerate mutase 1 (PGAM1) axis. The association between miR-299-3p expression with the prognosis of HCC patients was studied. miR-299-3p and PGAM1 sequences were transfected into matrine-treated HCC cells, and cell proliferation, invasion, apoptosis, and stemness were detected, as well as protein expression of EMT- and stemness-related makers. The targeting relationship between miR-299-3p and PGAM1 was identified. Matrine elevated miR-299-3p expression, repressed proliferation, invasion, and anti-apoptosis of HCC cells, and constrained EMT and stemness in vitro. PGAM1 was a target of miR-299-3p. Repression of PGAM1 rescued the effects of miR-299-3p downregulation on HCC cells. Matrine stimulates HCC cell apoptosis and represses the process of EMT and stemness through the miR-299-3p/PGAM1 axis.

Discrete Memristor and Discrete Memristive Systems.

In this paper, we investigate the mathematical models of discrete memristors based on Caputo fractional difference and G-L fractional difference. Specifically, the integer-order discrete memristor is a special model of those two cases. The "∞"-type hysteresis loop curves are observed when input is the bipolar periodic signal. Meanwhile, numerical analysis results show that the area of hysteresis decreases with the increase of frequency of input signal and the decrease of derivative order. Moreover, the memory effect, characteristics and physical realization of the discrete memristors are discussed, and a discrete memristor with short memory effects is designed. Furthermore, discrete memristive systems are designed by introducing the fractional-order discrete memristor and integer-order discrete memristor to the Sine map. Chaos is found in the systems, and complexity of the systems is controlled by the parameter of the memristor. Finally, FPGA digital circuit implementation is carried out for the integer-order and fractional-order discrete memristor and discrete memristive systems, which shows the potential application value of the discrete memristor in the engineering application field.

A hyperchaotic cycloid map with attractor topology sensitive to system parameters.

We propose herein a novel discrete hyperchaotic map based on the mathematical model of a cycloid, which produces multistability and infinite equilibrium points. Numerical analysis is carried out by means of attractors, bifurcation diagrams, Lyapunov exponents, and spectral entropy complexity. Experimental results show that this cycloid map has rich dynamical characteristics including hyperchaos, various bifurcation types, and high complexity. Furthermore, the attractor topology of this map is extremely sensitive to the parameters of the map. The x--y plane of the attractor produces diverse shapes with the variation of parameters, and both the x--z and y--z planes produce a full map with good ergodicity. Moreover, the cycloid map has good resistance to parameter estimation, and digital signal processing implementation confirms its feasibility in digital circuits, indicating that the cycloid map may be used in potential applications.

Recent Advances in Fluorescence Imaging of Traumatic Brain Injury in Animal Models.

Traumatic brain injury (TBI) is one of the top three specific neurological disorders, requiring reliable, rapid, and sensitive imaging of brain vessels, tissues, and cells for effective diagnosis and treatment. Although the use of medical imaging such as computed tomography (CT) and magnetic resonance imaging (MRI) for the TBI detection is well established, the exploration of novel TBI imaging techniques is of great interest. In this review, recent advances in fluorescence imaging for the diagnosis and evaluation of TBI are summarized and discussed in three sections: imaging of cerebral vessels, imaging of brain tissues and cells, and imaging of TBI-related biomarkers. Design strategies for probes and labels used in TBI fluorescence imaging are also described in detail to inspire broader applications. Moreover, the multimodal TBI imaging platforms combining MRI and fluorescence imaging are also briefly introduced. It is hoped that this review will promote more studies on TBI fluorescence imaging, and enable its use for clinical diagnosis as early as possible, helping TBI patients get better treatment and rehabilitation.

Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain-Computer Interface Technology.

In recent years, brain-computer interface (BCI) is expected to solve the physiological and psychological needs of patients with motor dysfunction with great individual differences. However, the classification method based on feature extraction requires a lot of prior knowledge when extracting data features and lacks a good measurement standard, which makes the development of BCI. In particular, the development of a multi-classification brain-computer interface is facing a bottleneck. To avoid the blindness and complexity of electroencephalogram (EEG) feature extraction, the deep learning method is applied to the automatic feature extraction of EEG signals. It is necessary to design a classification model with strong robustness and high accuracy for EEG signals. Based on the research and implementation of a BCI system based on a convolutional neural network, this article aims to design a brain-computer interface system that can automatically extract features of EEG signals and classify EEG signals accurately. It can avoid the blindness and time-consuming problems caused by the machine learning method based on feature extraction of EEG data due to the lack of a large amount of prior knowledge.