Dysregulated Long Non-coding RNAs in Myasthenia Gravis- A Mini-Review.

Myasthenia gravis (MG) is an acquired autoimmune disease that is mediated by humoral immunity, supplemented by cellular immunity, along with participation of the complement system. The pathogenesis of MG is complex; although autoimmune dysfunction is clearly implicated, the specific mechanism remains unclear. Long non-coding RNAs (lncRNAs) are a class of non-coding RNA molecules with lengths greater than 200 nucleotides, with increasing evidence of their rich biological functions and high-level structure conservation. LncRNAs can directly interact with proteins and microRNAs to regulate the expression of target genes at the transcription and post-transcription levels. In recent years, emerging studies have suggested that lncRNAs play roles in the differentiation of immune cells, secretion of immune factors, and complement production in the human body. This suggests the involvement of lncRNAs in the occurrence and progression of MG through various mechanisms. In addition, the differentially expressed lncRNAs in peripheral biofluid may be used as a biomarker to diagnose MG and evaluate its prognosis. Moreover, with the development of lncRNA expression regulation technology, it is possible to regulate the differentiation of immune cells and influence the immune response by regulating the expression of lncRNAs, which will provide a potential therapeutic option for MG. Here, we review the research progress on the role of lncRNAs in different pathophysiological events contributing to MG, focusing on specific lncRNAs that may largely contribute to the pathophysiology of MG, which could be used as potential diagnostic biomarkers and therapeutic targets.

"Sweat-Driven" MXene Composites with Energy-Storage and Thermal-Management Multifunctions: A Platform for Versatile Electronic Skins.

Most exogenous electronic skins (e-skins) currently face challenges of complex structure and poor compatibility with the human body. Utilizing human secretions (e.g., sweat) to develop e-skins is an effective solution strategy. Here, a new kind of "sweat-driven" e-skin is proposed, which realizes energy-storage and thermal-management multifunctions. Through the layer-by-layer assembly of MXene-carbon nanotube (CNT) composite with paper, lightweight and versatile e-skins based on supercapacitors and actuators are fabricated. Long CNTs wrap and entangle MXene nanosheets, enhancing their long-distance conductivity. Furthermore, the CNT network overcomes the structural collapse of MXene in sweat, improving the energy-storage performance of e-skin. The "sweat-driven" all-in-one supercapacitor with a trilayer structure is patternable, which absorbs sweat as electrolyte and harnesses the ions therein to store energy, exhibiting an areal capacitance of 282.3 mF cm-2 and a high power density (2117.8 µW cm-2 ). The "sweat-driven" actuator with a bilayer structure can be driven by moisture (bending curvature of 0.9 cm-1 ) and sweat for personal thermal management. Therefore, the paper serves as a separator, actuating layer, patternable layer, sweat extractor, and reservoir. The "sweat-driven" MXene-CNT composite provides a platform for versatile e-skins, which achieve the interaction with humans and offer insights into the development of multifunctional wearable electronics.

A Review of Traditional Chinese Medicine, Buyang Huanwu Decoction for the Treatment of Cerebral Small Vessel Disease.

Cerebral small vessel disease (CSVD) is often referred to as "collaterals disease" in traditional Chinese medicine (TCM), and commonly includes ischemic and hemorrhagic CSVD. TCM has a long history of treating CSVD and has demonstrated unique efficacy. Buyang Huanwu Decoction (BHD) is a classical TCM formula that has been used for the prevention and treatment of stroke for hundreds of years. BHD exerts its therapeutic effects on CSVD through a variety of mechanisms. In this review, the clinical and animal studies on BHD and CSVD were systematically introduced. In addition, the pharmacological mechanisms, active components, and clinical applications of BHD in the treatment of CSVD were reviewed. We believe that an in-depth understanding of BHD, its pharmacological mechanism, disease-drug interaction, and other aspects will help in laying the foundation for its development as a new therapeutic strategy for the treatment of CSVD.

Low temperature mitigating the paclitaxel-induced damages in mouse cell and hair follicle model.

Scalp cooling is currently the most approved treatment to prevent alopecia due to chemotherapy for cancer. Few reports can be available, and the mechanisms involved in scalp cooling were unclear. The present work tries to reveal a preliminary inhibitory mechanism of scalp cooling on paclitaxel-induced alopecia. The results found that low temperature enhanced local vasoconstriction to 0.32 (dimensionless diameter, @ 22 °C) and the vascular diameter presented an oscillating attenuation, which led to the concentration reduction of chemotherapeutic drugs transported by blood flow. Cooling significantly rescued M-HFK cells treated by paclitaxel, and the lower temperature for the better protection due to weakening the cytotoxicity in some extent. Cell cycle results showed that the G0/G1 phase was arrested at low temperature (i.e. 22 °C), which was beneficial to mitigate the effect of paclitaxel on the G2/M phase cycle and finally made the cell cycle return to normal. Also, cold stimulation significantly increased the concentration of HSP70 more than 3 times (@22 °C for 2h) compared with that of the control group, which means low temperature can protect cells from stresses. Furtherly, Cooling reduced the number of PH3+ and Caspase-3+ cells in the hair follicle, and effectively inhibited the cell apoptosis in the vitro hair follicle and alopecia in the mice experiments. The current work provides a basis for deeply understanding the chemotherapy-induced alopecia prevention with scalp cooling.

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids.

The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.

Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data.

Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from "layer" to "block" according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection.

A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data.

With the growth of the national economy, there is increasing demand for electricity, which forces transmission line corridors to become structurally complicated and extend to complex environments (e.g., mountains, forests). It is a great challenge to inspect transmission line in these regions. To address these difficulties, a novel method of autonomous inspection for transmission line is proposed based on cable inspection robot (CIR) LiDAR data, which mainly includes two steps: preliminary inspection and autonomous inspection. In preliminary inspection, the position and orientation system (POS) data is used for original point cloud dividing, ground point filtering, and structured partition. A hierarchical classification strategy is established to identify the classes and positions of the abnormal points. In autonomous inspection, CIR can autonomously reach the specified points through inspection planning. These inspection targets are imaged with PTZ (pan, tilt, zoom) cameras by coordinate transformation. The feasibility and effectiveness of the proposed method are verified by test site experiments and actual line experiments, respectively. The proposed method greatly reduces manpower and improves inspection accuracy, providing a theoretical basis for intelligent inspection of transmission lines in the future.