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BackgroundSystemic lupus erythematosus (SLE) is a systemic autoimmune disease with a remarkable predominance in female, suggesting that steroid hormones may be involved in the pathogenesis. However, steroid signature of SLE patients has not been fully explored.MethodsA metabolic profiling analysis based on gas chromatography/mass spectrometry (GC/MS) with high sensitivity and reproducibility was employed to comprehensively reveal SLE-specific steroid alterations.ResultsMore than 70 kinds of steroids in urine were detected by gas chromatography/mass spectrometry (GC/MS) to reveal SLE-specific steroid alterations. Principle component analysis demonstrated that the steroid profile was obviously distinguished between patients with SLE and controls. A lower level of total androgens was observed in patients, and nine androgens [dehydroepiandrosterone (DHEA), testosterone, Etio, androsterone, βαβ-Diol, Epi-An, Epi-DHT, 16α-OH-DHEA, and A-Diol] underwent significant decrease. Moreover, patients with SLE exhibited a slightly higher level of total estrogens than controls, and three estrogens (17-Epi-E3, 17α-E2, and E3) were remarkably increased. Furthermore, we identified the elevation of two sterols (Lan and Chol), and the reduction of one corticoid (11-DeoxyF) and two progestins (5α-DHP and 11β-OH-Prog) in patients.DiscussionIn this study, metabolic signature of urinary steroids associated with SLE was comprehensively defined by GC/MS for the first time, and steroid metabolism disorders were found in patients with SLE, especially the conversion of androgens to estrogens. Our findings will provide new insights for a deeper understanding of the mechanism of steroid hormones in the pathogenesis of SLE and will help to unravel the reason of sexual disparity in SLE.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, and the dysregulation of lipid metabolism has been found to play an important role in the pathogenesis of RA and is related to the severity and prognosis of patients. Tripterygium wilfordii glycosides (TWG) is extracted from the roots of Tripterygium wilfordii Hook F. with anti-inflammatory and immunosuppressive effects, and numerous clinical trials have supported its efficacy in the treatment of RA. Some evidence suggested that TWG can modulate the formation of lipid mediators in various innate immune cells; however whether it can improve RA-related lipid disorders has not been systematically studied. In the study, type Ⅱ collagen-induced arthritis (CIA) model was used to investigate the efficacy of TWG in the treatment of RA and its effect on lipid metabolism. Paw volume, arthritis score, pathological changes of ankle joint, serum autoantibodies and inflammatory cytokines were detected to assess the therapeutic effect on arthritis in CIA rats. Then, shotgun lipidomics based on multi-dimensional mass spectrometry platform was performed to explore the alterations in serum lipidome caused by TWG. The study showed that TWG could effectively ameliorate arthritis in CIA rats, such as reducing paw volume and arthritis score, alleviating the pathological damages of joint, and preventing the production of anti-CII autoantibodies and IL-1β cytokine. Significant increase in ceramide and decrease in lysophosphatidylcholine were observed in CIA rats, and were highly correlated with arthritis score and IL-1β level. After TWG treatment, these lipid abnormalities can be corrected to a great extent. These data demonstrate that TWG exerts a beneficial therapeutic effect on aberrant lipid metabolism which may provide new insights for further exploring the role and mechanism of TWG in the treatment of RA.

Five carbazole and diketopyrrolopyrrole-based donor-acceptor (D-A) new π-conjugated oligomers (π-COs) with gradually elongated lengths are facilely synthesized via a single pot of direct C–H arylation with merits of atom- and step-economy. The structure-property-performance correlations of these π-COs and their parent polymer are studied in detail by opto-electronic characterizations and bulk heterojunction (BHJ) organic photovoltaic (OPV) devices. It is found that the π-COs having longer lengths enable better performance in OPVs owing to the enhanced intermolecular interaction with the elongation of the conjugations. The above results not only highlight the powerful synthetic strategy here provided, but also reveal that π-COs with unique properties might find promising application in OPVs.

Microstructure functional surface is widely used in an optical system because of its special micro-topological structure and particular physical properties. This paper presents a context-aware autonomously controlling method of micro-dots punching machine tool via establishing the digital twin-driven cyber-physical system. Key enabling techniques on twinning of cyberspace and physical equipment are discussed. A dynamic adjustment model of piezoelectric ceramics for micro-dots punching is presented based on the high-precision online detection and control system. A novel staggered punching approach is proposed for improving the punching speed. A joint optimization model is proposed for coordinating micro-punching system and staggered process. Context-aware autonomous adjusting of the system with errors analysis and compensations in the punching process is realized. Finally, a positioning accuracy of $2~\mu \text{m}$ and a high punching speed of 20-65 dots/s are achieved. This paper is expected to provide a new approach for incorporating smart-enabling techniques in the ultra-precision machining of microstructure arrays.

Glucocorticoids (GCs) are commonly used to treat systemic lupus erythematosus (SLE). Unfortunately, excessive GCs can induce many side effects associated with disordered fatty acid (FA) metabolism. Although an increased level of total FA has been found after GCs treatment, it is not clear whether all FA species increased or only certain FA species were altered. A gas chromatography–mass spectrometry-based FA profiling approach was performed to reveal the alterations of FA species in SLE model mice (MRL/lpr) after treatment with 5 mg/kg of prednisone. The study showed a distinct FA profile in MRL/lpr mice compared to the controls, mainly manifested by elevated polyunsaturated FAs (arachidonate, docosahexaenoate, etc.), which are related to the inflammatory state; and altered (product FA/precursor FA) ratios representing the estimated activities of FA desaturase and elongase (higher activities of multiple elongases, △4 desaturase, △5 desaturase, △6 desaturase, and lower activity of △8 desaturase). Treatment with 5 mg/kg of prednisone decreased the total level of n-6 polyunsaturated FA in MRL/lpr mice; in particular, the level of arachidonate and estimated activity of △5 desaturase were reduced to the control level. Moreover, prednisone induced additional perturbations in FAs, including not only saturated FAs, but also monounsaturated FAs and n-3 polyunsaturated FAs, indicating that there was a strong effect of prednisone on FA metabolism. These results may be valuable for further studies of the side effects of GCs treatment.

As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography (sEMG). Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network (PLCNN) is designed to improve the accuracy and optimize the model with applications in sEMG signal movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization (PSO) is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods.

Microfluidics offer microenvironments for reagent delivery, handling, mixing, reaction, and detection, but often demand the affiliated equipment for liquid control for these functions. As a helpful tool, the capillary pressure control valve (CPCV) has become popular to avoid using affiliated equipment. Liquid can be handled in a controlled manner by using the bubble pressure effects. In this paper, we analyze and categorize the CPCVs via three determining parameters: surface tension, contact angle, and microchannel shape. Finally, a few application scenarios and impacts of CPCV are listed, which includes how CPVC simplify automation of microfluidic networks, work with other driving modes; make extensive use of microfluidics by open channel, and sampling and delivery with controlled manners. The authors hope this review will help the development and use of the CPCV in microfluidic fields in both research and industry.