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Multi-photon upconversion super-resolution microscopy is a recently proposed imaging modality, based on lanthanide-doped nanocrystals, which can emit visible emission upon low-intensity near-infrared excitation. This imaging modality exhibits many advantages, including increased imaging depth, high signal-to-noise ratio, low phototoxicity, and high photostability. However, two factors seriously restrict its scanning speed, sometimes even to an intolerable degree; the long lanthanide emission lifetime and the low brightness. For proper imaging, pixel dwell times of several milliseconds are often required. In this work, a facile strategy is proposed to overcome these two obstacles. By adopting a high sensitizer (Yb3+) doping strategy for upconversion nanocrystals, their emission intensity is greatly increased and their emission transients are significantly accelerated, without losing the emission depletion efficiency induced by the depletion laser. This enables the implementation of a very fast upconversion stimulated emission depletion super-resolution microscopy with a scanning speed of 10 μs per pixel. This work opens the possibility for upconversion super-resolution microscopy to capture vital biological activities in real time.

Compared with traditional de novo drug discovery, drug repurposing has become an attractive drug discovery strategy due to its low-cost and high efficiency. Through a comprehensive analysis of the candidates that have been identified with drug repositioning potentials, it is found that although some drugs do not show obvious advantages in the original indications, they may exert more obvious effects in other diseases. In addition, some drugs have a synergistic effect to exert better clinical efficacy if used in combination. Particularly, it has been confirmed that drug repositioning has benefits and values on the current public health emergency such as the COVID-19 pandemic, which proved the great potential of drug repositioning. In this review, we systematically reviewed a series of representative drugs that have been repositioned for different diseases and illustrated successful cases in each disease. Especially, the mechanism of action for the representative drugs in new indications were explicitly explored for each disease, we hope this review can provide important insights for follow-up research.

Lanthanide-doped upconversion nanoparticles (UCNPs) are promising bioimaging nanoprobes due to their excellent photostability. As one of the most commonly used lanthanide activators, Tm3+ ions have perfect ladder-type electron configuration and can be directly excited by bio-friendly near-infrared-II (NIR-II) wavelengths. Here, the emission characteristics of Tm3+-doped nanoparticles under laser excitations of different near-infrared-II wavelengths were systematically investigated. The 1064 nm, 1150 nm, and 1208 nm lasers are proposed to be three excitation strategies with different response spectra of Tm3+ ions. In particular, we found that 1150 nm laser excitation enables intense three-photon 475 nm emission, which is nearly 100 times stronger than that excited by 1064 nm excitation. We further optimized the luminescence brightness after investigating the luminescence quenching mechanism of bare NaYF4: Tm (1.75%) core. After growing an inert shell, a ten-fold increase of emission intensity was achieved. Combining the advantages of NIR-II wavelength and the higher-order nonlinear excitation, a promising facile excitation strategy was developed for the application of thulium-doped upconversion nanoparticles in nanoparticles imaging and cancer cell microscopic imaging.

Drug-induced cardiotoxicity has become one of the major reasons leading to drug withdrawal in past decades, which is closely related to the blockade of human Ether-a-go-go-related gene (hERG) potassium channel. Developing reliable hERG predicting model and optimizing model can greatly reduce the risk faced in drug discovery. In this study, we constructed eight hERG classification models, the best of which shows desirable generalization ability on low-similarity clinical compounds, as well as advantages in perceiving activity gap caused by small structural changes. Furthermore, we developed a hERG optimizer based on fragment grow strategy and explored its usage in four cases. After reinforcement learning, our model successfully suggests same or similar compounds as chemists’ optimization. Results suggest that our model can provide reasonable optimizing direction to reduce hERG toxicity when hERG risk is corresponding to lipophilicity, basicity, the number of rotatable bonds and pi-pi interactions. Overall, we demonstrate our model as a promising tool for medicinal chemists in hERG optimization attempts.

This study aimed to probe into the effect of TRIM33 on oxidative stress‐induced apoptosis of osteoblasts in osteoporosis and to probe into the underlying mechanism. The apoptosis of osteoblasts was induced by H2O2 treatment and tested by flow cytometry. A mouse osteoporosis model was conducted by ovariectomy (OVX). The function of TRIM33 was assessed by in vitro and in vivo experiments. The mechanism of TRIM33 was determined by immunoprecipitation, immunofluorescent staining and co‐transfection experiments. Here, we found that TRIM33 expression was lessened in the osteoblasts of patients with osteoporosis and was positively correlated with the bone mineral density of these patients. FOXO3a and TRIM33 were co‐localized in the osteoblasts nuclei. TRIM33 silence boosted FOXO3a degradation in normal osteoblasts, while TRIM33 overexpression restrained FOXO3a degradation in H2O2‐treated osteoblasts. The binding of TRIM33 to CBP and its overexpression restrained CBP‐mediated FOXO3a acetylation, thereby attenuating FOXO3a ubiquitylation. The H2O2‐induced apoptosis of osteoblasts was restrained by TRIM33 overexpression, while the FOXO3a knockdown reversed this trend. The in vivo experiments corroborated that TRIM33 overexpression attenuated the OVX‐driven impacts in mice. In general, our findings expounded that TRIM33 protected osteoblasts against oxidative stress‐induced apoptosis in osteoporosis and that the underlying mechanism was the restraint of FOXO3a ubiquitylation and degradation., The mechanism flow diagram to a better understanding of the pivotal role of TRIM33. In the presence of oxidative stress, the expression of TRIM33 in osteoblasts was lessened and the lessened TRIM33 further boosted the acetylation modification of FOXO3a by CBP, thereby boosting the ubiquitination and degradation of FOXO3a protein, and boosting the oxidative stress and apoptosis of osteoblasts and aggravating osteoporosis.

400Hz inverters are usually connected to work in parallel to provide larger power. Considering the suppression of circulating current, LCL filter is a desirable selection and widely applied in parallel inverter systems. Whereas, when connecting with nonlinear loads, the harmonic currents through the load-side inductor will lead to a severe distortion on the output voltage of the inverter. After a detailed analysis of the inverter impedance, it reveals that although the output voltage feedback control can reduce harmonics, the suppression of circulating current also weakens. To improve the output voltage quality, an impedance reshaping method realized by multiple proportional and resonant(MPR) control and load current feed-forward is proposed, with which the inverter impedance appears very small at low-order harmonic frequencies and much larger at high-order harmonic frequencies. Applying the method, the suppression of both harmonic voltages and circulating current can be obtained. Finally, a prototype is built and tested, and the experimental results validated the effectiveness of the proposed control method.

In this paper, an environment classification method for Global Navigation Satellite System (GNSS) is presented. The goal of the study is to characterize the statistical properties of the historical GNSS data in certain typical environments, so that appropriate localization or navigation algorithms can be chosen to achieve better performances once any environments are recognized in real practice. We extract Dilute of Precision (DOP) value, Carrier-to-Noise Ratio (C/N) and Number of Satellite in View from NMEA-0183 data collected in three real typical environments to characterize the environments. Further, an attention-based Recurrent Neural Network (RNN) is constructed; the historical characteristics extracted above are fed into the RNN. Attention values are then calculated using real-time characteristics and the RNN output in each time steps. High dimensional features are then constructed by soft attention and are used as the input of a fully connected network for classification. The performance of proposed method on the classification task of three typical environments has significantly improvement compared to recurrent neural networks without attention mechanism, and achieves an average accuracy of 94% on the testing set.

The structure and parameter optimization of filter has an important effect on UPS output voltage waveform quality as well as its size and weight. In this paper, approaches to improve LC filter which is commonly adopted in inverter are studied and a combination of passive damping and harmonic trap is used to optimize the filter structure. Firstly, an investigation of passive damping methods is presented, including the comparison of characteristics between R d damping circuit and R d -C d damping circuit. After that, the selection guide of damping parameters is put forward based on the analysis of circuit parametric effect on quality factor. Secondly, the research on attenuation of harmonics at switching frequency is carried out which leads to the introduction of the two trap filter, series LC trap and parallel LC trap. Moreover, the circuit parameter’s effect on trap filter bandwidth is analyzed. Finally, two types of filter structure including LC trap -LC-RC and LC-RC-LC trap are proposed and the characteristics of the two filters are also analyzed. The effectiveness of the proposed filters of UPS inverter was verified by simulation and experimental results.

Drug-induced cardiotoxicity has become one of the major reasons leading to drug withdrawal in past decades, which is closely related to the blockade of human Ether-a-go-go-related gene (hERG) potassium channel. Developing reliable hERG predicting model and optimizing model can greatly reduce the risk faced in drug discovery. In this study, we constructed eight hERG classification models, the best of which shows desirable generalization ability on low-similarity clinical compounds, as well as advantages in perceiving activity gap caused by small structural changes. Furthermore, we developed a hERG optimizer based on fragment grow strategy. Results reveal that after reinforcement learning, our model can provide reasonable optimizing direction to reduce hERG toxicity, especially when hERG risk is corresponding to lipophilicity, basicity and pi-pi interactions. We also prove its usage in helping chemists quickly pick out core fragments and fix on the region to be optimized. Overall, we demonstrate our model as a promising tool for medicinal chemists in hERG optimization attempts.

Digital control is suitable for high-performance 400Hz inverters because of its simple control circuit, strong robustness and good dynamic performance. Considering the efficiency and device characteristics, the frequency modulation ratio of 400Hz inverter is relatively low, which leads to a negative influence on the dynamic performance of the inverter. Meanwhile, the output voltage harmonics of 400Hz SPWM inverter in low frequency band are large because of dead-time effect and digital delay. To cope with these problems, firstly, the influence of dead-time and digital delay is introduced in this paper. Then, the approach which combines reducing switching frequency, adding PR controller with phase compensation and serial harmonic trap is put forward. The dynamic performance and power quality of 400Hz inverter can be improved by the optimization of output filter and control parameters. Theoretical analysis and experimental results verified the proposed control method.

Since selective detection of multiple narrow spectral bands in the near-infrared (NIR) region still poses a fundamental challenge, we have, in this work, developed NIR photodetectors (PDs) using photon upconversion nanocrystals (UCNCs) combined with perovskite films. To conquer the relatively high pumping threshold of UCNCs, we designed a novel cascade optical field modulation strategy to boost upconversion luminescence (UCL) by cascading the superlensing effect of dielectric microlens arrays and the plasmonic effect of gold nanorods, which readily leads to a UCL enhancement by more than four orders of magnitude under weak light irradiation. By accommodating multiple optically active lanthanide ions in a core-shell-shell hierarchical architecture, developed PDs on top of this structure can detect three well-separated narrow bands in the NIR region, i.e., those centered at 808, 980, and 1540 nm. Due to the large UCL enhancement, the obtained PDs demonstrate extremely high responsivities of 30.73, 23.15, and 12.20 A W−1 and detectivities of 5.36, 3.45, and 1.91 × 1011 Jones for 808, 980, and 1540 nm light detection, respectively, together with short response times in the range of 80–120 ms. Moreover, we demonstrate for the first time that the response to the excitation modulation frequency of a PD can be employed to discriminate the incident light wavelength. We believe that our work provides novel insight for developing NIR PDs and that it can spur the development of other applications using upconversion nanotechnology., Cascade amplified upconversion luminescence: Applied in narrow band NIR photodetection Selective detection of multiple narrow spectral bands in the near-infrared (NIR) region is still a challenge. Recently, Hongwei Song and Wen Xu at Jilin University/China, Haichun Liu at KTH Royal Institute of Technology/Sweden, and their co-workers have successfully fabricated a novel multiple NIR bands photo-detectors (PDs) by combining multiple-excitation-bands core-shell upconversion nanocrystals (UCNCs) with MAPbI3 perovskite photoelectric conversion layer. Through a cascade optical field modulation strategy, a combination of microlenses and gold plasmon nanocrystals, the emission intensity of the UCNCs and the photoelectric signal of the PDs can be enhanced four orders of magnitude. Moreover, the excitation frequency of the PD has been employed to discriminate the wavelength of incident light for the first time. This work provides a novel insight for developing multiple bands NIR PDs, and for applications of upconversion nanotechnology.

In this paper, the classification problem of schizophrenia patients from healthy controls is considered, whose goal is to explore the relationship between DNA characteristics and schizophrenia. However, the DNA methylation data has the properties of small samples in high dimension and non-Gaussian distribution which makes it hard to do classification with DNA methylation data. Hence a classification method based on deep learning is designed. We propose a feature selection method based on attention mechanism which embeds a weight gated layer in the network structure to get a task-related sparse representation of the DNA methylation data. The performance of proposed method outperforms existing feature selection methods. On a real-world data set, the classification with proposed method achieves a high accuracy.

As the support network of power grid, the reliability and stability of power communication network structure become more and more important with the continuous expansion of power grid scale. Identifying and protecting important nodes in the power communication network in advance is an effective method to improve the robustness of the network. Therefore, this paper proposes a comprehensive evaluation method for the importance of nodes in the power communication network based on MADM (Multiple Attribute Decision Making). This method considers three indexes: degree centrality, betweenness centrality and PageRank value. By determining the weight of each index objectively and calculating the closeness of each node scheme to the ideal scheme, the comprehensive evaluation result of the importance of the node is obtained. When determining the weight of each index, the contrast and correlation of the indexes are considered at the same time, so that the result is closer to reality. The experimental results show that the proposed algorithm is more reliable and effective than the traditional method in ranking the importance of nodes in power communication network.

Traditional half-bridge converters often suffer from the neutral point voltage fluctuation which may result in unequal voltage stress on switching devices, common mode interference, and poor power quality. Therefore, a voltage-competing flyback-based balancer is proposed, and it simultaneously serves as the auxiliary power supplier and the neutral point voltage balancer with the limited power devices. Since the excessive power of the capacitor with the higher voltage is stored and transferred to the other one through the same primary winding, additional power loss caused by the leakage inductor is avoided. Meanwhile, the output voltage of the secondary winding has a close relation with the lower voltage of the DC-link capacitor, which can be utilized to monitor the capacitor voltage difference without paying the price for the isolated sampling of the DC-side voltage. Compared to the traditional switching balancing methods or the auxiliary-circuit-based balancing methods, the proposed balancer not only alleviates the control burden but also reduces the hardware complexity. The experiments are finally conducted on a balancer with 24V auxiliary power output to verify the effectiveness of the proposed balancer in equalizing the capacitor voltages of the half-bridge-type converters.

Aberrant activation of p21-activated kinase 1 (PAK1) is associated with tumour progression, and PAK1 has been recognized as a promising target for anticancer drug discovery. However, the development of potent PAK1 inhibitors with satisfactory kinase selectivity and favourable physicochemical properties remains a daunting challenge. Herein, we identified the 1H-indazole-3-carboxamide derivatives as potential PAK1 inhibitors using a fragment-based screening approach. The representative compound 30l exhibited excellent enzyme inhibition (PAK1 IC50 = 9.8 nM) and high PAK1 selectivity toward a panel of 29 kinases. The Structure-activity relationship (SAR) analysis showed that substituting of an appropriate hydrophobic ring in the deep back pocket and introducing a hydrophilic group in the bulk solvent region were critical for PAK1 inhibitory activity and selectivity. Additionally, the hERG channel activity of 30l demonstrated its low risk of hERG toxicity. Furthermore, it significantly suppressed the migration and invasion of MDA-MB-231 cells by downregulating Snail expression without affecting the tumour growth. These results provide a new type of chemical scaffolds targeting PAK1 and suggested that 1H-indazole-3-carboxamide derivatives may serve as lead compounds for the development of potential and selective PAK1 inhibitors.

Overtone Vibrational Transition-Induced Lanthanide Excited-State Quenching in Yb3+/Er3+-Doped Upconversion Nanocrystals

Perovskite photovoltaics have recently attracted extensive attention due to their unprecedented high power conversion efficiencies (PCEs) in combination with primitive manufacturing conditions. How ...

Background The important role of miR-455-3p in the pathogenesis of bone metabolism associated diseases is gradually emerging. This study aims to ascertain the involvement of miR-455-3p and its underlying mechanisms in osteoporosis. Methods The osteoblast cell lines MC3T3-E1 was treated with ferric ammonium citrate (FAC) to mimic a pathological environment for osteoporosis. The cytotoxic effect of iron overload was assessed by proliferation, apoptosis and oxidative stress of osteoblasts using commercial kits. Molecular biological methods, including qRT-PCR analysis, cell transfection and luciferase reporter assays were used to explain the role of miR-455-3p and its potential mechanisms in osteoblast apoptosis. Results FAC dramatically inhibited the proliferation of osteoblast cells MC3T3-E1 but increased the apoptosis. We also observed that FAC significantly down-regulated miR-455-3p in MC3T3-E1 cells but enhanced HDAC2 protein level. Moreover, miR-455-3p overexpression eliminated the effects of iron overload on osteoblast cell proliferation, apoptosis and oxidative stress. In addition, miR-455-3p regulated osteoblast cell proliferation, apoptosis and oxidative stress through regulating HDAC2-Nrf2/ARE signaling pathway. MiR-455-3p overexpression alleviated the oxidative stress injury in osteoporosis mice. Conclusion Our results demonstrated that miR-455-3p activated Nrf2/ARE signal pathway through suppressing Keap1 via negative regulating HDAC2 protein level, thereby suppressing oxidative stress and promoting osteoblasts growth.

Lanthanide-doped upconversion nanoparticles (UCNPs) are of great interest for biomedical applications. Currently, the applicability of UCNP bionanotechnology is hampered by the generally low luminescence intensity of UCNPs and inefficient energy transfer from UCNPs to surface-bound chromophores used e.g. for photodynamic therapy or analyte sensing. In this work, we address the low-efficiency issue by developing versatile core–shell nanostructures, where high-concentration sensitizers and activators are confined in the core and shell region of representative hexagonal NaYF4:Yb,Er UCNPs. After doping concentration optimization, the sensitizer-rich core is able to harvest/accumulate more excitation energy and generate almost one order of magnitude higher luminescence intensity than conventional homogeneously doped nanostructures. At the same time, the activator ions located in the shell enable a ∼6 times more efficient resonant energy transfer from UCNPs to surface-bound acceptor dye molecules due to the short distance between donor–acceptor pairs. Our work provides new insights into the rational design of UCNPs and will greatly increase the general applicability of upconversion nanotechnologies.

Resveratrol (Res), a naturally occurring polyphenolic compound, has been reported to exert many biological effects like anti-inflammatory and anti-oxidant effects. In this study, we investigated the role of Res on IL-1β-induced osteoarthritis (OA) chondrocytes and its possible mechanism. Results demonstrated that Res suppressed IL-1β-induced IL-1, IL-6 and TNF-α production in a dose-dependent manner. Res also decreased MMP-1, MMP-3 and MMP-13 production in IL-1β-induced OA chondrocytes. These results suggested that Res suppressed IL-1β-induced inflammation and matrix-metalloproteases (MMP) expression in OA chondrocytes. In addition, Res was found to reverse the decreased autophagy level through increasing the expression of Beclin1, LC3 II/I ratio and LC3+ puncta in IL-1β-induced OA chondrocytes. Inhibition of autophagy by 3-methyladenine (3-MA) abolished the inhibitory effect of Res on inflammation and MMP expression in IL-1β-induced OA chondrocytes. Moreover, the Wnt/β-catenin signaling pathway was activated in IL-1β-induced OA chondrocytes. However, Res was found to suppress this activated Wnt/β-catenin signaling pathway. Activation of the Wnt/β-catenin signaling pathway counteracted the promoted effect on autophagy and inhibitory effect on inflammation and MMP expression of Res in IL-1β-induced OA chondrocytes. Taken together, our data demonstrated that Res attenuated inflammation and reduced MMP expression through inducing autophagy via inhibiting the Wnt/β-catenin signaling pathway in IL-1β-induced OA chondrocytes. Res may be used as a potential therapeutic agent for OA treatment.

Stimulated emission depletion microscopy provides a powerful sub-diffraction imaging modality for life science studies. Conventionally, stimulated emission depletion requires a relatively high light intensity to obtain an adequate depletion efficiency through only light–matter interaction. Here we show efficient emission depletion for a class of lanthanide-doped upconversion nanoparticles with the assistance of interionic cross relaxation, which significantly lowers the laser intensity requirements of optical depletion. We demonstrate two-color super-resolution imaging using upconversion nanoparticles (resolution ~ 66 nm) with a single pair of excitation/depletion beams. In addition, we show super-resolution imaging of immunostained cytoskeleton structures of fixed cells (resolution ~ 82 nm) using upconversion nanoparticles. These achievements provide a new perspective for the development of photoswitchable luminescent probes and will broaden the applications of lanthanide-doped nanoparticles for sub-diffraction microscopic imaging., Upconversion nanoparticles, which do not suffer from the photophysical artifacts that limit fluorescent molecules, offer an exciting opportunity for biological super-resolution imaging. Here, Zhan et al. develop an efficient STED mechanism using optimized lanthanide upconversion nanoparticles, enabling cytoskeleton nanoscopic imaging.

Prompted by the prominent role of angiotensin converting enzyme (ACE) in hypertension, heart failures, myocardial infarction and diabetic nephropathy, we have attempted to discover novel ACE inhibitors through ligand-based virtual screening. Molecular docking method and rigorously validated model was utilized to search a natural compounds database. Finally, 36 compounds were randomly selected and subjected to in vitro ACE kinase inhibitory assay using fluorescence assays method. The results showed that three compounds (Licochalcone A, Echinatin and EGCG) have strong potential to be developed as a new class of ACE inhibitors. Further chemical modification via fragment modifications guided by structure and ligand-based computational methodologies can lead to discover better agents as potential clinical candidates.

Ununited peripheral nerves represent attractive site for connectivity with neuroprostheses because their predictable internal topography allows precise sorting of motor and sensory signals. Also transplantation of bone marrow mesenchymal stem cells (BMSCs) is increasingly recognized as an effective method of restore the peripheral nervous system injury due to its neuron-directed differentiation potential. This study was to evaluate the in vivo performance of BMSCs-packed Poly(3,4-ethylenedioxythiophene) (PEDOT) scaffolds across a critical nerve conduction gap and examine the potential mechanism by which BMSCs-packed PEDOT scaffolds mediate peripheral nerve regeneration in rat model of recurrent laryngeal nerve (RLN) deletion. In this study, rat models of 8 mm-RLN deletion were randomly divided into three groups and respectively received transplantation of autologous nerve or PEDOT scaffolds or BMSCspacked PEDOT scaffolds. After transplantation, neurological function was assessed by laryngeal electromyography. BMSCs-directed neuron differentiation was induced and confirmed by the upregulation of 6 nerve cell markers. Expression level of miRs, Notch signals and 6 nerve cell markers in nerve grafts or cells were determined by quantitative real-time PCR or western blot analysis. The results showed that BMSCs-packed PEDOT scaffolds transplantation significantly improved neurological function compared with PEDOT scaffolds alone. Regenerative nerve of BMSCs-packed PEDOT scaffolds showed higher expression level of miR-21 and Notch signals (Hes-1 and Notch intracellular domain (NICD)) than PEDOT alone. We found that the expression levels of miR-21, Hes-1 and NICD are increased with timecourse of neuron-directed differentiation stimulating of BMSCs. Meanwhile, both miR-21 overexpression and Notch pathway activation promote the expression of 6 nerve cell markers in BMSCs-directed neuron, whereas the inactivation of Notch pathway abrogates miR-21-inudced upregulation of 6 nerve cell markers. Moreover, knock-down of miR-21 suppresses the pro-neural restoration action of BMSCs-packed PEDOT scaffolds. In summary, our data suggested that BMSCs-packed PEDOT effectively repairs recurrent laryngeal nerve injury and the potential mechanism is miR-21- mediated Notch signal activation.

Lanthanide-doped upconversion nanoparticles (UCNPs) are increasingly used as luminescent candidates in multiplexing applications due to their excellent optical properties. In the past, several encoding identities have been proposed for UCNPs, including emission colour, intensity ratio between different emission bands, colour spatial distribution, and luminescence lifetime. In this paper, a new optical encoding dimension for upconversion nanomaterials is developed by exploring their luminescence kinetics, i.e., the phase angle of upconversion luminescence in response to a harmonic-wave excitation. Our theoretical derivation shows that the phase angle is governed jointly by the rise and decay times, characterizing the upconversion luminescence kinetics. Experimentally, a full set of methods are developed to manage the upconversion luminescence kinetics, through which the rise and decay times can be manipulated dependently or independently. Furthermore, a large phase-angle space is achieved in which tens of unique codes can potentially be generated in the same colour channel. Our work greatly extends the multiplexing capacity of UCNPs, and offers new opportunities for their applications in a wide range such as microarray assays, bioimaging, anti-counterfeiting, deep tissue multiplexing labelling/detection and high-density data storage. In addition, the development of this luminescence kinetics-based optical encoding strategy is also instructive for developing multiplexing techniques using other cascade luminescent systems that inherently lack multi-spectral channels, such as triplet-triplet annihilation molecule pairs.

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

With the continuous evolution of power grid towards smart grid, the scale of power communication network is expanding day by day, with the services carried by it more complex. Thus, power communication network is more vulnerable. In order to solve this problem, it is necessary to accurately identify the important nodes of the network in advance. Based on the topological characteristics of the power communication network and the running services, three indexes are selected in this paper. They are close centrality, betweenness centrality and the importance of the services carried by the nodes. What’s more, the degree of correlation between each index and the ideal solution is obtained by using the gray correlation algorithm, and then the node importance is determined. Finally, the simulation is carried out in a small power communication network to verify the rationality of the algorithm.

Human pharmacokinetics is of great significance in the selection of drug candidates, and in silico estimation of pharmacokinetic parameters in the early stage of drug development has become the trend of drug research owing to its time- and cost-saving advantages. Herein, quantitative structure-property relationship studies were carried out to predict four human pharmacokinetic parameters including volume of distribution at steady state (VDss), clearance (CL), terminal half-life (t1/2), and fraction unbound in plasma (fu), using a data set consisting of 1352 drugs. A series of regression models were built using the most suitable features selected by Boruta algorithm and four machine learning methods including support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and XGBoost (XGB). For VDss, SVM showed the best performance with R2test = 0.870 and RMSEtest = 0.208. For the other three pharmacokinetic parameters, the RF models produced the superior prediction accuracy (for CL, R2test = 0.875 and RMSEtest = 0.103; for t1/2, R2test = 0.832 and RMSEtest = 0.154; for fu, R2test = 0.818 and RMSEtest = 0.291). Assessed by 10-fold cross validation, leave-one-out cross validation, Y-randomization test and applicability domain evaluation, these models demonstrated excellent stability and predictive ability. Compared with other published models for human pharmacokinetic parameters estimation, it was further confirmed that our models obtained better predictive ability and could be used in the selection of preclinical candidates.

Cnidium lactone is effective in the maintenance of bone mass in various osteoporosis models; however, the precise molecular mechanisms are not understood. In this study, we investigated the effects and underlying mechanisms of action of cnidium lactone on receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis. Cnidium lactone dose-dependently inhibited osteoclast differentiation and formation, decreased the bone-resorbing activity of osteoclasts, and downregulated the expression of osteoclast differentiation marker genes. Cnidium lactone treatment considerably reduced RANKL-induced p38 MAPK and PI3K-Akt signal activity in RAW264.7 cells. The cnidium lactone-induced osteoclastogenesis was significantly attenuated by inhibition of p38 and PI3K through pretreatment with SB203580 and LY294002, respectively. Furthermore, cnidium lactone inhibited the expression of c-Fos and NFATc-1 with dose-dependently and enhanced by SB203580 and LY294002. In conclusion, cnidium lactone inhibits osteoclast differentiation through p38 MAPK and PI3K-Akt signalling pathway/c-Fos/NFATc1 signalling pathway.

Spondylodiscitis is a challenging spinal disorder that requires surgical treatment only as a last resort. The use of various procedures and instruments remains controversial. In this retrospective study, we reported the safety and efficacy of a CaSOThirty-four patients with lumbar spondylodiscitis underwent TLIF. CaSOThe patients were followed up for at least 12 months (range, 12-24 months). At 3 months follow-up, C-reactive protein levels and erythrocyte sedimentation rate returned to normal with no relapse. Nineteen patients with American Spinal Injury Association grade D preoperatively returned to American Spinal Injury Association grade E. The improvement on the visual analog scale and Oswestry Disability Index was 66.9% and 84.9%, respectively. At 12 months follow-up, 67.6% of patients achieved definitive union. The corrections of total and local lordotic angle were 10.02° ± 8.77° and 7.7° ± 8.75°, respectively. The rate of achievement of satisfactory Kirkaldy-Willis functional criteria was 85.3%.The combined use of single-level TLIF with a CaSO

Human ether-a-go-go-related gene (hERG) K+ channel blockage may cause severe cardiac side-effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates.

Fms-like tyrosine kinase 3 (FLT3) has been considered as a potential drug target for the treatment of acute myeloid leukemia (AML), because of its high and aberrant expression in AML patients, especially the patients with FLT3-ITD mutation. Initiating from a hit compound (IC50: 500 nM against FLT3-ITD), a series of compounds were designed and synthesized based on benzo[d]oxazole-2-amine scaffold to discover new potent FLT3-ITD inhibitors. During the medicinal chemistry works, flexible molecular docking was used to provide design rationale and study the binding modes of the target compounds. Through the mixed SAR exploration based on the enzymatic and cellular activities, compound T24 was identified with potent FLT3-ITD inhibitory (IC50: 0.41 nM) and anti-proliferative (IC50: 0.037 μM against MV4-11 cells) activities. And the binding mode of T24 with “DFG-in” FLT3 was simulated by a 20-ns molecular dynamics run, providing some insights into further medicinal chemistry efforts toward novel FLT3 inhibitors in AML therapy.

In this study, we systematically investigate the decay characteristics of upconversion luminescence (UCL) under anti-Stokes excitation through numerical simulations based on rate-equation models. We find that a UCL decay profile generally involves contributions from the sensitizer's excited-state lifetime, energy transfer and cross-relaxation processes. It should thus be regarded as the overall temporal response of the whole upconversion system to the excitation function rather than the intrinsic lifetime of the luminescence emitting state. Only under certain conditions, such as when the effective lifetime of the sensitizer's excited state is significantly shorter than that of the UCL emitting state and of the absence of cross-relaxation processes involving the emitting energy level, the UCL decay time approaches the intrinsic lifetime of the emitting state. Subsequently, Stokes excitation is generally preferred in order to accurately quantify the intrinsic lifetime of the emitting state. However, possible cross-relaxation between doped ions at high doping levels can complicate the decay characteristics of the luminescence and even make the Stokes-excitation approach fail. A strong cross-relaxation process can also account for the power dependence of the decay characteristics of UCL. QC 20190318

Aims Current study aimed to investigate the effects of lncRNA SNHG1 on osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) and explore the underlying mechanisms. Main methods Mouse model of osteoporosis was created by ovariectomy (OVX). The BMD of mice spine, the serum level of β-CTX and the ALP activity were measured. The expression of SNHG1, JNK, p-JNK, p-38, p-p38 and Osterix were examined by qRT-PCR and Western blot. Co-IP assay was used to verify the effect of SNHG1 on the interaction between p-p38 and Nedd4. Ubiquitination assay was used to evaluate the roles of SNHG1 in ubiquitination of p-p38. Key findings In the mice with osteoporosis, BMD was decreased and β-CTX concentration and SNHG1 expression were increased. ALP activity and p-p38 protein level were elevated and SNHG1 expression was down-regulated in BMSCs stimulated by osteogenic inducer, while the effects were reversed by SNHG1 over-expression. SNHG1 over-expression enhanced the interaction between Nedd4 and p-p38, disrupted protein stability of p-p38, and promoted the ubiquitination of p-p38. In addition, pcDNA-SNHG1 down-regulated p-p38 protein level, and sh-Nedd4 removed the trend. Nedd4 silencing elevated ALP activity and Osterix protein level, while p-38 inhibitor abrogated the effects. In vivo, SNHG1 silence increased BMD and Osterix protein level, and decreased endogenous SNHG1 expression in mice with OVX. Significance This study proved the regulation mechanism that lncRNA SNHG1 negatively regulates p38 MAPK signal pathway through ubiquitination mediated by Nedd4, and thus inhibits osteogenic differentiation of BMSCs.

Our current study demonstrated that BMMSC-Exos could accelerate the proliferation and migration of endothelial cells and osteoblast cells, further promoting angiogenesis and osteogenesis to enhance fracture healing. As an intercellular communicator, exosomes can activate the HIF-1ɑ/VEGF and the BMP-2/Smad1/RUNX2 signalling pathways, and this may be one of the underlying mechanisms in the fracture healing process. The data presented here provide the first evidence for the potential of BMMSC-Exos in treating nonunion. Our findings suggest that BMMSC-Exos may be a prospective therapeutic approach in the treatment of nonunion.

The dynamic sag correctors are used at the sensitive load terminals of the centralized power supply in order to avoid the grid voltage sag. This paper focuses on the correction of grid voltage swell/sag in large range and the quality of compensated voltage. A wide correction range dynamic voltage corrector is proposed for solving above problems with low cost. The operation principle, the voltage corrector range, and the design consideration of key parameters are analyzed. The increase of dc-link voltage broadens the voltage sag correction range, while the realization of the voltage swell correction benefits from the bidirectional energy flow. The simulation and experimental results verify the validity of the theoretical analysis. A prototype which applies to the centralized power supply-based ground power unit for aircrafts has been developed. It is characterized as small size, light weight, high compensation efficiency, good dynamic performance, and high quality of output waveform.

Covalent drugs have attracted increasing attention in recent years due to good inhibitory activity and selectivity. Targeting noncatalytic cysteines with irreversible inhibitors is a powerful approach for enhancing pharmacological potency and selectivity because cysteines can form covalent bonds with inhibitors through their nucleophilic thiol groups. However, most human kinases have multiple noncatalytic cysteines within the active site; to accurately predict which cysteine is most likely to form covalent bonds is of great importance but remains a challenge when designing irreversible inhibitors. In this work, FTMap was first applied to check its ability in predicting covalent binding site defined as the region where covalent bonds are formed between cysteines and irreversible inhibitors. Results show that it has excellent performance in detecting the hot spots within the binding pocket, and its hydrogen bond interaction frequency analysis could give us some interesting instructions for identification of covalent binding cysteines. Furthermore, we proposed a simple but useful covalent fragment probing approach and showed that it successfully predicted the covalent binding site of seven targets. By adopting a distance-based method, we observed that the closer the nucleophiles of covalent warheads are to the thiol group of a cysteine, the higher the possibility that a cysteine is prone to form a covalent bond. We believe that the combination of FTMap and our distance-based covalent fragment probing method can become a useful tool in detecting the covalent binding site of these targets.

Silicon is essential for bone formation. A low-silicon diet leads to bone defects, and numerous animal models have demonstrated that silicon supplementation increases bone mineral density (BMD) and reduces bone fragility. However, the exact mechanism of this action has not been characterized. In this study, we aimed to determine the role of biological silicon in the induction of osteoblast differentiation and the possible underlying mechanism. We examined whether orthosilicic acid promotes collagen type 1 (COL-1) and osteocalcin synthesis through the bone morphogenetic protein-2 (BMP-2)/Smad1/5/runt-related transcription factor 2 (RUNX2) signaling pathway by investigating its effect in vitro at several concentrations on COL-1 and osteocalcin synthesis in human osteosarcoma cell lines (MG-63 and U2-OS). The expression of relevant proteins was detected by Western blotting following exposure to noggin, an inhibitor of BMP-2. In MG-63 cells, immunofluorescence methods were applied to detect changes in the expression of BMP-2, phosphorylated Smad1/5 (P-Smad1/5), and RUNX2. Furthermore, rat bone mesenchymal stem cells (BMSCs) were used to determine the effect of orthosilicic acid on osteogenic differentiation. Exposure to 10 μM orthosilicic acid markedly increased the expression of BMP-2, P-Smad1/5, RUNX2, COL-1, and osteocalcin in osteosarcoma cell lines. Enhanced ALP activity and the formation of mineralized nodules were also observed under these conditions. Furthermore, preconditioning with noggin inhibited the silicon-induced upregulation of P-Smad1/5, RUNX2, and COL-1 expression. In conclusion, the BMP-2/Smad1/5/RUNX2 signaling pathway participates in the silicon-mediated induction of COL-1 and osteocalcin synthesis, and orthosilicic acid promotes the osteogenic differentiation of rat BMSCs.

Optogenetics is an emerging powerful tool to investigate workings of the nervous system. However, the use of low tissue penetrating visible light limits its therapeutic potential. Employing deep penetrating near-infrared (NIR) light for optogenetics would be beneficial but it cannot be used directly. This issue can be tackled with upconversion nanoparticles (UCNs) acting as nanotransducers emitting at shorter wavelengths extending to the UV range upon NIR light excitation. Although attractive, implementation of such NIR-optogenetics is hindered by the low UCN emission intensity that necessitates high NIR excitation intensities, resulting in overheating issues. A novel quasi-continuous wave (quasi-CW) excitation approach is developed that significantly enhances multiphoton emissions from UCNs, and for the first time NIR light-triggered optogenetic manipulations are implemented in vitro and in C. elegans. The approach developed here enables the activation of channelrhodopsin-2 with a significantly lower excitation power and UCN concentration along with negligible phototoxicity as seen with CW excitation, paving the way for therapeutic optogenetics.

Owing to their extraordinary surface plasmon and semiconductor properties, copper chalcogenide nanocrystals (NCs) have experienced a steeply increased interest for various types of applications since the first discovery of their plasmonic property in 2009. This article critically and comprehensively reviews the decade long research effort devoted to doped plasmonic copper chalcogenide binary NCs with respect to their synthesis methods, their theoretical description and various applications. In particular, we focus on factors that impact their localized surface plasmon resonances (LSPRs) and on methods used for tuning the LSPRs. We emphasize the underlying mechanisms of LSPR generation and the unique roles and advantages of the copper chalcogenide NCs with respect to the commonly attended plasmonic metal nanoparticles. Finally, we review current challenges in the field of copper chalcogenide NCs and give a perspective for further research. We believe that this review provides a timely and concise summary of the field of plasmonic copper chalcogenide NCs for the benefit and inspiration of its rapid and formulated development.

In order to explore the situation of college students’ emotional state, regulatory emotion self-efficacy and mobile phone addiction tendency as well as their relationships, a total of 350 college students were assessed with Mobile Phone Addiction Tendency Scale (MPATS), Positive Affect and Negative Affect Scale (PANAS) and the Scale of Regulatory Emotional Self-efficacy (SRESE). The result showed that: (1) 40.86% of college students had the tendency of cell phone addiction, which was serious; 72.0% of college students were in a positive emotional state, 22% were in a negative emotional state; college students' regulatory emotion self-efficacy was in the middle level; (2) there was no gender, grade, major type, household registration type (rural and urban) and whether only child difference in mobile phone addiction tendency; (3) the positive emotions of college students were negatively correlated with the tendency of mobile phone addiction, while the negative emotions were positively correlated with the tendency of mobile phone addiction, and the positive emotion was positively correlated with regulatory emotional self-efficacy；(4) the regulatory emotion self-efficacy had a partial mediating effect between the positive emotions and mobile phone addiction tendency and had no mediating effect between the negative emotions and mobile phone addiction tendency.

This chapter gives a tutorial account of existing mathematical works that are relevant to the statistical analysis of random matrices arising in smart grids. It aims to build a model for spatiotemporal phasor measurement unit (PMU) data using large dimensional random matrices. Representation of the spatiotemporal PMU data as a sequence of large random matrices is a crucial part for power state evaluation, as it turns the big PMU data into tiny data for practical uses. To improve the understanding and application of random matrix theory (RMT) technologies, the chapter then introduces some basic principles of RMT, such as asymptotic spectrum laws, transforms, convergence rate, and free probability. It also presents a brief introduction to the main development of RMT. Motivated by the immediate demands of tackling the tricky problems raised from large‐scale smart grids, the chapter further explores RMT‐based schemes for spatiotemporal big data analysis.

Off-line multi-hyphenated methods including affinity-ultrafiltration mass spectrometry, molecular docking, and reverse-phase high-performance liquid chromatography-fluorescence detection (RP-HPLC-FD) were used to rapidly screen and identify natural inducible nitric oxide synthase (iNOS) inhibitors isolated from Ophiopogon japonicus (Thunb.) Ker-Gawl. Ultrafiltration high-performance liquid chromatography quadrupole time-of-flight mass spectrometry was used to screen and identify the potential ligands with high binding affinity to iNOS. Seven compounds (three saponins and four flavonoids) were found in Radix Ophiopogonis. Molecular docking identified the binding sites and found that the binding affinity of the three ophiopogonis ligands was higher than that of the four homoisoflavonoids. Pre-column derivatization of l-citrulline and RP-HPLC-FD verified that the in vitro inhibition activity of the three ophiopogonis compounds was stronger than that of the four homoisoflavonoids, which was similar to the screened results. In conclusion, combining an in vitro iNOS inhibition assay and affinity-ultrafiltration mass spectrometry with molecular docking was a powerful tool for rapid screening and identification of iNOS inhibitors isolated from this traditional Chinese medicine.

Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross-validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure-based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.

FLT3 is often over-expressed in AML, and FLT3 mutants, especially FLT-ITD, are closely related to the poor prognosis in AML patients. Thus, FLT3 has become an attractive target for AML therapy. A series of FLT3 inhibitors have been evaluated in various clinical trials, one of which was approved for AML. However, current FLT3 inhibitors still faced the challenges of kinase selectivity and drug resistance due to concurrent FLT3-ITD-TKD mutations. In this work, a new FLT3 inhibitor (compound 1) with simple structure was discovered through virtually screening an in-house molecule database which contains numerous compounds with kinase-inhibition activity. Compound 1 was identified with potent inhibitory activity against several FLT3 mutants and high FLT3 selectivity over other kinases. Moreover, its anti-growth effects on tumor cells in vitro were dependent on the expression of FLT3-ITD, and it showed little cytotoxicity to MV4-11 and human normal cells. Mechanism studies illustrated that compound 1 blocked FLT3 pathway, caused cell cycle arrest and induced apoptosis in MV4-11 cells. Finally, the binding mode of compound 1 was studied by molecular dynamics simulations, which provides insights into key residues involved in intermolecular binding and further structural optimization strategy. Compound 1 can thus serve as a good starting point for the research on FLT3 inhibitors towards the kinase selectivity and potential to overcome drug resistance.

Silicon is one of the essential trace elements in the human body; the deficiency of which may lead to bone diseases. Numerous animal experiments have shown that an appropriate increase in the intake of silicon is beneficial to enhancing bone density and toughness to prevent osteoporosis. However, the molecular mechanisms of the silicon-mediated osteogenesis process have not been sufficiently clarified. In this study, we determined the possible osteogenesis-related mechanisms of orthosilicic acid at a molecular level. We detected the relevant pathway and osteogenic indicators by immunofluorescence (IF), Western blot, alkaline phosphatase (ALP) staining (using 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium [BCIP/NBT]), ALP enzyme labeling method, osteocalcin (OCN), and N-terminal propeptide of type 1 procollagen (P1NP) enzyme-linked immunosorbent assay (ELISA). We found that orthosilicic acid is capable of enhancing the expression of phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K), phospho-protein kinase B (P-Akt), phospho-mammalian target of rapamycin (P-mTOR), and related osteogenic markers (runt-related transcription factor 2 [RUNX2], type I collagen [COL1], ALP, OCN, and P1NP). However, with the addition of PI3K-Akt-mTOR pathway-specific inhibitor LY294002, the expression of PI3K, P-Akt, P-mTOR, RUNX2, COL1, ALP, OCN, and P1NP decreased. The results indicated that the PI3K-Akt-mTOR pathway played a positive regulatory role in the process of orthosilicic acid-mediated osteogenesis in vitro.

The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood–brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure–property relationship study was carried out to predict blood–brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ( $$ R^{2} $$ = 0.938), test set ( $$ R^{2} $$ = 0.840) and tenfold cross-validation ( $$ Q^{2} $$ = 0.788). Finally, we found that the polar surface area and octanol–water partition coefficient have the greatest influence on blood–brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.

Osteosarcoma is the most common non-hematological primary bony malignancy in children and young adults with tumor metastasis being a common event at diagnosis. Understanding the pathogenesis of metastatic osteosarcoma may help identify potential therapeutic targets. In this study, we found that the level of microRNA-645 (miR-645) in osteosarcoma tumor tissues was significantly increased compared with their paired non-tumorous tissues, and was associated with histologic grade, TNM staging, lymph metastasis and distant metastasis. Knockdown of miR-645 caused a remarkable inhibition of migration of osteosarcoma U2OS cells. Furthermore, miR-645 inhibited NME2 (nucleoside diphosphate kinase 2) expression through directly binding to its 3' untranslated region. In human osteosarcoma tissues, we also found that NME2 was significantly decreased in tumor tissues, and its level was negatively correlated with miR-645. In addition, silencing NME2 attenuated the decreased cell migration by knockdown of miR-645, suggesting that it was involved in the miR-645 induced cell migration of osteosarcoma cells. Taken together, we found that miR-645 was up-regulated in osteosarcoma tissues and could promote osteosarcoma cell migration through directly inhibiting the tumor suppressor NME2. Our data provide novel insight into the role of miR-645 in osteosarcoma and indicate that miR-645 might be a potential therapeutic target of osteosarcoma.

Resveratrol (Res), a naturally occurring polyphenolic compound, has been reported to exert many biological effects like anti-inflammatory and anti-oxidant effects. In this study, we investigated the role of Res on IL-1β-induced osteoarthritis (OA) chondrocytes and its possible mechanism. Results demonstrated that Res suppressed IL-1β-induced IL-1, IL-6 and TNF-α production in a dose-dependent manner. Res also decreased MMP-1, MMP-3 and MMP-13 production in IL-1β-induced OA chondrocytes. These results suggested that Res suppressed IL-1β-induced inflammation and matrix-metalloproteases (MMP) expression in OA chondrocytes. In addition, Res was found to reverse the decreased autophagy level through increasing the expression of Beclin1, LC3 II/I ratio and LC3