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Background: Oxidative stress and inflammatory responses are critical factors in calcium oxalate (CaOx) crystal-induced renal injury. Reactive oxygen species (ROS) are usually produced in the cytoplasm and mitochondria and trigger the priming and activation of the NLRP3 inflammasome, thereby regulating cytokines and inflammation. Polydatin is a plant rhizome extract with anti-inflammatory, antioxidant, and antitumor effects. However, it remains not clear whether and how these pathophysiological processes exists in CaOx crystal-induced renal inflammatory injury. Methods: Here, we measured the expression of the NLRP3 inflammasome, IL-18, IL-1β, intracellular and mitochondrial ROS (mtROS) levels and relevant morphological changes in treated renal tubular epithelial cells (TECs) and stone-forming rats. The study further explored the action of intracellular ROS and mtROS on these inflammatory damage, and the beneficial effects and pathway of polydatin. Results: We verified that CaOx crystal-induced cytoplasmic ROS and mtROS upregulation promoted the priming and activation of the NLRP3 inflammasome, thereby stimulating IL-18/1β maturation and activation. Polydatin can relieve oxidative stress and inflammatory damage by decreasing ROS. We further demonstrated that mtROS is the main target for polydatin to exert the NLRP3 inflammasome-regulating function. The inhibition of mtROS can effectively relieve the inflammatory damage to TECs and kidney caused by CaOx crystal. Conclusion: These findings provide new insight into the relationship between mitochondrial damage and inflammation in nephrolithiasis and show that polydatin-mediated anti-inflammatory and antioxidative protection is a therapeutic strategy for, but not limited to, crystalline nephropathy.

Abstract Background Colorectal cancer is the third most common cause of death among cancers in the world. Although improvements in various treatments have greatly improved the survival time of colorectal cancer patients, since colorectal cancer is often at an advanced stage when diagnosed, the prognosis of patients is still very poor. Since the ceRNA regulatory network was proposed in 2011, it has greatly promoted the study of the molecular mechanism of colorectal cancer occurrence and development. Objective Exploring the new molecular mechanism of colorectal cancer occurrence and development and providing new targets for the diagnosis and treatment of colorectal cancer. Method We analyzed the RNA-seq data of CRC from TCGA, such as differential expression analysis, weighted gene co-expression network analysis (WGCNA) and construction of ceRNA regulatory network. Results We constructed a ceRNA network using RNA-seq data of CRC from TCGA. In the ceRNA regulatory network, 19 hub molecules with significant prognostic effects were ultimately identified, including 8 lncRNAs, 2 mRNAs and 9 miRNAs. These hub molecules constitute the lncRNA-miRNA, miRNA-mRNA or lncRNA-miRNA-mRNA axis. Conclusion In this article, some new ceRNA regulatory axes have been discovered, which may potentially disclose new molecular mechanisms for the occurrence and development of colorectal cancer, thereby providing an important blueprint for the treatment and prognosis assessment of CRC patients.

Neuroinflammation mediated by microglia is an important pathophysiological mechanism in neurodegenerative diseases. However, there is a lack of effective drugs to treat neuroinflammation. N-acetyldopamine dimer (NADD) is a natural compound from the traditional Chinese medicine Isaria cicada. In our previous study, we found that NADD can attenuate DSS-induced ulcerative colitis by suppressing the NF-κB and MAPK pathways. Does NADD inhibit neuroinflammation, and what is the target of NADD? To answer this question, lipopolysaccharide (LPS)-stimulated BV-2 microglia was used as a cell model to investigate the effect of NADD on neuroinflammation. Nitric oxide (NO) detection, reactive oxygen species (ROS) detection and enzyme-linked immunosorbent assay (ELISA) results show that NADD attenuates inflammatory signals and proinflammatory cytokines in LPS-stimulated BV-2 microglia, including NO, ROS, tumor necrosis factor (TNF)-α, interleukin (IL)-1β and interleukin-6 (IL-6). Western blot analysis show that NADD inhibits the protein levels of Toll-like receptor 4 (TLR4), nuclear factor kappa-B (NF-κB), NOD-like receptor thermal protein domain associated protein 3 (NLRP3), ASC and cysteinyl aspartate specific proteinase (Caspase)-1, indicating that NADD may inhibit neuroinflammation through the TLR4/NF-κB and NLRP3/Caspase-1 signaling pathways. In addition, surface plasmon resonance assays and molecular docking demonstrate that NADD binds with TLR4 directly. Our study reveals a new role of NADD in inhibiting the TLR4/NF-κB and NLRP3/Caspase-1 pathways, and shows that TLR4-MD2 is the direct target of NADD, which may provide a potential therapeutic candidate for the treatment of neuroinflammation.

The potential applications of multilayer graphene in many fields, such as superconductivity and thermal conductivity, continue to emerge. However, there are still many problems in the growth mechanism of multilayer graphene. In this paper, a simple control strategy for the preparation of interlayer-coupled multilayer graphene on a liquid Cu substrate was developed. By adjusting the flow rate of a carrier gas in the CVD system, the effect for finely controlling the carbon source supply was achieved. Therefore, the carbon could diffuse from the edge of the single-layer graphene to underneath the layer of graphene and then interlayer-coupled multilayer graphene with different shapes were prepared. Through a variety of characterization methods, it was determined that the stacked mode of interlayer-coupled multilayer graphene conformed to AB-stacking structure. The small multilayer graphene domains stacked under single-layer graphene was first found, and the growth process and growth mechanism of interlayer-coupled multilayer graphene with winged and umbrella shapes were studied, respectively. This study reveals the growth mechanism of multilayer graphene grown by using a carbon source through edge diffusion, paving the way for the controllable preparation of multilayer graphene on a liquid Cu surface.

Abstract Twisted bilayer graphene (tBLG) possesses various novel physical properties. Most of tBLG are fabricated by using artificial methods of stacking or folding single‐layer graphene. Chemical vapor deposition (CVD) has been verified that it holds great potential for preparation of large‐size high‐quality graphene. Therefore, it is significant for preparing tBLG in situ by using CVD technology. In this work, a novel approach is developed to directly prepare tBLGs on liquid Cu substrate. When the growth temperature exceeds a certain critical value, the state of aligned high‐quality single‐layer graphene domains grown on liquid Cu will be broken. Then, tBLG with twisted double‐layer regions is prepared in situ by rotating and intercalating between graphene domains. Experimental observations suggest that the liquid phase of Cu substrate and gas flow play a crucial role for the formation of tBLGs. These results demonstrate that the liquid Cu is an ideal potential substrate for preparing tBLGs with a full range of twisted angles and studying the formation mechanism of layer‐stacked materials.

Classical trigeminal neuralgia (CTN) is a unilateral and severe facial pain disease, which seriously affects the patient's quality of life. Microvascular decompression (MVD) is currently the most effective surgical method, and it is the only treatment for the etiology of CTN. Imaging for MVD has been increasingly used, and the advantages and disadvantages of endoscopy-assisted vascular decompression surgery have been controversially debated. In this review, we aimed to discuss the advantages of MVD in the treatment of patients with CTN, the importance of using imaging in disease management, and the improvements of vascular decompression surgery through the application and maturity of endoscopic techniques. Compared with other surgical methods, MVD has more prominent short- and long-term treatment effects. Its selection depends on the accurate discovery of neurovascular compression by preoperative imaging. Moreover, magnetic resonance imaging plays a diverse role in MVD, not only in identifying the responsible vessels but also in determining the prognosis and as a tool for scientific research. The use of endoscopic techniques provides improved visualization of the MVD and additional benefits for vascular decompression surgery.

Efficient detection of pavement cracks can effectively prevent traffic accidents and reduce road maintenance costs. In this paper, an improved YOLOv5 network combined with a Bottleneck Transformer is proposed for crack detection, called YOLOv5-CBoT. By combining the CNN and Transformer, YOLOv5-CBoT can better capture long-range dependencies to obtain more global information, so as to adapt to the long-span detection task of cracks. Moreover, the C2f module, which is proposed in the state-of-the-art object detection network YOLOv8, is introduced to further optimize the network by paralleling more gradient flow branches to obtain richer gradient information. The experimental results show that the improved YOLOv5 network has achieved competitive results on RDD2020 dataset, with fewer parameters and lower computational complexity but with higher accuracy and faster inference speed.

Abstract Dietary compounds play an important role in the prevention and treatment of many cancers, although their specific molecular mechanism is not yet known. In the present study, thirty dietary agents were analyzed on nine drug targets through in silico studies. However, nine dietary scaffolds, such as silibinin, flavopiridol, oleandrin, ursolic acid, α-boswellic acid, β-boswellic acid, triterpenoid, guggulsterone, and oleanolic acid potentially bound to the cavity of PI3K-α, PKC-η, H-Ras, and Ras with the highest binding energy. Particularly, the compounds silibinin and flavopiridol have been shown to have broad spectrum anticancer activity. Interestingly, flavopiridol was embedded in the pockets of PI3K-α and PKC-η as bound crystal inhibitors in two different conformations and showed significant interactions with ATP binding pocket residues. However, complex-based pharmacophore modeling achieved two vital pharmacophoric features namely, two H-bond acceptors for PI3K-α, while three are hydrophobic, one cat-donor and one H-bond donor and acceptor for PKC-η, respectively. The database screening with the ChemBridge core library explored potential hits on a valid pharmacophore query. Therefore, to optimize perspective lead compounds from the hits, which were subjected to various constraints such as docking, MM/GBVI, Lipinski rule of five, ADMET and toxicity properties. Henceforth, the top ligands were sorted out and examined for vital interactions with key residues, arguably the top three promising lead compounds for PI3K-α, while seven for PKC-η, exhibiting binding energy from − 11.5 to − 8.5 kcal mol−1. Therefore, these scaffolds could be helpful in the development of novel class of effective anticancer agents.

Automatic crack detection is always a challenging task due to the inherent complex backgrounds, uneven illumination, irregular patterns, and various types of noise interference. In this paper, we proposed a U-shaped encoder–decoder semantic segmentation network combining Unet and Resnet for pixel-level pavement crack image segmentation, which is called RUC-Net. We introduced the spatial-channel squeeze and excitation (scSE) attention module to improve the detection effect and used the focal loss function to deal with the class imbalance problem in the pavement crack segmentation task. We evaluated our methods using three public datasets, CFD, Crack500, and DeepCrack, and all achieved superior results to those of FCN, Unet, and SegNet. In addition, taking the CFD dataset as an example, we performed ablation studies and compared the differences of various scSE modules and their combinations in improving the performance of crack detection.

Abstract Graphene‐based moiré heterostructures are strongly correlated materials, and they are considered to be an effective platform to investigate the challenges of condensed matter physics. This is due to the distinct electronic properties that are unique to moiré superlattices and peculiar band structures. The increasing research on strongly correlated physics via graphene‐based moiré heterostructures, especially unconventional superconductors, greatly promotes the development of condensed matter physics. Herein, the preparation methods of graphene‐based moiré heterostructures on both in situ growth and assembling monolayer 2D materials are discussed. Methods to improve the quality of graphene and optimize the transfer process are presented to mitigate the limitations of low‐quality graphene and damage caused by the transfer process during the fabrication of graphene‐based moiré heterostructures. Then, the topological properties in various graphene‐based moiré heterostructures are reviewed. Furthermore, recent advances regarding the factors that influence physical performances via a changing twist angle, the exertion of strain, and regulation of the dielectric environment are presented. Moreover, various unique physical properties in graphene‐based moiré heterostructures are demonstrated. Finally, the challenges faced during the preparation and characterization of graphene‐based moiré heterostructures are discussed. An outlook for the further development of moiré heterostructures is also presented.

Abstract Based on diketopyrrolopyrrole (DPP) and (E)-3-phenyl-2-(thiophen-2-yl)acrylonitrile (BVCNT)-linked conjugated backbones, three donor–acceptor type conjugated organic small-molecule compounds DPP-BVCNT, DPP-2FBVCNT, and DPP-3FBVCNT were designed and synthesized. Among them, the 2-decyltetradecyl side chain on the DPP acceptor unit was used to ensure the solubility of the material. The fluorine (F) atoms combined with the nitrile on the BVCNT donor unit were used to adjust electronic structures and charge carrier transport properties of the conjugated system. All the three small molecules exhibited good solution dispersibility and thermal stability, providing an important guarantee for the solution processing and annealing optimization of organic field-effect transistors (OFETs). The top-gate-bottom-contact OFET devices based on these compounds showed good ambipolar or p-type performances. The relationship between molecular structures and OFET performances indicated that the F-substitution and its position significantly affected their charge carrier transport properties. The F-substitution could remarkably change the performance from p-type to ambipolar especially for the outer-side-F-substituted compound DPP-2FBVCNT, which showed the best OFET performances with the maximum hole/electron mobilities of 0.023/0.220 cm2 V−1 s−1. These results provided a promising idea for developing small-molecule OFET materials with good solution processability, good thermal stability, and high ambipolar performances.

The unipolar n‐type polymeric semiconductors are crucial for the development of complementary inverters and complementary logic circuits. To achieve this target, the polymer skeleton should be electron‐deficient, which guarantees the energy‐level alignment between the lowest unoccupied molecular orbital energy level of polymeric materials and the work function of electrode, further permitting effective electron injection. Different from the introduction of the sp2‐hybridized nitrogen atoms and fluorine atoms, cyano‐substituted aromatic blocks are synthesized and further copolymerized with naphthalene diimide (NDI) unit, affording a series of copolymers of PNDI‐BTCN, PNDI‐TVTCN, and PNDI‐SVSCN. The photophysical, electrochemical, and thermal properties of all the copolymers are systematically investigated, and their semiconducting performance is studied by fabricating field‐effect transistors and tested under atmosphere. All the polymers exhibit unipolar n‐type semiconducting performance because of the synergetic effect of strong electron‐withdrawing NDI units and cyano‐substituted aromatic blocks. The highest mobility of 0.20 cm2 V−1 s−1 is obtained. Moreover, theoretical simulation and thin‐film characterization are conducted to reveal the difference in semiconducting performance among the three polymeric materials.

The composite of graphene and carbon nanotubes (CNTs) can increase the electrical conductivity and the electrochemical active sites in favor of the application in energy storage and conversion. However, the fabrication of graphene/CNT hybrid material requires multiple steps to introduce two kinds of catalysts for separate growth of graphene and CNTs. Here, we report a one-step process to in situ grow a seamless graphene/CNT heterojunction using a single catalyst of copper foil. Through precise tuning of the evaporation temperature of ethanol, creation of a method for seamlessly bonding CNTs on the graphene surface without assistance of metal particles has been realized. A study of the growth mechanism shows that the self-limited surface reaction of copper foil leads to many defects in multilayer graphene films, which provides the nucleation sites for the seamless growth of CNTs. The as-grown graphene/CNT heterojunctions have a higher electrical conductivity than the single graphene film, and its optical transmittance is almost equal to that of graphene film.

The traditional estimation methods of space targets pose are based on artificial features to match the transformation relationship between the image and the object model. With the explosion of deep learning technology, the approach based on deep neural networks (DNN) has significantly improved the performance of pose estimation. However, the current methods still have problems such as complex calculation, low accuracy, and poor real-time performance. Therefore, a new pose estimation algorithm is proposed in this paper. Firstly, the mask image of the target is obtained by the instance segmentation algorithm, and its point cloud image is obtained based on a depth map combined with camera parameters. Finally, the correlation among points is established to realize the prediction of pose based on multi-modal feature fusion. Experimental results in the YCB-Video dataset show that the proposed algorithm can recognize complex images at a speed of about 24 images per second with an accuracy of more than 80%. In conclusion, the proposed algorithm can realize fast pose estimation for complex stacked objects and has strong stability for different objects.

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance.

Superlubricity can be unstable in graphene systems, especially under high applied loads. Here the authors use microspheres uniformly coated by graphene to measure friction between 2D materials and show that superlow friction is preserved for long periods of time under high loads and various atmospheres.

Graphene, with amazing physical and chemical properties, exhibits great potential for next-generation electronic devices. Promising achievements were obtained in recent years. Nevertheless, there are challenges before the industrialization of graphene-based electronic devices (G-EDs), which present opportunities as well. Mass-production of graphene and the growing G-EDs are the major issues. In this perspective, we briefly outline the notable advances in the production of graphene and the development of diverse G-EDs. Then we probe into the critical challenges on the way of G-EDs and provide corresponding strategies. Finally, we give our expectations of G-EDs in the near future.

Multidrug resistance in Pseudomonas aeruginosa is a noticeable and ongoing major obstacle for inhibitor design. In P. aeruginosa, uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) acetyltransferase (PaLpxA) is an essential enzyme of lipid A biosynthesis and an attractive drug target. PaLpxA is a homotrimer, and the binding pocket for its substrate, UDP-GlcNAc, is positioned between the monomer A−monomer B interface. The uracil moiety binds at one monomer A, the GlcNAc moiety binds at another monomer B, and a diphosphate form bonds with both monomers. The catalytic residues are conserved and display a similar catalytic mechanism across orthologs, but some distinctions exist between pocket sizes, residue differences, substrate positioning and specificity. The analysis of diversified pockets, volumes, and ligand positions was determined between orthologues that could aid in selective inhibitor development. Thenceforth, a complex-based pharmacophore model was generated and subjected to virtual screening to identify compounds with similar pharmacophoric properties. Docking and general Born-volume integral (GBVI) studies demonstrated 10 best lead compounds with selective inhibition properties with essential residues in the pocket. For biological access, these scaffolds complied with the Lipinski rule, no toxicity and drug likeness properties, and were considered as lead compounds. Hence, these scaffolds could be helpful for the development of potential selective PaLpxA inhibitors.