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AbstractLiver fibrosis is a key pathological process shared by the progression of various chronic liver diseases. Treatment of liver fibrosis can effectively block the occurrence and development of hepatic cirrhosis or even carcinoma. Currently, there is no effective drug delivery vehicle for curing liver fibrosis. In this study, we designed matrine (MT)-loaded mannose 6-phosphate (M6P) modified human serum albumin (HSA) conjugated solid lipid nanoparticles (SLN), named M6P-HSA-MT-SLN for treatment of hepatic fibrosis. We demonstrated that M6P-HSA-MT-SLN exhibited controlled and sustained release properties and good stability over 7 days. The drug release experiments showed that M6P-HSA-MT-SLN exhibited slow and controlled drug release characteristics. In addition, M6P-HSA-MT-SLN showed a significant targeted ability to fibrotic liver. Importantly, in vivo studies indicated that M6P-HSA-MT-SLN could significantly improve histopathological morphology and inhibit the fibrotic phenotype. In addition, in vivo experiments demonstrate that M6P-HSA-MT-SLN could reduce the expression of fibrosis markers and alleviate the damage of liver structure. Hence, the M6P-HSA-MT-SLN provide a promising strategy to deliver therapeutic agents to fibrotic liver to prevent liver fibrosis.

We propose and demonstrate a hybrid communication architecture that combines millimeter-wave (MMW) in the radio frequency (RF) domain and free-space-optics (FSO) technologies using adaptive combining and hybrid automatic repeat request (HARQ) techniques. At the receiving end, we employed joint signal processing with an adaptive diversity combining technique (ADCT) based on a maximum ratio combining (MRC) algorithm. We derived closed-form expressions for the outage probability and throughput of the hybrid RF and FSO (RF/FSO) system, considering various characteristics of atmospheric turbulence in the FSO link. Experimental testing with 10-Gbaud quadrature phase shift keying (QPSK) data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratios, and forward error correction (FEC) overheads. Additionally, we validated improvements in terms of bit error ratio (BER), outage probability, and throughput performance.

The explosive development of emerging telecommunication services has stimulated a huge growth in bandwidth demand as people seek universal access to telecommunication networks. In addition, the kinds of services of an existing optical access network are becoming more flexible. In order to provide higher capacity and meet higher transmission performance requirements, it is necessary to further explore the application of the beyond-100G passive optical network (PON). This paper offers a comprehensive review and outline of the prospects of technologies for bringing a beyond-100G PON to practical applications in the future. We review the current existing technologies, mainly in terms of the physical layer and higher media access control layer. These key technologies for the beyond-100G PON, which plays an increasingly significant role, include the advanced multiplexing technology, physical layer digital signal processing technology, infrastructure-sharing technology, security protection technology, and intelligent control management key technologies. Finally, open issues and new challenges for the next-generation PON are focused upon.

The prevalence of obesity has reached alarming levels, which is considered a major risk factor for several metabolic diseases, including type 2 diabetes (T2D), non-alcoholic fatty liver, atherosclerosis, and ischemic cardiovascular disease. Obesity-induced chronic, low-grade inflammation may lead to insulin resistance, and it is well-recognized that macrophages play a major role in such inflammation. In the current review, the molecular mechanisms underlying macrophages, low-grade tissue inflammation, insulin resistance, and T2D are described. Also, the role of macrophages in obesity-induced insulin resistance is presented, and therapeutic drugs and recent advances targeting macrophages for the treatment of T2D are introduced.

Gout is a kind of joint disease characterized by the accumulation of monosodium urate (MSU) crystals in the joint and its surrounding tissue, causing persistent hyperuricemia. Colchicine is the first choice of treatment for acute gout attacks. Due to strong toxicity of colchicines oral tablets, there are high fluctuations of blood drug concentration and serious irritation of gastrointestinal tract, and hence a transdermal preparation can help to slow down the blood drug concentration, reduce the frequency of drug taking, and improve the patients' compliance of the drug. The ethosome is a lipid carrier with high concentration of ethanol and has been proved to promote the penetration of drugs into the skin. Borneol (BO) is an excellent penetration enhancer in Chinese medicine, which can promote the entry of drugs into the skin. This paper prepared the borneol-physically-modified colchicine ethosome (COL-bpES) and used the prepared borneol-dioleoyl phosphoethanloamine (BO-DOPE) to prepare borneol-chemically-modified colchicine ethosome (COL-bcES). Compared to the free colchicine aqueous solution (free COL) and normal colchicine ethosome (COL-ES), the borneol-modified colchicine ethosome (COL-bES) demonstrated better drug penetration effect, while the particle size of the COL-bcES was lower than that of the COL-bpES. Toxicity, in vitro diffusion, pharmacokinetics and pharmacodynamics are superior to those of COL-bpES, providing a better delivery system for the treatment of small molecule inflammatory drugs.

With the explosion of traffic demand in recent years, the integration of satellite optical networks and terrestrial networks (ISTN) creates a promising networking solution for future low-latency, high-rate, and high-capacity communications. Owing to the high cost of deploying and maintaining a satellite optical network, it is critical to carefully design and plan the network to ensure the performance of the network. Thus, a semi-physical simulation platform based on software-defined networks (SDNs) is developed to simulate a satellite optical network and evaluate the performance of the proposed label-based advanced orbiting system (AOS) frame switching method that adheres to the Consultative Committee for Space Data Systems’ recommended standard (CCSDS). The semi-physical simulation platform has two major innovations: (1) adapting and integrating network protocols between the CCSDS and open system interconnect (OSI) reference models, particularly at the data link layer, and (2) the foundation for an SDN-based satellite optical network. In the control plane, real-time VxWorks Simulators serve as controllers to establish and manage various network protocols and the link manager protocol (LMP). Here, network protocols include open shortest path first (OSPF) for routing managing and controlling messages, constraint shortest path first–traffic engineering (CSPF-TE), and constraint-label distribution protocol (CR-LDP) for routing data services. LMP is used to assign and reserve satellite optical link resources. The performance of the architecture and protocols is evaluated via a semi-physical simulation platform.

Dantrolene sodium (DS) is the only drug specifically used for the treatment of malignant hyperthermia. Nevertheless, its clinical application is significantly restricted due to its aqueous insolubility and the limited formulations available in clinical practice. In order to solve these problems, a novel mixed micelle composed of phospholipid and Cremophor EL was designed and evaluated. The mixed micelle was prepared using a stirring- ultrasonic method. The Dynamic Light Scattering (DLS) results showed that the micelle was small in size (12.14 nm) and narrowly distributed (PdI = 0.073). Transmission Electron Microscopy (TEM) images showed that the micelle was homogeneous and spherical. The stability study indicated that the system was stable for storage and dilution with distilled water, while the safety testing showed that the micelle was safe for intravenous administration with low hemolysis rates and low allergic reaction rates. In the pharmaceutics study, the Cmax and AUC0-t of the DS-loaded micelle were 4- and 4.5- folds higher than that of the DS. Therefore, the constructed phospholipid-Cremophor EL mixed micelle is a promising drug delivery system for DS.

Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR.

Landslide detection and distribution mapping are essential components of geohazard prevention. For the extremely difficult problem of automatic forested landslide detection, airborne remote sensing technologies, such as LiDAR and optical cameras, can obtain more accurate landslide monitoring data. In practice, however, airborne LiDAR data and optical images are treated independently. The complementary information of the remote sensing data from multiple sources has not been thoroughly investigated. To address this deficiency, we investigate how to use LiDAR data and optical images together to develop an automatic detection model for forested landslide detection. First, a new dataset for detecting forested landslides in the Jiuzhaigou earthquake region is compiled. LiDAR-derived DEM and hillshade maps are used to mitigate the influence of forest cover on the detection of forested landslides. Second, a new deep learning model called DemDet is proposed for the automatic detection of forested landslides. In the feature extraction component of DemDet, a self-supervised learning module is proposed for extracting geometric features from LiDAR-derived DEM. Additionally, a transformer-based deep neural network is proposed for identifying landslides from hillshade maps and optical images. In the data fusion component of DemDet, an attention-based neural network is proposed to combine DEM, hillshade, and optical images. DemDet is able to extract key features from hillshade images, optical images, and DEM, as demonstrated by experimental results on the proposed dataset. In comparison to ResUNet, LandsNet, HRNet, MLP, and SegFormer, DemDet obtains the highest mean accuracy, mIoU, and F1 values, namely 0.95, 0.67, and 0.777. DemDet is therefore capable of autonomously identifying the forest-covered landslides in the Jiuzhaigou earthquake zone. The results of landslide detection mapping reveal that slopes along roads and seismogenic faults are the most crucial areas requiring geohazard prevention.

Atmospheric effects are among the primary error sources affecting the accuracy of interferometric synthetic aperture radar (InSAR). The topography-dependent atmospheric effect is particularly noteworthy in reservoir areas for landslide monitoring utilizing InSAR, which must be effectively corrected to complete the InSAR high-accuracy measurement. This paper proposed a topography-dependent atmospheric correction method based on the Multi-Layer Perceptron (MLP) neural network model combined with topography and spatial data information. We used this proposed approach for the atmospheric correction of the interferometric pairs of Sentinel-1 images in the Baihetan dam. We contrasted the outcomes with those obtained using the generic atmospheric correction online service for InSAR (GACOS) correction and the traditional linear model correction. The results indicated that the MLP neural network model correction reduced the phase standard deviation of the Sentinel-1 interferogram by an average of 64% and nearly eliminated the phase-elevation correlation. Both comparisons outperformed the GACOS correction and the linear model correction. Through two real-world examples, we demonstrated how slopes with displacements, which were previously obscured by a significant topography-dependent atmospheric delay, could be successfully and clearly identified in the interferograms following the correction by the MLP neural network. The topography-dependent atmosphere can be better corrected using the MLP neural network model suggested in this paper. Unlike the previous model, this proposed approach could be adjusted to fit each interferogram, regardless of how much of the topography-dependent atmosphere was present. In order to improve the effectiveness of DInSAR and time-series InSAR solutions, it can be applied immediately to the interferogram to retrieve the effective displacement information that cannot be identified before the correction.

Earthquake-triggered landslides frequently occur in active mountain areas, which poses great threats to the safety of human lives and public infrastructures. Fast and accurate mapping of coseismic landslides is important for earthquake disaster emergency rescue and landslide risk analysis. Machine learning methods provide automatic solutions for landslide detection, which are more efficient than manual landslide mapping. Deep learning technologies are attracting increasing interest in automatic landslide detection. CNN is one of the most widely used deep learning frameworks for landslide detection. However, in practice, the performance of the existing CNN-based landslide detection models is still far from practical application. Recently, Transformer has achieved better performance in many computer vision tasks, which provides a great opportunity for improving the accuracy of landslide detection. To fill this gap, we explore whether Transformer can outperform CNNs in the landslide detection task. Specifically, we build a new dataset for identifying coseismic landslides. The Transformer-based semantic segmentation model SegFormer is employed to identify coseismic landslides. SegFormer leverages Transformer to obtain a large receptive field, which is much larger than CNN. SegFormer introduces overlapped patch embedding to capture the interaction of adjacent image patches. SegFormer also introduces a simple MLP decoder and sequence reduction to improve its efficiency. The semantic segmentation results of SegFormer are further improved by leveraging image processing operations to distinguish different landslide instances and remove invalid holes. Extensive experiments have been conducted to compare Transformer-based model SegFormer with other popular CNN-based models, including HRNet, DeepLabV3, Attention-UNet, U2Net and FastSCNN. SegFormer improves the accuracy, mIoU, IoU and F1 score of landslide detectuin by 2.2%, 5% and 3%, respectively. SegFormer also reduces the pixel-wise classification error rate by 14%. Both quantitative evaluation and visualization results show that Transformer is capable of outperforming CNNs in landslide detection.