A new kind of superhydrophilic drug-carrying coating was synthesized from dopamine and rapamycin to prevent nasolacrimal duct from obstructing through anti-inflammation, anti-infection and anti-fibrosis regulation. This research provides a versatile surface bioengineering strategy.
The delineation of tumor target and organs-at-risk is critical in the radiotherapy treatment planning. Automatic segmentation can be used to reduce the physician workload and improve the consistency. However, the quality assurance of the automatic segmentation is still an unmet need in clinical practice. The patient data used in our study was a standardized dataset from AAPM Thoracic Auto-Segmentation Challenge. The OARs included were left and right lungs, heart, esophagus, and spinal cord. Two groups of OARs were generated, the benchmark dataset manually contoured by experienced physicians and the test dataset automatically created using a software AccuContour. A resnet-152 network was performed as feature extractor, and one-class support vector classifier was used to determine the high or low quality. We evaluate the model performance with balanced accuracy, F-score, sensitivity, specificity and the area under the receiving operator characteristic curve. We randomly generated contour errors to assess the generalization of our method, explored the detection limit, and evaluated the correlations between detection limit and various metrics such as volume, Dice similarity coefficient, Hausdorff distance, and mean surface distance. The proposed one-class classifier outperformed in metrics such as balanced accuracy, AUC, and others. The proposed method showed significant improvement over binary classifiers in handling various types of errors. Our proposed model, which introduces residual network and attention mechanism in the one-class classification framework, was able to detect the various types of OAR contour errors with high accuracy. The proposed method can significantly reduce the burden of physician review for contour delineation.
With the development of Marine economy, underwater acoustic sensor networks (UASNs), as an important means to acquire Marine information, will be more and more important. The design of efficient and reliable routing protocols to improve UASNs' performance has become a current research focus. In this paper, we propose a clustering guiding-network based routing (CGNBR) protocol for large scale UASNs, where K-Means clustering algorithm is used to improve the existing opportunity routing protocol's guiding-network transmission mechanism. After clustering the guiding-network, when the network is abnormal, it only needs to rebuild the failed guiding-network cluster instead of the entire guiding-network, which can reduce the transmission delay and energy consumption caused by the network rebuilding. The cluster head node is responsible for the packet transmission when the cluster is abnormal, which reduces the transmission delay and improves the packet delivery ratio. Simulation results show that the proposed CGNBR method can reduce transmission delay and network energy consumption, while improving the packet delivery ratio.
Osteoarthritis (OA) is a degenerative joint disease characterized by the progressive degeneration of articular cartilage, leading to pain, stiffness, and loss of joint function. The pathogenesis of OA involves multiple factors, including increased intracellular reactive oxygen species (ROS), enhanced chondrocyte apoptosis, and disturbances in cartilage matrix metabolism. These processes contribute to the breakdown of the extracellular matrix (ECM) and the loss of cartilage integrity, ultimately resulting in joint damage and dysfunction. RNA interference (RNAi) therapy has emerged as a promising approach for the treatment of various diseases, including hATTR and acute hepatic porphyria. By harnessing the natural cellular machinery for gene silencing, RNAi allows for the specific inhibition of target genes involved in disease pathogenesis. In the context of OA, targeting key molecules such as matrix metalloproteinase-13 (MMP13), which plays a critical role in cartilage degradation, holds great therapeutic potential. In this study, we developed an innovative therapeutic approach for OA using a combination of liposome-encapsulated siMMP13 and NG-Monomethyl-L-arginine Acetate (L-NMMA) to form an injectable hydrogel. The hydrogel served as a delivery vehicle for the siMMP13, allowing for sustained release and targeted delivery to the affected joint. Experiments conducted on destabilization of the medial meniscus (DMM) model mice demonstrated the therapeutic efficacy of this composite hydrogel. Treatment with the hydrogel significantly inhibited the degradation of cartilage matrix, as evidenced by histological analysis showing preserved cartilage structure and reduced loss of proteoglycans. Moreover, the hydrogel effectively suppressed intracellular ROS accumulation in chondrocytes, indicating its anti-oxidative properties. Furthermore, it attenuated chondrocyte apoptosis, as demonstrated by decreased levels of apoptotic markers. In summary, the injectable hydrogel containing siMMP13, endowed with anti-ROS and anti-apoptotic properties, may represent an effective therapeutic strategy for osteoarthritis in the future.
Abstract The output power of the wind-solar energy storage hybrid power generation system encounters significant fluctuations due to changes in irradiance and wind speed during grid-connected operation when using traditional control methods. This situation affects the power balance when connecting the power generation system with the electrical grid. In order to address this issue, a novel improved Perturb and Observe (P&O) method by fuzzy control algorithms is proposed to achieve tracking control of the maximum power point (MPPT) of the photovoltaic array under irradiance variations. Then, an optimal tip-speed ratio control strategy is employed to achieve maximum power point tracking control of the wind turbine under wind speed fluctuations. Charging and discharging of the batteries are controlled in real time based on the balance between power generation and grid power demand. In this way, grid voltage stability and power balance are maintained. Finally, to analyze the output power of each system, a combined wind-solar energy storage generation system model is established. It is evident from the results that the proposed scheme enables the power generation system to stably transmit electrical energy in a volatile wind-solar environment.
Peptide hydrogels are highly hydrophilic, three-dimensional network gels formed by the self-assembly of nanofibers or polymers, creating water-locking networks. Their morphology closely resembles that of the extracellular matrix, allowing them to exhibit both the biological functions of peptides and responsive gelation properties. These unique characteristics have led to their extensive application in tissue engineering, three-dimensional cell culture, cancer therapy, regenerative medicine, and other biomedical fields. This article describes three methods for preparing ECF-5 peptide hydrogels using self-assembling peptides with environmentally responsive gelation processes: (1) pH-responsive gelation: varying pH levels induce the protonation or deprotonation of amino acid residues, altering electrostatic interactions between peptide molecules and promoting their self-assembly into hydrogels; (2) Metal ion addition: polyvalent metal ions chelate with negatively charged amino acid residues, acting as bridges between peptides to form a network hydrogel; (3) Solvent exchange: hydrophobic peptides are initially dissolved in non-polar organic solvents and subsequently induce self-assembly into hydrogels upon transitioning to a polar aqueous environment. These methods utilize conventional experimental procedures to facilitate peptide self-assembly into hydrogels. By designing peptide sequences to align with specific gelation-inducing conditions, it is possible to achieve finely tuned micro/nanostructures and biological functions, highlighting the significant potential of peptide hydrogels in the biomedical domain.
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