The plants of the genus Caragana Fabr. are desert plants of the Leguminosae family, which are widely used in traditional ethnomedicine, with the effects of nourishing Yin and nourishing blood, dispelling wind and removing dampness, clearing heat and detoxifying toxins. The anti-inflammatory effect of Caragana Fabr. has attracted much attention, the research on the related mechanism has made some progress, but it lacks systematic collation. By summarising the anti-inflammatory effects of the active ingredients of Caragana Fabr., the authors found that they have good anti-inflammatory activities on different inflammatory cell models in vitro, and have good improvement effects on rheumatoid arthritis, colitis, complex nephritis, and acute lung injury and other disease models. The anti-inflammatory effects of the active components of this genus mainly include the reduction of the levels of a variety of pro-inflammatory factors, and the signalling pathways involved mainly include TLR4/NF-κB, TLR4/MAPK, TLR4/NF-κB/IRF3, JAK/STAT-1, ERK/STAT-1 and so on. Therefore, the paper mainly reviews the research progress on the anti-inflammatory effects and mechanisms of the Caragana Fabr. plants and their active components according to disease types, with a view to providing reference for the in-depth study of their anti-inflammatory activities and the development of new products with related functions.

Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.
Humans ; Electroencephalography/methods* ; Neural Networks, Computer ; Fatigue/physiopathology* ; Automobile Driving ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

OBJECTIVES: To construct a bone tumor classification model based on feature decoupling and fusion for processing modality loss and fusing multimodal information to improve classification accuracy. METHODS: A decoupling completion module was designed to extract local and global bone tumor image features from available modalities. These features were then decomposed into shared and modality-specific features, which were used to complete the missing modality features, thereby reducing completion bias caused by modality differences. To address the challenge of modality differences that hinder multimodal information fusion, a cross-attention-based fusion module was introduced to enhance the model's ability to learn cross-modal information and fully integrate specific features, thereby improving the accuracy of bone tumor classification. RESULTS: The experiment was conducted using a bone tumor dataset collected from the Third Affiliated Hospital of Southern Medical University for training and testing. Among the 7 available modality combinations, the proposed method achieved an average AUC, accuracy, and specificity of 0.766, 0.621, and 0.793, respectively, which represent improvements of 2.6%, 3.5%, and 1.7% over existing methods for handling missing modalities. The best performance was observed when all the modalities were available, resulting in an AUC of 0.837, which still reached 0.826 even with MRI alone. CONCLUSIONS The proposed method can effectively handle missing modalities and successfully integrate multimodal information, and show robust performance in bone tumor classification under various complex missing modality scenarios.
Humans ; Bone Neoplasms/diagnosis* ; Multimodal Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Image Processing, Computer-Assisted/methods* ; Algorithms

Objective To investigate the prevalence of Echinococcus infections in small rodents around human residential areas in Yushu City, Qinghai Province in 2023, so as to provide insights into precision echinococcosis control. Methods One or two quadrats, each measuring 50 m × 50 m, were randomly assigned in Shanglaxiu Township and Longbao Township, Yushu City, Qinghai Province on June 2023, respectively, and 300 plate-type mouse traps, each measuring 12.0 cm × 6.5 cm, were assigned in each quadrat. Small rodents were captured during the period between 10 : 00 and 18 : 00 each day for 4 days. Then, all captured small rodents were identified and dissected, and liver specimens with suspected Echinococcus infections were subjected to pathological examinations. The Echinococcus cytochrome c oxidase 1 (cox1) gene was amplified using PCR assay, and the sequence of the amplified product was aligned to that was recorded in the GenBank to characterize the parasite species. In addition, a phylogenetic tree of Echinococcus was generated based on the cox1 gene sequence using the neighbor-joining method. Results A total of 236 small rodents were captured in Shanglaxiu and Longbao townships, Yushu City, including 65 Qinghai voles and 51 plateau pikas in Shanglaxiu Township, and 62 Qinghai voles and 58 plateau pikas in Longbao Township, and there was no significant difference in the constituent ratio of small rodents between the two townships (χ² = 0.294, P > 0.05). Seven plateau pikas and 12 Qinghai voles were suspected to be infected with Echinococcus by dissection, and pathological examinations showed unclear structure of hepatic lobules and disordered hepatocyte arrangement in livers of small rodents suspected of Echinococcus infections. PCR assay identified E. shiquicus DNA in 7 Qinghai voles, which were all captured from Shanglaxiu Township. Phylogenetic analysis showed that the cox1 gene sequence of Echinococcus in small rodents was highly homologous to the E. shiquicus cox1 gene sequence reported previously. Conclusion Plateau pika and Qinghai vole were predominant small rodents around human residential areas in Yushu City, Qinghai Province in 2023, and E. shiquicus infection was detected in Qinghai voles.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.

High-quality development of digital health industry is an important symbol of realizing a healthy and digital China,and it is a new economic growth point.Using literature and other research methods,it comprehensively explores the theoretical logic,practical bottlenecks and solutions to the high-quality development of digital health industry driven by new quality productivity.It concludes that the new quality productivity can promote the high-quality development of digital health industry by strengthening the effect of institutional innovation,creating a good development environment for the industry;strengthening the effect of structural optimization,driving the upgrading of the industry;strengthening the effect of strengthening and complementing the chain,prolonging the industrial value chain;strengthening the effect of organizational remodeling,improving the industrial organizational system;and strengthening the effect of factor enhancement,fostering the high-end innovation factors.Aiming at the bottlenecks of unsound institutional mechanism,deep structural traps,low-end locking of the value chain,weak integration capacity of health organizations,and insufficient allocation of innovative elements,proposing to promote the deep integration of new elements and resources,and to accumulate the kinetic energy of innovation and development of the digital health industry;strengthening the status of digital health enterprise as the main body of innovation,and to stimulate the vitality of health management organizations;vertically and horizontally integrating the digital health industry chain,and comprehensively excavating the value of the digital health industry;driving the digital health industry to change the new and new,rooting the foundation of digital health industry development;deepening the reform of the digital health management system and mechanism,and improving the system to ensure the relief of the system.