ObjectiveTo analyze the diversity and structural characteristics of microbial communities in the rhizosphere soil of healthy and diseased Andrographis paniculata and to explore the interactions of soil， plants， and microorganisms during the occurrence of diseases. MethodsThe physicochemical properties of the rhizosphere soil of healthy and diseased A.paniculata were determined， and the composition and diversity of bacterial and fungal communities in the rhizosphere soil were analyzed by Illumina high-throughput sequencing. Furthermore， the correlations between physicochemical properties and microorganisms of the rhizosphere soil were explored. ResultsThe content of total nitrogen， total potassium， and available potassium in the rhizosphere soil of diseased A. paniculata was significantly higher than that of healthy A. paniculata. The alpha diversity and richness （operational taxonomic units） of bacterial and fungal communities in the rhizosphere soil of diseased plants decreased compared with those of healthy plants. The microbial communities in the rhizosphere soil of healthy and diseased A. paniculata showed similar composition but different relative abundance. At the phylum level， the relative abundance of Proteobacteria and Chytridiomycota significantly increased， while that of Bacteroidota significantly decreased in the rhizosphere soil of diseased plants. At the genus level， the relative abundance of Sphingomonas， Pseudomonas， and Bryobacter significantly increased， while that of RB41 showed a significant decrease in the rhizosphere soil of diseased plants. The correlation analysis showed different correlations of microbial phyla with physicochemical properties of the rhizosphere soil between healthy and diseased plants. Organic matter， alkaline nitrogen， available phosphorus， and total potassium were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of healthy plants， while available nitrogen and total phosphorus were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of diseased plants. ConclusionThere are differences in the diversity and richness of microbial communities in the rhizosphere soil of healthy and diseased A. paniculata. The physicochemical properties of soil may have an impact on the rhizosphere microorganisms of A. paniculata， leading to the development of diseases. The results provide a scientific basis for the prevention and ecological management of A. paniculata diseases.

ObjectiveTo analyze the diversity and structural characteristics of microbial communities in the rhizosphere soil of healthy and diseased Andrographis paniculata and to explore the interactions of soil， plants， and microorganisms during the occurrence of diseases. MethodsThe physicochemical properties of the rhizosphere soil of healthy and diseased A.paniculata were determined， and the composition and diversity of bacterial and fungal communities in the rhizosphere soil were analyzed by Illumina high-throughput sequencing. Furthermore， the correlations between physicochemical properties and microorganisms of the rhizosphere soil were explored. ResultsThe content of total nitrogen， total potassium， and available potassium in the rhizosphere soil of diseased A. paniculata was significantly higher than that of healthy A. paniculata. The alpha diversity and richness （operational taxonomic units） of bacterial and fungal communities in the rhizosphere soil of diseased plants decreased compared with those of healthy plants. The microbial communities in the rhizosphere soil of healthy and diseased A. paniculata showed similar composition but different relative abundance. At the phylum level， the relative abundance of Proteobacteria and Chytridiomycota significantly increased， while that of Bacteroidota significantly decreased in the rhizosphere soil of diseased plants. At the genus level， the relative abundance of Sphingomonas， Pseudomonas， and Bryobacter significantly increased， while that of RB41 showed a significant decrease in the rhizosphere soil of diseased plants. The correlation analysis showed different correlations of microbial phyla with physicochemical properties of the rhizosphere soil between healthy and diseased plants. Organic matter， alkaline nitrogen， available phosphorus， and total potassium were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of healthy plants， while available nitrogen and total phosphorus were correlated with the relative abundance of some dominant bacterial and fungal phyla in the rhizosphere soil of diseased plants. ConclusionThere are differences in the diversity and richness of microbial communities in the rhizosphere soil of healthy and diseased A. paniculata. The physicochemical properties of soil may have an impact on the rhizosphere microorganisms of A. paniculata， leading to the development of diseases. The results provide a scientific basis for the prevention and ecological management of A. paniculata diseases.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Objective:To strengthen the management of low-value consumables in public hospitals by introducing the Supply Processing Distribution(SPD)management model,and to explore refined operational management strategies and path optimization for low-value consumables.Methods:The SPD management model was introduced,and the entire process of hospital consumables was refinedly managed using third-party supply chain information management platforms,visualized tertiary department warehouses,Radio Frequency Identification(RFID)technology and intelligent cabinet systems,Unique Device Identification(UDI)coding,"four-code integration"and other supporting technologies.Results:Based on the analysis of the current situation in the target Hospital,specific measures related to the management of low-value consumables were introduced after the introduction of the SPD model.Conclusion:It provides a reference and guidance for the hospital's medical consumables management department to promote refined management of medical consumables under the SPD model.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Objective:To strengthen the management of low-value consumables in public hospitals by introducing the Supply Processing Distribution(SPD)management model,and to explore refined operational management strategies and path optimization for low-value consumables.Methods:The SPD management model was introduced,and the entire process of hospital consumables was refinedly managed using third-party supply chain information management platforms,visualized tertiary department warehouses,Radio Frequency Identification(RFID)technology and intelligent cabinet systems,Unique Device Identification(UDI)coding,"four-code integration"and other supporting technologies.Results:Based on the analysis of the current situation in the target Hospital,specific measures related to the management of low-value consumables were introduced after the introduction of the SPD model.Conclusion:It provides a reference and guidance for the hospital's medical consumables management department to promote refined management of medical consumables under the SPD model.

Objective To explore and validate the application value of a deep learning model based on the Huawei Cloud ModelArts platform in the diagnosis of atypical cervical cells in liquid-based cytology(LBC)and to evaluate its assistive effect for pathologists with different diagnostic experiences.Methods We retrospectively analyzed 1 044 cervical cytology specimens from Dongguan People's Hospital in 2020.The artifical intelligence(AI)-assisted diagnostic system developed on the Huawei Cloud ModelArts platform was compared with junior,intermediate,and senior pathologists for diagnosis.Sensitivity,specificity,precision,recall,and area under the receiver operating characteristic curve(AUC)were calculated to assess the diagnostic performance of the Al system and its assistive effect for pathologists with different levels of experience.The McNemar test was used to compare the differences between the Al system and manual diagnosis.P＜0.05 was considered statistically significant.Results For the 1 044 cervical exfoliated cytology specimens,the sensitivity and specificity of the AI system in detecting atypical cells was 98.96％and 89.15％,both of which were higher than those of junior doctors(81.95％and 91.81％,respectively).The overall diagnostic accuracy of the Al system was 93.68％,which was significantly higher than that of junior doctors(87.26％,P＜0.001).Al assistance could significantly improve junior doctors'ability to detect atypical cells,with the sensitivity and specificity increasing from 80.1％to 96.5％and from 85.6％to 92.3％,respectively.Conclusion The AI-assisted cervical cytology diagnostic system developed in this study demonstrated superior performance,particularly in significantly improving the diagnostic level of junior pathologists,showing promising clinical application prospects.

【Objective】 To investigate the role and mechanism of dapagliflozin (Dapa), a sodium glucose co-transporter 2 inhibitor, in acute liver injury. 【Methods】 Eight-week-old C57BL6/J mice were given a single intraperitoneal injection of CCl4 to induce acute liver injury. The mice were preventively given 5 mg/kg Dapa by gavage 24 h and 2 h before CCl4 injection, while those in the control group were given an equal volume of solvent gavage. After 24 h, the mice were anesthetized and sacrificed. H&E staining, plasma biochemistry, RT-qPCR, and Western blotting were used to detect the severity of liver injury and the expressions of macrophage-related genes. 【Results】 In the CCl4 group, hepatic infiltration of inflammatory cells increased, and liver and renal functions significantly deteriorated, which was further aggravated by Dapa. CCl4 could promote the expressions of M1 macrophages and fibrosis-related genes in the liver, but reduce those of M2 and antioxidant-related genes, and the latter was further inhibited by Dapa. In addition, the protein expression of arginase 1 decreased and that of SGLT2 increased after Dapa intervention, while NF-κB pathway did not change significantly, suggesting that Dapa might directly affect the energy metabolism homeostasis in the liver and aggravate acute liver injury induced by CCl4. 【Conclusion】 Dapa can exacerbate hepatic and renal damage in acute stage of liver injury, inhibit macrophages M2 polarization, and aggravate oxidative stress and inflammatory injury induced by CCl4.

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.
Humans ; Aged ; Arrhythmias, Cardiac/diagnosis* ; Ventricular Fibrillation ; Electrocardiography ; Accidental Falls ; Vital Signs ; Posture ; Monitoring, Physiologic

Objective To investigate the relationship between lesion size of solitary bone cyst ( SBC) and pathological fracture of calcaneus, so as to provide references for the treatment of SBC. Methods The three dimensional (3D) finite element model of foot and ankle was established based on CT images. Four models with gradient spherical bone defects were constructed in the focal area to simulate different SBC lesion sizes, and the biomechanical characteristics of calcaneus in different gait phases were analyzed. Results With the increasement of SBC size, the kinematics of calcaneus did not change significantly, but the peak stress of calcaneus increased gradually. When the SBC size exceeded 75% of the calcaneal width, the stress in calcaneal sulcus and cortical bone below SBC increased by 1. 48 times and 7. 74 times, respectively. Conclusions The risk of pathological fracture increases when the SBC diameter exceeds 75% of the calcaneal width, and early surgical intervention should be recommended. The calcaneal sulcus and the cortex bone below SBC are stress concentration regions and can be used as important areas to evaluate pathological fractures.