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 investigate the current status and influencing factors of barriers to going out among elderly patients with chronic obstructive pulmonary disease (COPD), and to provide a reference for implementing targeted preventive measures.Methods:Elderly COPD patients from the People's Hospital of Xinjiang Uygur Autonomous Region were selected between January and May 2024 by convenience sampling. A cross-sectional survey was conducted using a general information questionnaire, Scale on Barriers to Going Out for the Elderly, Perceived Isolation Scale and COPD Assessment Test (CAT).Results:A total of 270 questionnaires were distributed, with 250 valid responses, the effective response rate was 92.6%. Among the 250 elderly COPD patients, there were 124 males and 126 females, with age distributions: 116 patients aged 60-69 years, 94 aged 70-79 years, and 40 aged ≥80 years. The total score for Scale on Barriers to Going Out for the Elderly was (20.01 ± 4.09). The score on barriers to going out in elderly COPD patients were positively correlated with the Perceived Isolation Scale and CAT scores ( r = 0.456 and 0.625, both P <0.05). Multiple linear regression analysis revealed that age, oxygen therapy, exercise habits,Perceived Isolation Scale, and CAT score classification were the main influencing factors for barriers to going out ( t values were -2.85 to 8.93, all P<0.05), explaining 63.0% of the total variance. Conclusions:The level of barriers to going out in elderly COPD patients is moderate-to-high level. Healthcare professionals should emphasize the assessment of barriers to going out, closely monitor high-risk groups, and develop and implement interventions to prevent such barriers.

Objective To explore the application effect of multimodal somatosensory exercise based on artificial intelligence technology in home rehabilitation exercise for elderly patients with chronic obstructive pulmonary dis-ease(COPD),so as to promote COPD patients to participate in home rehabilitation exercise.Methods Using the convenient sampling method,80 elderly patients with COPD admitted to the Department of Respiratory Medicine of a tertiary A hospital in Urumqi from November 2023 to February 2024 were selected as the research subjects.Ac-cording to the random number table method,they were divided into a control group and an experimental group,with 40 cases in each group.The control group adopted the traditional exercise training method,and the experimental group adopted the multi-modal somatosensory movement based on artificial intelligence technology for exercise in-tervention,with 5 times a week,and the intervention was implemented for 12 weeks.The pulmonary function index,modified Medical Research Council scale score,physical fitness level,Chronic Obstructive Pulmonary Disease As-sessment Test scale score and exercise compliance of the 2 groups were compared before intervention and 12 weeks after intervention.Results 77 patients completed the study,with 39 in the experimental group and 38 in the control group.The forced vital capacity,forced expiratory volume in one second,forced expiratory volume in one second to forced vital capacity ratio,physical fitness level and exercise compliance of the experimental group were higher than those of the control group,while the modified British Medical Research Council scale score and Chron-ic Obstructive Pulmonary Disease Assessment Test score were lower than those of the control group.The differences were statistically significant(P＜0.05).Conclusion Somatosensory exercise based on artificial intelligence technology can improve the lung function of the patients with COPD,improve the exercise compliance and physical fitness in-dicators of elderly patients and improve the quality of life of the patients.

Objective To construct a prediction model for the risk of peripherally inserted central venous catheter(PICC)-related bloodstream infection(CRBSI)in preterm infants,and evaluate the effect of the model.Methods 1 295 preterm infants admitted to the neonatal intensive care unit(NICU)in a hospital and received PICC intrave-nous infusion from January 2019 to October 2023 were selected as the study subjects,including 1 080 preterm in-fants from January 2019 to December 2022 in the modeling set and 215 premature infants from January to October 2023 in the validation set.Risk factors of cases were analyzed based on 24 clinical characteristics,optimized charac-teristics was selected by LASSO regression,independent risk factors for CRBSI of preterm infants during PICC in-dwelling period were identified by multiple logistic regression analysis,and nomogram model was constructed with R software.Discrimination and fitting of the model were evaluated by the area under the curve(AUC)of the receiver operating characteristic(ROC)as well as Hosmer-Lemeshow test and calibration curve,and clinical practicality of the model was evaluated by decision curve analysis(DCA).Results Multivariate logistic analysis showed that birth weight ≥1 500 g,sterile protection during catheter maintenance,and sterile cloth wrapped joints were protective factors for CRBSI during PICC indwelling period in preterm infants(OR=0.172,0.187,0.063,respectively,all P＜0.05),while puncture frequency＞2 times,catheter indwelling period＞14 days,and use of tees were inde-pendent risk factors for CRBSI during PICC indwelling period in premature infants(OR=2.533,14.128,13.256,respectively,all P＜0.05).The AUC of ROC of the modeling set was 0.953(95％CI:0.936-0.969),and that of the validation set was 0.930(95％CI:0.885-0.974),indicating good discriminability of the model.The calibra-tion curve and Hosmer-Lemeshow goodness of fit test showed that the model had good accuracy and consistency,with high net profit value,indicating that the predictive value of the model was high and with good clinical practica-lity.The statistical test result in the rationality analysis of the model was P＜0.001.Conclusion The nomogram model based on the general clinical characteristics of preterm infants as well as the basic prevention and control measures of the catheter can provide a visual and simple evaluation tool for early identification of high risk factors for CRBSI in preterm infants.

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 construct a prediction model for the risk of peripherally inserted central venous catheter(PICC)-related bloodstream infection(CRBSI)in preterm infants,and evaluate the effect of the model.Methods 1 295 preterm infants admitted to the neonatal intensive care unit(NICU)in a hospital and received PICC intrave-nous infusion from January 2019 to October 2023 were selected as the study subjects,including 1 080 preterm in-fants from January 2019 to December 2022 in the modeling set and 215 premature infants from January to October 2023 in the validation set.Risk factors of cases were analyzed based on 24 clinical characteristics,optimized charac-teristics was selected by LASSO regression,independent risk factors for CRBSI of preterm infants during PICC in-dwelling period were identified by multiple logistic regression analysis,and nomogram model was constructed with R software.Discrimination and fitting of the model were evaluated by the area under the curve(AUC)of the receiver operating characteristic(ROC)as well as Hosmer-Lemeshow test and calibration curve,and clinical practicality of the model was evaluated by decision curve analysis(DCA).Results Multivariate logistic analysis showed that birth weight ≥1 500 g,sterile protection during catheter maintenance,and sterile cloth wrapped joints were protective factors for CRBSI during PICC indwelling period in preterm infants(OR=0.172,0.187,0.063,respectively,all P＜0.05),while puncture frequency＞2 times,catheter indwelling period＞14 days,and use of tees were inde-pendent risk factors for CRBSI during PICC indwelling period in premature infants(OR=2.533,14.128,13.256,respectively,all P＜0.05).The AUC of ROC of the modeling set was 0.953(95％CI:0.936-0.969),and that of the validation set was 0.930(95％CI:0.885-0.974),indicating good discriminability of the model.The calibra-tion curve and Hosmer-Lemeshow goodness of fit test showed that the model had good accuracy and consistency,with high net profit value,indicating that the predictive value of the model was high and with good clinical practica-lity.The statistical test result in the rationality analysis of the model was P＜0.001.Conclusion The nomogram model based on the general clinical characteristics of preterm infants as well as the basic prevention and control measures of the catheter can provide a visual and simple evaluation tool for early identification of high risk factors for CRBSI in preterm infants.

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.

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 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.