Objective To verify the radiation shielding effects of a self-developed protective shield for infant bedside X-ray radiography, and measure the dose distribution in the indoor radiation field with and without the shield, and to provide guidance for the radiation protection of infants sharing the room. Methods A MUX-100J mobile medical diagnostic X-ray machine was used as the radiographic device to simulate the exposure conditions of infant bedside X-ray radiography. The ambient dose equivalent rates at measurement points in eight directions on the irradiation plane in the indoor radiation field were measured with and without the protective shield using an AT1121 X/γ dose rate meter under the conventional condition of 56 kV and the maximum single exposure time. Results During infant bedside X-ray radiography, the dose levels in all directions on the irradiation plane within 150 cm from the scatterer center were essentially symmetrically distributed. The ambient dose equivalent rate at 100 cm from the scatterer center on the irradiation plane was (8.01-8.28)×10³ μSv/h without the protective shield and 48-84 μSv/h with the shield. At various distances from the scatterer center in all directions on the irradiation plane, the doses measured with the protective shield ranged from 0.34% to 14.12% of those measured without the shield, with an average of 1.85%. Conclusion The dose levels in the indoor radiation field were significantly reduced with the protective shield during infant bedside X-ray radiography. The use of the protective shield can effectively reduce the radiation risk for individuals near the radiographic site within the same room.

Objective To verify the radiation shielding effects of a self-developed protective shield for infant bedside X-ray radiography, and measure the dose distribution in the indoor radiation field with and without the shield, and to provide guidance for the radiation protection of infants sharing the room. Methods A MUX-100J mobile medical diagnostic X-ray machine was used as the radiographic device to simulate the exposure conditions of infant bedside X-ray radiography. The ambient dose equivalent rates at measurement points in eight directions on the irradiation plane in the indoor radiation field were measured with and without the protective shield using an AT1121 X/γ dose rate meter under the conventional condition of 56 kV and the maximum single exposure time. Results During infant bedside X-ray radiography, the dose levels in all directions on the irradiation plane within 150 cm from the scatterer center were essentially symmetrically distributed. The ambient dose equivalent rate at 100 cm from the scatterer center on the irradiation plane was (8.01-8.28)×10³ μSv/h without the protective shield and 48-84 μSv/h with the shield. At various distances from the scatterer center in all directions on the irradiation plane, the doses measured with the protective shield ranged from 0.34% to 14.12% of those measured without the shield, with an average of 1.85%. Conclusion The dose levels in the indoor radiation field were significantly reduced with the protective shield during infant bedside X-ray radiography. The use of the protective shield can effectively reduce the radiation risk for individuals near the radiographic site within the same room.

Objective To verify the radiation shielding effects of a self-developed protective shield for infant bedside X-ray radiography, and measure the dose distribution in the indoor radiation field with and without the shield, and to provide guidance for the radiation protection of infants sharing the room. Methods A MUX-100J mobile medical diagnostic X-ray machine was used as the radiographic device to simulate the exposure conditions of infant bedside X-ray radiography. The ambient dose equivalent rates at measurement points in eight directions on the irradiation plane in the indoor radiation field were measured with and without the protective shield using an AT1121 X/γ dose rate meter under the conventional condition of 56 kV and the maximum single exposure time. Results During infant bedside X-ray radiography, the dose levels in all directions on the irradiation plane within 150 cm from the scatterer center were essentially symmetrically distributed. The ambient dose equivalent rate at 100 cm from the scatterer center on the irradiation plane was (8.01-8.28)×10³ μSv/h without the protective shield and 48-84 μSv/h with the shield. At various distances from the scatterer center in all directions on the irradiation plane, the doses measured with the protective shield ranged from 0.34% to 14.12% of those measured without the shield, with an average of 1.85%. Conclusion The dose levels in the indoor radiation field were significantly reduced with the protective shield during infant bedside X-ray radiography. The use of the protective shield can effectively reduce the radiation risk for individuals near the radiographic site within the same room.

ObjectiveTo construct and evaluate various machine learning models for predicting the risk of chronic obstructive pulmonary disease （COPD） in individuals， thereby providing data support for early screening and intervention. MethodsA total of 823 subjects were selected for this study， comprising 142 individuals in the high-risk group for COPD and 681 individuals in the low-risk group. Data collected included demographic characteristics， smoking history， symptoms （such as cough and shortness of breath）， and scores from the Chronic obstructive pulmonary disease screening questionnaire. Four machine learning algorithms—Logistic regression， random forest， support vector machine， and XGBoost—were utilized to construct risk prediction models. The performance of these models was assessed using 5-fold cross-validation， with evaluation metrics including accuracy， precision， recall， F1-score， area under the receiver operating characteristic curve （AUC）， and average precision （AP）. Furthermore， a feature importance analysis was performed. ResultsThe Logistic Regression model exhibited superior performance， achieving an AUC of 0.982 and an AP of 0.939. This was closely followed by the Random Forest model， which recorded an AUC of 0.975 and an AP of 0.890. Feature importance analysis revealed that smoking history， symptoms of shortness of breath， and body weight were significant predictors. All models demonstrated robust performance in identifying low-risk populations； however， variations were observed in their efficacy in identifying high-risk populations. ConclusionMachine learning models have proven effective in identifying individuals at high risk for COPD. Among these， the Logistic regression model exhibits the best overall performance， efficiently identifying high-risk populations and serving as a valuable clinical auxiliary screening tool. Various models， each with distinct performance characteristics， are suited to different clinical screening scenarios， thereby offering targeted decision-making support for the establishment of a hierarchical and intelligent COPD screening pathway.

Objective To introduce the calculation of the DISO index in multi-dimensional data ranking for disease surveillance, and evaluate its applicability. Methods Using the data from the quality supervision and inspection of infectious disease reporting in Jingzhou City in a certain year as an example, the DISO index was used to rank 16 investigated medical institutions and compared with the TOPSIS method. The consistency between the two evaluation results was analyzed using Spearman correlation. Then, The DISO index from different medical institutions was used to evaluate the ranking of 8 counties (cities, districts). The evaluation indicators for the supervision and inspection included six items: report rate, timely report rate, completeness of report card filling, accuracy of report card filling, consistency rate with network information report, and completeness of valid ID number filling. Results Among the 16 medical institutions, 8 institutions had all six evaluation indicators at 100%. Using the two methods to rank the 16 medical institutions, the two methods showed that the 8 medical institutions with all six evaluation indicators at 100% were ranked first. There was a strong correlation between the rankings of the 16 medical institutions using the two methods (r_s=0.992, P=0.000). The three counties (cities, districts) with all six evaluation indicators at 100% were all ranked first, while country G was ranked seventh and country E was ranked eighth. Conclusion The results of the DISO index and the TOPSIS method were similar in one-dimensional ranking. DISO can be used for multi-dimensional ranking of disease surveillance data and deserves to be promoted and applied.

AIM: To investigate the influence of relevant inflammatory markers in peripheral blood on the progression of neovascular glaucoma(NVG)secondary to diabetic retinopathy(DR)patients.METHODS: Retrospective case-control study. Patients were categorized into two groups based on the presence or absence of NVG: those with proliferative diabetic retinopathy(PDR)alone(PDR group, n=148)and those with NVG secondary to PDR(NVG secondary to PDR group, n=142). Peripheral blood inflammatory markers were evaluated, including white blood cell-related indices, neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), monocyte-to-lymphocyte ratio(MLR), and systemic immune-inflammation index(SII). The distinctions in peripheral blood inflammatory markers between the two groups of patients and their relationships with NVG secondary to PDR were analyzed.RESULTS:No statistically significant differences were observed in basic characteristics between the two groups, confirming their comparability. However, significant differences were found in eosinophil percentage and MLR between the PDR group and the NVG secondary to PDR group(all P<0.05), with both values being significantly higher in the NVG secondary to PDR group. Multivariate Logistic regression analysis revealed that the eosinophil percentage and the MLR were factors influencing the development of patients with NVG secondary to PDR.CONCLUSION: Eosinophil percentage and MLR may be associated with the progression of PDR to NVG, and could serve as potential predictive markers for NVG development in PDR patients.

Objective To analyze the incidence and mortality of gastric cancer in countries and territories with different human development index(HDI)levels in 2022,and to understand the burden of gastric cancer globally and in China.Methods Data on gastric cancer incidence and mortality were collected from GLOBOCAN 2022 and HDI data for all countries were obtained from the Human development report 2022.Spearman correlation was applied to examine the associations between the age-standardized incidence rate(ASIR),age-standardized mortality rate(ASMR),mortality-to-incidence ratio(M/I),and HDI for gastric cancer.The Wilcoxon rank-sum test was used to assess the differences in ASIR and ASMR between males and females.Results In 2022,gastric cancer ranked the 5th in both incidence and mortality among all cancer types globally.In China,gastric cancer ranked the 5th in incidence and the 3rd in mortality among all cancer types.The ASIR and ASMR of gastric cancer showed a descending trend from high,very high,medium to low HDI countries and territories.The ASIR of gastric cancer was positively correlated with HDI(rs=0.256,P=0.001),while ASMR showed no significant correlation with HDI(rs=-0.008,P=0.918).The M/I was negatively correlated with HDI(rs=-0.831,P＜0.001).The ASIR and ASMR of gastric cancer in males were significantly higher than those in females globally,in China,and across all HDI groups(all P＜0.05).Globally,both ASIR and ASMR of gastric cancer remained relatively stable before the age of 45,but showed a consistently rising trend after the age of 45.In China,the ASIR and ASMR of gastric cancer exceeded global average level across all age groups.Conclusion The burden of gastric cancer incidence and mortality is higher in very high and high HDI countries and territories compared to medium and low HDI countries and territories.In China,the burden of gastric cancer incidence and mortality is above the global average,highlighting the need for targeted prevention and control measures.

Objective:To construct a risk classification model for adverse events of medical consumables,so as to achieve automatic evaluation for risk level of such events,and enhance the capability for risk management of adverse events of medical consumables,and ensure the safety of medical apparatuses.Methods:The data of adverse events of medical consumables of 370 cases of 148 types that were reported by Shandong Provincial Third Hospital from 2020 to 2023 were selected,and they were divided into high-risk and low-risk types.Eight key factors of them,which included the number of cases,injury level,type of registration certificate,with source and without source,high-value and low-value,domestic and imports,product classification,and risk levels,were counted to form a dataset.K-nearest neighbor(KNN),support vector machine(SVM)and decision tree algorithms in machine learning were used to construct a risk classification model for adverse events of medical consumables.The data of 12 adverse events of medical consumables of 5 types of our hospital,which were newly reported in 2024,were integrated for their parameters.Then,the accuracy rate and prediction performance of the model were further analyzed.Results:By comparing the KNN,SVM and decision tree algorithm models,the effect of SVM algorithm model was better,and its accuracy rate was 90.54%,and its area under curve(AUC)value of the receiver operating characteristic(ROC)curve was 0.944,and its Kolmogorov-Smirnov(KS)test value was 0.808.The model had favorable predictive performance.The results,that invoked SVM algorithm model to conduct verification of actual prediction for 12 adverse events of medical consumables of 5 types,indicated it was same between predictive outcomes and risk levels of manual evaluation.Conclusion:The risk classification model of adverse events of medical consumables has established an operational model for assessing the risk level of such events,which can assist monitoring personnel for adverse event of medical apparatuses to quickly and accurately find risk signals of adverse events of medical consumables,and improve the monitoring capability of them for these adverse events.

Objective:To investigate the pathogen distribution, temporal trends in the rates of antimicrobial resistance, and susceptibility of bloodstream isolates and comparatively explore the epidemiological characteristics of bloodstream infections in hematology departments across 56 healthcare facilities in Guangdong Province from 2020 to 2024.Methods:A multicenter analysis was conducted to evaluate the constituent ratio of different pathogens isolated from clinical isolate data from bloodstream specimens in hematology, respiratory, and intensive care unit (ICU) departments across 56 healthcare facilities in Guangdong Province (2020-2024), and antimicrobial resistance trends in pathogens with high-detection rate over 5 years were assessed. Carbapenem-resistant Gram-negative organisms (CRO) were randomly sampled for carbapenemase gene detection and in vitro antimicrobial susceptibility tests with novel antimicrobial agents.Results:From 2020 to 2024, a total of 8 968, 6 440, and 25 511 bloodstream isolates were identified in the hematology, respiratory, and ICU departments, respectively, across 56 participating facilities in Guangdong Province, with significant differences in the pathogen constituent ratio among departments ( P<0.001). Notably, the hematology department demonstrated a predominance of Escherichia coli (24.1%), Klebsiella pneumoniae (17.5%), Pseudomonas aeruginosa (11.7%), coagulase-negative Staphylococci (15.2%), and Staphylococcus aureus (5.1%). In the resistance analysis, the rates of meropenem resistance of Escherichia coli and Klebsiella pneumonia increased from 6.7% and 5.8% (2020) to 14.0% and 15.8% (2024), respectively. Conversely, Pseudomonas aeruginosa exhibited a declining trend in the rate of meropenem resistance (6.2% to 1.9%) and imipenem (10.2% to 6.1%) during the same period. Acinetobacter baumannii demonstrated a biphasic resistance pattern to common antimicrobial agents, characterized by an initial decline, followed by a rebound. In this study, the susceptibility rates to conventional antimicrobial agents were significantly higher in Staphylococcus aureus versus coagulase-negative Staphylococci, with no glycopeptide- or linezolid-resistant strains detected. Notably, the prevalence of vancomycin-resistant Enterococcus faecium increased from 0 in 2020 to 23.1% in 2024. CRO carbapenemase phenotypes through active surveillance revealed that 80% Escherichia coli isolates were carrying blaNDM, 90% Klebsiella pneumoniae isolates were carrying blaKPC, 10% Pseudomonas aeruginosa isolates were carrying blaVIM, and 100% Acinetobacter baumannii were carrying blaOXA-23. The results of the antimicrobial susceptibility test in CRO revealed that carbapenem-resistant Escherichia coli (CRECO) demonstrated a 0 resistance rate to tigecycline, polymyxin B, and aztreonam/avibactam, whereas carbapenem-resistant Klebsiella pneumoniae exhibited a 0 resistance rate to aztreonam/avibactam, ceftazidime/avibactam, and imipenem/relebactam. Carbapenem-resistant Pseudomonas aeruginosa exhibited a 95.0% susceptibility rate to amikacin and polymyxin B, with a 45.0% resistance rate to ceftazidime/avibactam. In contrast, carbapenem-resistant Acinetobacter baumannii demonstrated complete susceptibility (100.0%) to sulbactam/durlobactam (MIC90=2 μg/ml), whereas eravacycline showed MIC50 and MIC90 values of 1 and 2 μg/ml, respectively. Conclusion:The pathogen constituent ratio of bloodstream isolates differed significantly among hematology, respiratory, and ICU departments. Notably, although CRO exhibited an escalating prevalence, it sustained high susceptibility to novel antimicrobial agents.