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.

The pathogenesis of recurrent acute exacerbations of bronchiectasis is considered to be "yang floating and yin weakness". Yin weakness refers to the depletion of the water and fire of true yin in the lower jiao （焦）， which is the root of the disease. Yang floating refers to the ministerial fire floating upward and scorching the lung metal due to the failure of true yin to nourish and constrain yang， which is the manifestation of acute exacerbations. Yin weakness and yang floating interact with each other， forming a vicious cycle of "yin depletion leading to fire floating， and floating fire in turn injuring yin"， which leads to recurrent， prolonged， and refractory acute exacerbations of bronchiectasis. Based on the pathogenesis of yang floating and yin weakness， therapeutic formulas are proposed. Yinhuo Decoction （引火汤） can be used to nourish true yin and anchor floating fire， while a combination of Qianyang Pill （潜阳丹） and Fengsui Pill （封髓丹） is applied to warm the kidneys， subdue yang， and secure true fire， and Quanzhen Yiqi Decoction （全真一气汤） is utilized to drastically supplement the primal origin and rescue both yin and yang simultaneously.

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.

Urothelial carcinoma (UC), including bladder urothelial carcinoma and upper tract urothelial carcinoma (UTUC), is the most common malignant tumor in the urinary system. Traditional cell line models fall short in simulating its tumor microenvironment and in vivo behavior. Patient-derived organoid (PDO) models offer a new way to overcome these shortcomings. This paper reviews the construction techniques of PDO models in UC, their biological simulation capabilities, and their applications in preclinical research. It also analyzes the technical limitations of these models. PDO models can retain the histological, genomic, and transcriptomic features of the parent tumor and accurately simulate the tumor microenvironment and biological behavior. They have been widely used in bladder cancer research, providing a precise platform for drug screening, personalized treatment, and immunotherapy evaluation. However, their use in UTUC research is still in its infancy. In the future, through technological optimization, PDO models are expected to enhance their value in UC research, advancing precision medicine research and clinical translation.

Objectives:To investigate the incidence of tyrosine kinase inhibitor (TKI) withdrawal syndrome and psychological issues, and their associated factors, in patients with chronic-phase chronic myeloid leukemia (CML-CP) after TKI discontinuation.Methods:We retrospectively analyzed the clinical data of CML-CP patients who discontinued TKI therapy at Peking University People's Hospital after September 2012. Logistic regression models were used to identify independent factors associated with the occurrence of TKI withdrawal syndrome and psychological issues.Results:A total of 158 patients were included, of whom 92 (58%) were female. The median age at discontinuation was 50 ( IQR, 35-60) years. With a median follow-up of 25 ( IQR, 11-49) months, the 4-year rate of sustained major molecular response (MMR) was 60% (95% CI: 51%-70%) . Fifty-one (32%) patients experienced TKI withdrawal syndrome at a median of 1.3 ( IQR, 0.5-2.0) months after TKI discontinuation. Fifty-one (32%) patients reported psychological issues such as anxiety. These concerns stemmed from fears of fluctuating BCR::ABL1 levels or disease relapse, and, for those who discontinued TKI for pregnancy, worries about adverse fetal effects and/or the fetus inheriting CML. Multivariable analyses revealed that older age at discontinuation [ P=0.003 when adjusting for TKI therapy duration; P=0.002 when adjusting for deep molecular response (DMR) duration], longer TKI therapy duration ( P=0.010) , and longer DMR duration before discontinuation ( P=0.005) were significantly associated with a higher risk of TKI withdrawal syndrome; a university degree or higher ( P=0.010) and TKI discontinuation due to pregnancy or adverse events ( P=0.001) were significantly associated with psychological issues after discontinuation. The occurrence of TKI withdrawal syndrome or psychological issues had no impact on the probability of major molecular response loss after discontinuation. Conclusion:TKI withdrawal syndrome and psychological issues are common in CML patients who discontinue TKI therapy. Older age at discontinuation and longer TKI therapy duration or DMR duration are significantly associated with TKI withdrawal syndrome. Higher education level and TKI discontinuation due to pregnancy or adverse events are significantly associated with psychological issues.