With the improvement of living standards， the incidence rate of metabolic dysfunction-associated fatty liver disease （MAFLD） is gradually increasing with a younger age of onset， and MAFLD has become a global health problem， while its specific pathogenesis remains unclear. Macrophages， as one of the important cells involved in the pathogenesis of MAFLD， have the ability to present antigens， eliminate pathogenic microorganisms， and promote liver inflammatory responses， thereby attracting wide attention for a long time. The latest studies have shown that macrophages may become a new therapeutic target for MAFLD. This article systematically reviews the role of intrahepatic macrophages in liver inflammation caused by MAFLD， including their activation， polarization， recruitment mechanisms， and interactions with other cells， in order to provide new ideas and perspectives for the clinical prevention and treatment of MAFLD.

Organic ultraviolet absorbers (OUVs) have been widely used in personal care products and industrial products due to their unique physicochemical properties. However, with the advancement of science and technology and the deepening of research, the potential risks of OUVs have gradually emerged. They have been proven to be persistent, bioconcentrated and potential endocrine disruptors that may pose a threat to human health. In recent years, some OUVs have been widely detected in environmental and human samples worldwide, and the concentration of detection has been increasing year by year, which has attracted extensive attention both domestically and internationally. This article summarizes the research results of OUVs exposure in recent years from two aspects: external environmental exposure and internal human exposure, aiming to provide a valuable reference for the subsequent research on human exposure and health risk assessment of OUVs.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the association between blood selenium levels and sex hormones in Chinese men aged 18-79 years.Methods:Data were derived from the China National Human Biomonitoring survey conducted in 2017-2018, with a final sample size of 5 414 men. General demographic characteristics, behavioral habits, and dietary frequency were collected through questionnaires and physical examinations. Fasting blood samples were collected to measure blood lead, serum testosterone, and estradiol levels. Complex sampling linear regression models were used to analyze the associations between blood selenium levels and testosterone, estradiol, and the testosterone/estradiol ratio, adjusting for confounding factors including age, education level, marital status, smoking status, alcohol consumption, seafood intake, soy product intake, protein supplement intake, BMI, and diabetes status.Results:The mean age of the 5 414 participants was (46.85±27.91) years; 4 774 (91.65%) were of Han ethnicity and 4 505 (86.68%) were married. The median ( Q1, Q3) blood selenium concentration in men was 97.80 (80.64, 116.99) μg/L. After adjusting for confounding factors, the complex sampling linear regression model revealed negative associations between blood selenium levels and both testosterone levels and the testosterone/estradiol ratio, with a significant linear trend ( Ptrend<0.05). Compared with the Q1 group, the β (95% CI) values for testosterone in the Q2, Q3, and Q4 groups were -0.02 (-0.06 to 0.02), -0.03 (-0.08 to 0.01), and -0.06 (-0.09 to -0.02), respectively. Similarly, the β (95% CI) values for the testosterone/estradiol ratio in the Q2, Q3, and Q4 groups were -0.01 (-0.03 to 0.02), -0.01 (-0.04 to 0.04), and -0.03 (-0.06 to -0.01), respectively. Subgroup analysis indicated stronger associations between blood selenium levels and testosterone/estradiol levels in non-smoking and obese men (BMI≥28 kg/m2). Conclusion:Blood selenium levels are negatively associated with testosterone levels and the testosterone/estradiol ratio in Chinese adult males.

Objective:To investigate the changes of nitric oxide(NO),endothelin-1(ET-1),thromboxane B2(TXB2)levels in patients with chronic heart failure(CHF)and their correlation with plasma brain natriuretic peptide(BNP).Methods:We enrolled 110 CHF patients admitted in the Second Hospital of Qinhuangdao between January and December 2022.According to the New York Heart Association(NYHA)cardiac function classification,the patients were divided into cardiac function class Ⅱ group(n=22),cardiac function class Ⅲ group(n=50),and cardiac function class Ⅳ group(n=38).Baseline data,levels of NO,ET-1,TXB2,BNP and other related laboratory indexes were compared among three groups.Pearson correlation analysis was used to analyze the association of NO,ET-1,TXB2 with plasma BNP in CHF patients.Multivariate linear regression was employed to analyze related factors of elevated BNP in CHF patients.Results:Compared with patients in class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher uric acid[(467.39±32.60)μmol/L vs.(367.25±22.39)μmol/L vs.(421.42±28.34)μmol/L],total bilirubin[(17.36±3.10)μmol/L vs.(10.65±1.39)μmol/L vs.(11.12±2.01)μmol/L],BNP[(897.60±50.11)ng/L vs.(381.37±31.25)ng/L vs.(527.60±47.84)ng/L],NO[(50.12±5.95)μmol/L vs.(25.36±2.14)μmol/L vs.(37.92±4.84)μmol/L],ET-1[(114.10±10.53)pg/L vs.(80.25±7.38)pg/L vs.(97.03±8.40)pg/L],TXB2[(417.98±29.35)pg/ml vs.(302.63±19.63)pg/ml vs.(381.29±26.44)pg/ml](P＜0.001 all).Compared with those in class Ⅱ group,those in class Ⅲ group had significant higher above-mentioned indexes(except total bilirubin)(P＜0.001 all).Pearson correlation analysis indicated that plasma NO,ET-1,TXB2 were positively associated with BNP(r=0.828,0.750,0.720,P＜0.001 all).Multivariate linear regression analysis indicated that uric acid,total bilirubin,plasma NO,ET-1 and TXB2 levels were independent risk factors for elevated BNP level in CHF patients(B=0.555～20.550,P＜0.05 or＜0.01),while the use of angiotensin converting enzyme inhibitors(ACEI)/angiotensin Ⅱ receptor blockers(ARB)was an independent protective factor(B=-46.222,P=0.027).Conclusion:The levels of NO,ET-1,and TXB2 are closely related to the occurrence and development of CHF,and they show an increasing trend with the progression of CHF,and is closely related to BNP.

Objective:To explore the application value of the combination of failure mode and effects analysis (FMEA) and work breakdown structure (WBS) on management of telemetry monitoring system. Methods:Seven sets of used telemetry monitoring systems in Huzhou Third People's Hospital from March 2022 to April 2024 were selected. The routine mode was used to manage the telemetry monitoring systems from March 2022 to February 2023,and the management mode of telemetry monitoring systems based on FMEA and WBS was adopted to manage the telemetry monitoring systems from May 2023 to April 2024. FMEA method was used to analyze the quantitative risk scores of various failures in 1740 uses of the telemetry monitoring system from March 2022 to February 2023,and the fault type was represented by Fx. According to the risk quantification score,the top 5 failure types included poor radio frequency (RF) signal (F2),larger pseudo-interference (F3) of electrocardiogram (ECG),abnormal measurement of blood oxygen saturation (SpO2) (F6),excessive movement and mistake touch (F8) of patients,and identification (ID) number error (F9) of telemetry emission box. WBS quality management method combined with tree chart,countermeasure fishbone chart,task assignment matrix and other tools were performed to conduct the setting of expected objectives,countermeasure analysis,task formulation and work implementation of management of telemetry monitoring system. The above indicators were compared with the changes of the top 5 failure risk grading and incidence of failure in 1850 uses of management of telemetry monitoring system from May 2023 to April 2024. Results:After the telemetry monitoring system was used to conduct management,the risk grading of the F2,F3,F6,F8 and F9 failures with highest risk grading reduced respectively to low grade,middle grade,middle grade,low grade and middle grade. At the same time,the number of F2,F3,F6,F8,F9 failures with the highest risk grading and total failures were respectively 91,89,39,45,39 and 478 times,and the incidence rates of them were 4.92％,4.81％,2.11％,2.43％,2.11％ and 25.84％,which were significantly lower than those of adopting conventional management methods. The differences of them between two methods were statistically significant (x2=2.125,6.911,9.099,2.261,2.431,32.491,P<0.05). Conclusion:The management of combined quality management method of FMEA and WBS for telemetry monitoring system can reduce risk grading and incidence rate of equipment failure,and ensure the safety of equipment in clinical use.

Objective:To investigate the changes of nitric oxide(NO),endothelin-1(ET-1),thromboxane B2(TXB2)levels in patients with chronic heart failure(CHF)and their correlation with plasma brain natriuretic peptide(BNP).Methods:We enrolled 110 CHF patients admitted in the Second Hospital of Qinhuangdao between January and December 2022.According to the New York Heart Association(NYHA)cardiac function classification,the patients were divided into cardiac function class Ⅱ group(n=22),cardiac function class Ⅲ group(n=50),and cardiac function class Ⅳ group(n=38).Baseline data,levels of NO,ET-1,TXB2,BNP and other related laboratory indexes were compared among three groups.Pearson correlation analysis was used to analyze the association of NO,ET-1,TXB2 with plasma BNP in CHF patients.Multivariate linear regression was employed to analyze related factors of elevated BNP in CHF patients.Results:Compared with patients in class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher uric acid[(467.39±32.60)μmol/L vs.(367.25±22.39)μmol/L vs.(421.42±28.34)μmol/L],total bilirubin[(17.36±3.10)μmol/L vs.(10.65±1.39)μmol/L vs.(11.12±2.01)μmol/L],BNP[(897.60±50.11)ng/L vs.(381.37±31.25)ng/L vs.(527.60±47.84)ng/L],NO[(50.12±5.95)μmol/L vs.(25.36±2.14)μmol/L vs.(37.92±4.84)μmol/L],ET-1[(114.10±10.53)pg/L vs.(80.25±7.38)pg/L vs.(97.03±8.40)pg/L],TXB2[(417.98±29.35)pg/ml vs.(302.63±19.63)pg/ml vs.(381.29±26.44)pg/ml](P＜0.001 all).Compared with those in class Ⅱ group,those in class Ⅲ group had significant higher above-mentioned indexes(except total bilirubin)(P＜0.001 all).Pearson correlation analysis indicated that plasma NO,ET-1,TXB2 were positively associated with BNP(r=0.828,0.750,0.720,P＜0.001 all).Multivariate linear regression analysis indicated that uric acid,total bilirubin,plasma NO,ET-1 and TXB2 levels were independent risk factors for elevated BNP level in CHF patients(B=0.555～20.550,P＜0.05 or＜0.01),while the use of angiotensin converting enzyme inhibitors(ACEI)/angiotensin Ⅱ receptor blockers(ARB)was an independent protective factor(B=-46.222,P=0.027).Conclusion:The levels of NO,ET-1,and TXB2 are closely related to the occurrence and development of CHF,and they show an increasing trend with the progression of CHF,and is closely related to BNP.

Objective:To analyze the symptoms,diagnosis and treatment of upper urinary tract calculi patients combined with mild and moderate benign prostatic hyperplasia(BPH)after ureteral stent implantation.Methods:One hundred and six BPH pa-tients who were hospitalized for upper urinary tract calculi and had ureteral stents retained from January 2019 to December 2022 were selected and divided into 2 weeks group and 4 weeks group according to the time of removal of ureteral stents after surgery.Their gener-al clinical data were analyzed and compared.International Prostatic Symptom Scale(IPSS),postoperative ureteral Stent Symptom Questionnaire(USSQ),and incidence of adverse events after ureteral stent removal were recorded before and after removal.Results:The scores of IPSS were significantly increased in all patients,and symptoms in urinary tract had improved significantly after discharge(P＜0.05).Compared with the 2 weeks group,the USSQ score of the 4 weeks group was significantly increased(P＜0.05).And no significant adverse event was observed in the 2 weeks group after the removal of ureteral sten.Conclusion:IPSS score and USSQ score increased significantly during stent implantation in BPH patients with lithiasis.And complications increased sig-nificantly over time.Following thorough clinical assessment,early ureteral stent removal demonstrates both safety and efficacy,repre-senting an optimal therapeutic approach in selected cases.

Objective:To explore the application value of the combination of failure mode and effects analysis (FMEA) and work breakdown structure (WBS) on management of telemetry monitoring system. Methods:Seven sets of used telemetry monitoring systems in Huzhou Third People's Hospital from March 2022 to April 2024 were selected. The routine mode was used to manage the telemetry monitoring systems from March 2022 to February 2023,and the management mode of telemetry monitoring systems based on FMEA and WBS was adopted to manage the telemetry monitoring systems from May 2023 to April 2024. FMEA method was used to analyze the quantitative risk scores of various failures in 1740 uses of the telemetry monitoring system from March 2022 to February 2023,and the fault type was represented by Fx. According to the risk quantification score,the top 5 failure types included poor radio frequency (RF) signal (F2),larger pseudo-interference (F3) of electrocardiogram (ECG),abnormal measurement of blood oxygen saturation (SpO2) (F6),excessive movement and mistake touch (F8) of patients,and identification (ID) number error (F9) of telemetry emission box. WBS quality management method combined with tree chart,countermeasure fishbone chart,task assignment matrix and other tools were performed to conduct the setting of expected objectives,countermeasure analysis,task formulation and work implementation of management of telemetry monitoring system. The above indicators were compared with the changes of the top 5 failure risk grading and incidence of failure in 1850 uses of management of telemetry monitoring system from May 2023 to April 2024. Results:After the telemetry monitoring system was used to conduct management,the risk grading of the F2,F3,F6,F8 and F9 failures with highest risk grading reduced respectively to low grade,middle grade,middle grade,low grade and middle grade. At the same time,the number of F2,F3,F6,F8,F9 failures with the highest risk grading and total failures were respectively 91,89,39,45,39 and 478 times,and the incidence rates of them were 4.92％,4.81％,2.11％,2.43％,2.11％ and 25.84％,which were significantly lower than those of adopting conventional management methods. The differences of them between two methods were statistically significant (x2=2.125,6.911,9.099,2.261,2.431,32.491,P<0.05). Conclusion:The management of combined quality management method of FMEA and WBS for telemetry monitoring system can reduce risk grading and incidence rate of equipment failure,and ensure the safety of equipment in clinical use.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.