ObjectiveTo perform metabolic associated fatty liver disease （MAFLD） screening among individuals attending community health institutions， to identify the patients at a low， moderate or high risk of advanced liver fibrosis based on fibrosis-4 index （FIB-4） and liver stiffness measurement （LSM） measured by liver transient elastography， and to implement stratified management. MethodsA cross-sectional study was conducted among 630 individuals attending Beijing Baizhifang Community Health Service Center from January to July 2024， and they were divided into MAFLD group and non-MAFLD group. According to body mass index （BMI）， the MAFLD group was further divided into lean MAFLD group （BMI<23 kg/m²） and non-lean MAFLD group （BMI≥23 kg/m²）. The above groups were compared in terms of demographic features， laboratory markers， hepatic steatosis， and LSM. Fibrosis risk stratification was performed for MAFLD patients based on FIB-4 and LSM， and a closed-loop management system involving referral to tertiary hospitals and follow-up at community health institutions was implemented. The Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. ResultsThere were 445 individuals （70.6%） in the MAFLD group and 185 individuals （29.4%） in the non-MAFLD group. Compared with the non-MAFLD group， the MAFLD group had a significantly lower proportion of male individuals （χ²=4.299， P<0.05）， a significant reduction in the level of high-density lipoprotein cholesterol （Z=3.484， P<0.05）， and significant increases in body weight （Z=-7.366， P<0.05）， BMI （Z=-9.740， P<0.05）， waist circumference （Z=-6.397， P<0.05）， hip circumference （Z=-6.935， P<0.05）， alanine aminotransferase （ALT） （Z=-2.765， P<0.05）， fasting blood glucose （Z=-3.646， P<0.05）， triglyceride （TG） （Z=-6.569， P<0.05）， total cholesterol （Z=-2.033， P<0.05）， low-density lipoprotein cholesterol （Z=-2.935， P<0.05）， controlled attenuation parameter （CAP） （Z=-19.784， P<0.05）， and LSM （Z=-5.703， P<0.05）. Within the MAFLD group， there were 124 individuals （27.9%） in the lean MAFLD group and 321 individuals （72.1%） in the non-lean MAFLD group. Compared with the non-lean MAFLD group， the lean MAFLD group had significantly lower body weight （Z=-12.414， P<0.05）， BMI （Z=-16.363， P<0.05）， waist circumference （Z=-7.733， P<0.05）， hip circumference （Z=-8.595， P<0.05）， ALT （Z=-2.835， P<0.05）， aspartate aminotransferase （Z=-1.972， P<0.05）， TG （Z=-2.407， P<0.05）， CAP （Z=-4.429， P<0.05）， degree of steatosis （χ²=16.588， P<0.05）， and LSM （Z=-3.908， P<0.05）. Based on the results of FIB-4 and LSM， 76 patients at a moderate or high risk of liver fibrosis should be referred to a higher-level hospital for further management. ConclusionThe detection rate of MAFLD reaches 70.6% among the individuals attending community health institutions， among whom 76 （17.1%） should be referred to a higher-level hospital. Establishing a liver fibrosis risk stratification and management system based on FIB-4 and LSM among MAFLD individuals in communities provides a practical pathway for chronic disease management and referral system construction in community health institutions.

Objective:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

Objective:To analyze the trend of infertility disease burden from 1990 to 2019 and predict the age-standardized prevalence rate (ASPR) and age-standardized disability-adjusted life-year (DALY) rate of male and female infertility for 2020—2029, providing a certain reference for the prevention and treatment of infertility diseases.Methods:Based on the Global Burden of Disease 2019, prevalence, ASPR, DALY rate, and age-standardized DALY rate were used to evaluate the global burden of infertility comprehensively. The estimated annual percentage change was used to describe trends in the disease burden of infertility in 21 regions worldwide. The Bayesian age period cohort model was used to predict the ASPR and age-standardized DALY rate for male and female infertility in 204 countries and regions from 2020 to 2029. Sensitivity analysis was performed using the autoregressive integrated moving average model.Results:The prevalence, DALY rate, ASPR and age-standardized DALY rate of global male infertility increased from 319.52 per 100 000,1.82 per 100 000, 2 856.53 per 100 000, and 16.19 per 100 000 in 1990 to 565.30 per 100 000, 3.22 per 100 000, 3 398.53 per 100 000, and 19.36 per 100 000 in 2019, respectively. The prevalence, DALY rate, ASPR, and age-standardized DALY rate of global female infertility increased from 656.67 per 100 000, 3.53 per 100 000, 6 036.36 per 100 000, and 32.27 per 100 000 in 1990 to 1 223.78 per 100 000, 6.59 per 10 000, 7 483.12 per 100 000, and 40.33 per 100 000 in 2019, respectively. The burden of infertility disease was the highest in men and women aged 30-34 years, and the ASPR and age-standardized DALY rates were 4 407.47 per 100 000, 25.08 per 100 000, 10 270.55 per 100 000 and 55.65 per 100 000, respectively. Only in 45-49 years of age, the prevalence of infertility (11.31 per 100 000) and DALY rate (0.06 per 100 000) in women were lower than those in men (15.68 per 100 000 and 0.08 per 100 000). In addition, the burden of infertility was the lowest in high socio-demographic index regions. Cameroon had the highest ASPR (7 652.40 per 100 000) and age-standardized DALY rate (43.94 per 100 000) for male infertility. Chinese women had the highest ASPR (20 402.30 per 100 000) and age-standardized DALY rate (106.16 per 100 000) of infertility. The forecast results show that the burden of male and female infertility diseases will increase in 204 countries and regions from 2020 to 2029.Conclusion:The burden of infertility diseases in men and women increased in 204 countries and regions from 1990 to 2019, and it is predicted that the global burden of infertility diseases will continue to rise in 2020—2029. Preliminary screening of infertility should be carried out as soon as possible, health education should be strengthened and effective prevention and treatment strategies should be formulated.

Objective:To analyze the trend of infertility disease burden from 1990 to 2019 and predict the age-standardized prevalence rate (ASPR) and age-standardized disability-adjusted life-year (DALY) rate of male and female infertility for 2020—2029, providing a certain reference for the prevention and treatment of infertility diseases.Methods:Based on the Global Burden of Disease 2019, prevalence, ASPR, DALY rate, and age-standardized DALY rate were used to evaluate the global burden of infertility comprehensively. The estimated annual percentage change was used to describe trends in the disease burden of infertility in 21 regions worldwide. The Bayesian age period cohort model was used to predict the ASPR and age-standardized DALY rate for male and female infertility in 204 countries and regions from 2020 to 2029. Sensitivity analysis was performed using the autoregressive integrated moving average model.Results:The prevalence, DALY rate, ASPR and age-standardized DALY rate of global male infertility increased from 319.52 per 100 000,1.82 per 100 000, 2 856.53 per 100 000, and 16.19 per 100 000 in 1990 to 565.30 per 100 000, 3.22 per 100 000, 3 398.53 per 100 000, and 19.36 per 100 000 in 2019, respectively. The prevalence, DALY rate, ASPR, and age-standardized DALY rate of global female infertility increased from 656.67 per 100 000, 3.53 per 100 000, 6 036.36 per 100 000, and 32.27 per 100 000 in 1990 to 1 223.78 per 100 000, 6.59 per 10 000, 7 483.12 per 100 000, and 40.33 per 100 000 in 2019, respectively. The burden of infertility disease was the highest in men and women aged 30-34 years, and the ASPR and age-standardized DALY rates were 4 407.47 per 100 000, 25.08 per 100 000, 10 270.55 per 100 000 and 55.65 per 100 000, respectively. Only in 45-49 years of age, the prevalence of infertility (11.31 per 100 000) and DALY rate (0.06 per 100 000) in women were lower than those in men (15.68 per 100 000 and 0.08 per 100 000). In addition, the burden of infertility was the lowest in high socio-demographic index regions. Cameroon had the highest ASPR (7 652.40 per 100 000) and age-standardized DALY rate (43.94 per 100 000) for male infertility. Chinese women had the highest ASPR (20 402.30 per 100 000) and age-standardized DALY rate (106.16 per 100 000) of infertility. The forecast results show that the burden of male and female infertility diseases will increase in 204 countries and regions from 2020 to 2029.Conclusion:The burden of infertility diseases in men and women increased in 204 countries and regions from 1990 to 2019, and it is predicted that the global burden of infertility diseases will continue to rise in 2020—2029. Preliminary screening of infertility should be carried out as soon as possible, health education should be strengthened and effective prevention and treatment strategies should be formulated.

Objective:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

ObjectiveTo investigate the regulatory effect of Danggui Shaoyaosan on adenosine monophosphate-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR）/Unc-51-like kinase-1 （ULK1） signaling pathway in the rat model of metabolism-associated fatty liver disease （MAFLD）. MethodSixty SD rats were randomized into control， model， western medicine （polyene phosphatidylcholine capsules，0.144 g·kg^-1）， and low-， medium-， and high-dose （2.44， 4.88， 9.76 g·kg^-1， respectively） Danggui Shaoyaosan groups. After being fed with a high-fat diet for 8 weeks， the rats in each group were administrated with corresponding drugs for 4 weeks. At the end of drug treatment， serum and liver tissue were collected for subsequent determination of related indicators. ResultCompared with the control group， the model group showed increased contents of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C） and activities of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） in the serum， increased contents of TC， TG， and free fatty acids （FFAs） in the liver （P<0.01）， and decreased content of high-density lipoprotein cholesterol （HDL-C） in the serum （P<0.01）. Furthermore， the model group showed down-regulated protein levels of p-AMPK， microtubule-associated protein 1 light chain 3B （LC3B） Ⅱ， Beclin1， and ULK1 （P<0.01） and up-regulated protein levels of p-mTOR and ubiquitin-binding protein p62 in the liver （P<0.01）. The hepatic steatosis was obvious and the NAFLD activity score （NAS） and oil red O staining area increased in the model group， （P<0.05， P<0.01）. Compared with the model group， Danggui Shaoyaosan reduced the contents of TC and TG and the activities of ALT and AST in the serum， lowered the levels of TC， TG， and FFA in the liver， down-regulated the protein levels of p-mTOR and p62 （P<0.01）， elevated the serum HDL-C level， and up-regulated the protein levels of p-AMPK， LCBⅡ， Beclin1， and ULK1 in the liver （P<0.05， P<0.01）. Moreover， it alleviated hepatic steatosis and decreased the NAS and oil red O staining area （P<0.05， P<0.01）. ConclusionDanggui Shaoyaosan has therapeutic effect on MAFLD rats by regulating AMPK/mTOR/ULK1 signaling pathway to enhance autophagy.

Objective To study the corrosion-fatigue properties of a novel low modulus Ti-3Zr-2Sn-3Mo-25Nb(TLM)titanium alloy subjected to simulated body fluid(SBF).Methods Ti-6Al-4V(TC4)alloy was used as the control group.The electrochemical corrosion polarization curves of the two titanium alloys were measured in SBF.The pre-corroded TC4 and TLM titanium alloy samples were subjected to rotational bending fatigue tests.The loading stress amplitude and fatigue fracture cycle relationship was established using the experimental data,and the stress life curves were drawn.Subsequently,the fracture mechanism was analyzed by analyzing the corrosion fatigue micro-fracture morphology of the sample,and fatigue analysis on the titanium alloy sample was then conducted combined with the finite element software.Results The self-corrosion potential of the TC4 titanium alloy under stress annealing was lower than that of the TLM titanium alloy after heat treatment.The TLM titanium alloy was most sensitive to changes in cyclic stress.A comparison between the simulation and experimental results showed that the TC4 titanium alloy under stress annealing had a higher fatigue strength and stronger resistance to crack propagation than the TLM titanium alloy did after heat treatment,whereas its corrosion resistance was the opposite.Compared to the specimens without pre-corrosion treatment,the brittleness of the TLM titanium alloy increased,and its fatigue performance decreased after pre-immersion in SBF.Conclusions Through comparative analysis,the reliability of the test results proved to be high,and the COMSOL finite element software could effectively predict the fatigue life of titanium alloy materials.

Objective:To understand the sleep quality and mental health status of nurses in public health emergencies, and analyze the correlation between them.Methods:A total of 128 first-line nursing staff participating in public health emergencies on February 22-23, 2020 in Tianjin Beichen Hospital, Tianjin First Central Hospital, Tianjin Fourth Central Hospital were investigated by the general data questionnaire, Pittsburgh Sleep Quality Index (PSQI), and Symptom Checklist 90 (SCL-90).Results:70.3%(90/128) of nursing staff had poor sleep quality, and the total score of PSQI was (9.71±4.01) points, which was statistically significant compared with the domestic norm ( t value was 16.479, P<0.01). The total score of SCL-90 was 1.59±0.52, which was statistically significant compared with the domestic norm ratio ( t value was 4.505, P<0.01). One-way ANOVA showed that the nursing staff's age had a significant impact on sleep quality, and the difference was statistically significant ( F value was 4.092, P<0.05). Pearson correlation analysis showed that the Pittsburgh sleep quality scale index scores and symptom self-assessment scale and somatization, force, sensitive interpersonal relationship, depression, anxiety, hostile, terrorist, paranoia, and psychosis were positively correlated( r values were 0.292-0.444, P< 0.01). Conclusions:The sleep quality and mental health status of nurses in public health emergencies are poor, and the sleep quality is correlated with mental health status.

Objective:To understand the construction and problems of budget management system in public hospitals, and to provide countermeasures and suggestions for its continuous improvement and construction.Methods:Literature review was used to summarize the national policy requirements for comprehensive budget management system of these hospitals in China. Random sampling was used to select 109 public hospitals as pilots, and a questionnaire was customized from March to April in 2019, to investigate the current budget management and problems, with the data studied by descriptive statistical analysis.Results:Seventy-seven hospitals (70.64%) reported in self-appraisal their budget management as satisfactory and above, 78 hospitals (71.56%) had in place a comprehensive budget management system, 90 hospitals(82.57%) had in place a budget management committee, and 91 hospitals (83.49%) had in place refined budget preparation. The main problems were unclear budgeting requirements and nonstandard process, poor information integration and informationization, inadequate inter-department synergy, and poor alignment between business and finance.Conclusions:The public hospitals are recommended to enhance their comprehensive budget management system and their informationization, for full-process informationized management.

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.
Heart Rate ; Humans ; Monitoring, Physiologic ; Movement ; Sleep Apnea Syndromes ; Wearable Electronic Devices