Objective To investigate the epidemiological characteristics of lymphoma mortality and potential years of life lost among Wuhan residents, and to provide a scientific basis for the prevention and control of lymphoma. Methods Data on lymphoma deaths among residents in Wuhan from 2010 to 2019 were collected from the population-based Wuhan Mortality Surveillance System. Joinpoint regression model was used to evaluate the trends in age-standardized mortality rates and potential years of life lost due to lymphoma among the residents of different ages and genders. Potential years of life lost rate was used to assess the disease burden. Results There were 4 055 deaths (2481in male and 1 574 in female) from major kidney diseases in Wuhan residents between 2010 to 2019, with an age-standardized mortality rate of 5.11/100 000. The mortality rate of major kidney diseases showed an upward trend with increasing age. Between 2010 and 2019, the age-standardized mortality rates of the multiple myeloma and non-Hodgkin lymphoma were significantly increased (APC=6.924%, APC=1.407%, P＜0.05), and the potential years of life lost rate of non-Hodgkin lymphoma in female were significantly increased (APC=7.935%，P＜0.05). Conclusion From 2010 to 2019, the mortality rate of lymphoma among residents of Wuhan City shows an upward trend, especially for multiple myeloma and non-Hodgkin lymphoma. The disease burden of multiple myeloma shows an increasing trend in the female population, suggesting the need to take corresponding preventive and control measures.

Objective To systematically analyze the disease burden, long-term trends, and age-sex distribution of major valvular heart disease (VHD) subtypes—rheumatic heart disease (RHD), non-rheumatic valvular disease (NRVD), and non-rheumatic calcific aortic valve disease (CAVD)—in global, Chinese, and US populations from 1990 to 2021, providing evidence for public health strategies and clinical resource allocation. Methods Based on publicly available data from the Global Burden of Disease (GBD) Study 2021, we extracted incidence, mortality, and disability-adjusted life years (DALYs) for VHD from 1990 to 2021. Age-standardized rates (ASRs) were calculated using the GBD 2021 global standard population, and the estimated annual percentage change (EAPC) with its 95% uncertainty interval (UI) was computed for the period. Data from the Agency for Healthcare Research and Quality (AHRQ), the European Society of Cardiology (ESC)/Eurostat surveys, and Chinese national registries were used for trend triangulation and contextual background. Results From 1990 to 2021, the ASR and disease burden of RHD significantly decreased globally and in China (EAPC for DALYs in China: −4.8%, 95%UI: −5.0% to −4.6%). In contrast, the burden of NRVD and CAVD steadily increased in aging populations like those in China and the US, with a higher burden observed in older adults and males. In 2021, the incidence of NRVD and CAVD peaked in individuals aged ≥65 years, with rates being significantly higher in men than in women. RHD burden was concentrated in low socio-demographic index (SDI) regions, whereas NRVD/CAVD burden was strongly associated with high-SDI regions. Conclusion The global VHD epidemiological landscape is transitioning from an RHD-dominant to an NRVD/CAVD-dominant pattern. China faces a dual challenge of a residual RHD burden and a rapidly growing burden of degenerative valvular diseases. Developing tailored screening, prevention, and treatment strategies for different disease subtypes and populations is crucial.

Objective: To translate the common metrics for donation attitude, subjective norm, perceived behavioral control, and intention for the blood donation context (BD-ASPI) into Chinese, and to test its reliability and validity among college students. Methods: A research team was established. Following Beaton's cross-cultural adaptation guidelines, the BD-ASPI was translated, culturally adapted, and pre-tested to develop the Chinese version. Using convenience sampling, 620 students from four universities in Wuhan were surveyed form August to November 2024 to test the scale's reliability and validity. Results: The Chinese version of the scale consisted of 21 items across four dimensions: attitude towards blood donation, subjective norm, perceived behavioral control, and intention. The item-level content validity index ranged from 0.89 to 1.00, and the average scale-level content validity index was 0.984. Confirmatory factor analysis indicated a good fit for the second-order factor model. The Criterion validity was 0.509 (P<0.001). The overall Cronbach's α coefficient was 0.965, with the coefficients for each dimension ranging from 0.891 to 0.974. The test-retest reliability was 0.894. Conclusion: The Chinese version of the BD-ASPI demonstrates good reliability and validity, and can serve as an effective tool for assessing the behavioral intention of voluntary blood donation among college students in China.

Objective To analyze the global pattern, long-term trends, and regional and population heterogeneity in the burden of 375 diseases and injuries and 88 risk factors from 1990 to 2023 using data from the Global Burden of Disease Study 2023 (GBD 2023). Methods Publicly available GBD 2023 estimates were extracted for incidence, deaths, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs), and age-standardized rates at the global level and by socio-demographic index (SDI), GBD region, age group, and sex. Descriptive analysis, ranking comparison, stratified analysis, and trend analysis were performed, and 95% uncertainty interval was used to assess the stability of estimates. Results In 2023, non-communicable diseases were the leading contributor to global DALYs, accounting for 64.39% of DALYs across the three major disease and injury groups (communicable, maternal, neonatal, and nutritional diseases, non-communicable diseases, and injuries), and also had the highest age-standardized DALYs rate. From 1990 to 2023, age-standardized DALYs rates declined for three major disease and injury groups, with estimated annual percentage changes of −2.69%, −0.78%, and −1.47%, respectively. Cardiovascular diseases ranked first by both DALYs and deaths in 2023, whereas respiratory infections and tuberculosis ranked first by incidence. Non-communicable diseases were the leading cause of disease burden across all SDI strata. As the SDI level declined, the burden of communicable, maternal, neonatal, and nutritional diseases, as well as injuries, increased markedly. Behavioral and metabolic risks remained the main contributors to attributable deaths and DALYs. Conclusion From 1990 to 2023, the global burden of disease continues to shift toward non-communicable diseases; however, some regions still face a double burden of infectious, maternal and child, injury-related, and chronic diseases. Integrating deaths, DALYs, YLLs, YLDs, and risk-attributable burden helps identify priority areas for prevention and resource allocation across regions and populations.

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

From July 23, 2018, to December 31, 2019, a total of 299 219 children in Shandong Province received the fifth dose of the diphtheria, tetanus, and acellular pertussis combined vaccine (DTaP). Among these recipients, the distribution by age was as follows: 20 children under 2 years old (0.01%), 273 996 children aged 2 years (91.57%), 20 242 children aged 3 years (6.76%), 3 932 children aged 4 years (1.31%), 963 children aged 5 years (0.32%), and 66 children aged 6 years and above (0.02%). In total, 1 972 cases of adverse events following immunization (AEFI) were reported after the administration of the fifth dose of DTaP, resulting in an incidence rate of 659.05 per 100 000 doses. Among these, 1 718 cases were classified as common vaccine reactions, with an incidence rate of 574.16 per 100 000 doses, while 247 cases were identified as rare reactions, yielding an incidence rate of 82.55 per 100 000 doses. The incidence of AEFIs, as well as the rates of common and rare reactions, exhibited a significant increasing trend with the number of doses administered (all P<0.001). Among the rare reactions, there were 10 cases classified as severe, resulting in a reported incidence of 3.34 per 100 000 doses.

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

Inflammatory response is a common feature of many acute and chronic diseases,which involves in activation of various cell types.Similar to tumor cells,inflammatory response is regulated by glucose metabolic reprogramming.Cells involved in pro-inflammatory response,such as M1 macrophages,Th1 and Th17 lymphocytes,must generate energy rapidly through glycolysis transformed by glucose metabolic reprogramming to promote inflammation,while cells involved in immune regulation and anti-inflam-matory response,such as regulatory T cells and M2 macrophages,preferentially use fatty acid oxidation and oxidative phosphorylation as a main source for energy production.This article systematically reviews the role of glucose metabolic reprogramming in different stages of inflammatory response,as well as the various molecular mechanisms of glucose metabolic reprogramming in inflammatory response,including activation or inhibition of signaling pathways,epigenetic regulation,post-transcriptional regulation and post-translational modification,which aims to provide a reference for exploring the specific regulatory mechanisms of glucose metabolic reprogramming on inflammatory response and the therapeutic targets of inflammatory-related diseases.

Medical therapy and surgical intervention are the two primary approaches for treating myocardial bridge.However,there remains controversy regarding the use of coronary artery bypass grafting(CABG)and myocardial bridge unroofing.Here,we report a case of myocardial infarction following CABG in a patient with a myocardial bridge.The patient was admitted to Lanzhou First Peopie's Hospital with persistent chest pain,chest tightness,and shortness of breath lasting 2 hours.Physical examination revealed no significant abnormalities.Electrocardiography(ECG)indicated extensive anterior wall myocardial infarction.Laboratory findings showed myoglobin levels of 140.1 ng/ml and troponin Ⅰ levels of 2.59 ng/ml,with no other significant abnormalities.The initial diagnosis was acute extensive anterior wall myocardial infarction.Emergency coronary angiography revealed a myocardial bridge in the mid-segment of the left anterior descending artery(LAD).Emergency CABG using the left internal mammary artery to the LAD was performed,leading to symptomatic improvement,and the patient was discharged in stable condition.However,the patient experienced a recurrent myocardial infarction seven years post-surgery and received secondary preventive medical therapy.The patient is currently under ongoing follow-up care.CABG is an effective treatment for myocardial bridge.However,based on the case reported in this study,we recommend careful evaluation of whether a patient may benefit from CABG.

Neonatal sepsis is a global health problem that seriously affects the body health and life safety of newborns. It has a higher incidence in preterm infants,especially for early-onset sepsis (EOS) within 72 hours of birth. The diagnosis of neonatal EOS requires a series of examinations,and early and accurate diagnosis can improve clinical outcomes and reduce antibiotic overuse in a timely manner. At present,the commonly used biomarkers and traditional blood culture methods for EOS diagnosis have certain shortcomings,so it is urgent to find new molecular diagnostic methods. This article summarizes and compares the early and novel diagnostic methods of neonatal EOS,in order to provide a reference for clinical practice.