Objective To analyze the disease burden of chronic kidney diseases (CKD) attributed to high body mass index (BMI) in China from 1992 to 2021 and predict the disease burden for the next decade, and to provide evidence for the prevention and treatment of CKD. Methods Using the Global Burden of Disease (GBD) database and the Joinpoint model, the average annual percentage rate change (AAPC) of the mortality rate and disability-adjusted life year (DALY) rate was calculated to describe and analyze the CKD disease burden attributed to high BMI in China from 1992 to 2021. The ARIMA model was employed to predict and analyze the change trend of the CKD disease burden. Results From 1992 to 2021, the mortality rate and DALY rate attributed to high BMI-induced chronic kidney disease showed an upward trend. Compared to 1992, the attributed number of deaths increased by 324.38%, and DALYs increased by 268.56%; the mortality rate increased by 64.00%, and the DALY rate grew by 51.62%. From 1992 to 2021, the mortality rate and DALY rate for males were lower than those for females, but the growth rate for males exceeded that of females. From 1992 to 2021, the mortality rate and DALY rate of chronic kidney disease attributed to high BMI in China increased with age. The average annual change rate of chronic kidney disease attributed to high BMI in China from 1992 to 2021 (mortality rate: 1.40 per 100,000 (95% CI: 1.04–1.76), DALY rate: 1.43 per 100 000 (95% CI: 1.17–1.70)) was higher than thHuaiyin Normal University, Huai'anher social demographic index (SDI) regions. The ARIMA model predicted that the age-standardized mortality rate increased from 2.91 per 100 000 in 2022 to 3.05 per 100 000 in 2026, and the age-standardized DALY rate increased from 69.65 per 100 000 in 2022 to 73.58 per 100 000 in 2026. Conclusion Chronic kidney disease attributed to high BMI in China is on the rise, and it will continue to grow in the future. The focus of CKD prevention and control should be on males and the elderly, while active measures should be taken to reduce the occurrence and progression of chronic kidney disease.

Objective To understand the driver gene mutation status in patients with non-small cell lung cancer (NSCLC) in Xi'an City, and to analyze the association with environmental exposure factors. Methods A total of 305 NSCLC patients admitted to the First Affiliated Hospital of the Air Force Medical University from January 2019 to December 2023 were included. The driver gene mutation status was observed, and the relationship with environmental exposure factors was analyzed. Results The driver gene mutation rate of 305 patients was 46.89%, with EGFR gene mutation accounting for the highest proportion, and 4 cases of gene co-mutations were detected. There was a difference in gender among patients with different single drive gene mutations (P<0.05), and the proportion of EGFR in women was significantly higher (P<0.05). Univariate analysis showed that there were statistical differences in family history, smoking history, long-term cooking history, and fried smoked food intake between patients with driver gene mutation and patients without driver gene mutation (P<0.05). Logistic regression analysis suggested that long-term cooking history (OR=2.392), and fried smoked food intake (OR=2.849) were the environmental exposure factors affecting EGFR gene mutation (P<0.05), and smoking history (OR=1.377) was an environmental exposure factor of KRAS gene mutation (P<0.05). Conclusion EGFR gene mutation accounts for the highest proportion of NSCLC patients in Xi'an City, and is mainly female. Long-term cooking history, and fried smoked food intake are related to EGFR gene mutation. There is a certain association between smoking history and KRAS gene mutation.

Background Fine particulate matter (PM_2.5) monitoring stations may generate missing data for a certain period of time due to various factors. This data loss will adversely affect air quality assessment and pollution control decision-making. Objective To propose an inverse distance weighted (IDW) spatiotemporal interpolation method based on particle swarm optimization (PSO) to interpolate and fill missing PM_2.5 spatiotemporal sequence data and increase interpolation accuracy. Methods An interpolation experiment was designed into two parts. The first part used hourly PM_2.5 observational data from four moments on January 1, 2017 in the Yangtze River Delta region. The second part employed daily PM_2.5 observational data from the first 10 d of January 2017 in the Beijing-Tianjin-Hebei region. Interpolation accuracy was evaluated using four metrics: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean relative error (MRE). Results IDW spatiotemporal interpolation method optimized with PSO significantly improved the accuracy of filling missing PM_2.5 spatiotemporal sequence data. In the hourly-scale experiment conducted in the Yangtze River Delta region, compared to a distance index of 2, the accuracy metrics RMSE, MAE, MAPE, and MRE generated by the proposed method improved on average by 0.17 μg·m⁻³, 0.27 μg·m⁻³, 0.17%, and 0.01%, respectively. The PM_2.5 spatial field maps generated for four moments based on this method clearly illustrated the spatiotemporal distribution characteristics of hourly PM_2.5 concentrations in the Yangtze River Delta region. In the daily-scale experiment conducted in the Beijing-Tianjin-Hebei region, the PSO-optimized distance index outperformed the traditional method, with interpolation accuracy improvements of approximately 0.215 μg·m⁻³, 0.283 μg·m⁻³, 0.174%, and 0.014%, respectively. Furthermore, the seasonal PM_2.5 spatial field maps generated by this method revealed the spatiotemporal distribution characteristics of PM_2.5 concentrations in the Beijing-Tianjin-Hebei region across different seasons, further validating the effectiveness and applicability of this method. Conclusion The IDW spatiotemporal interpolation method optimized with PSO is highly accurate and reliable for interpolating the missing data in the Yangtze River Delta region and the Beijing-Tianjin-Hebei region, providing valuable insights for air pollution control and public health protection.

Objective: To investigate the urban and rural differences in adverse outcomes of pulmonary tuberculosis (PTB) in patients with pulmonary tuberculosis-diabetes mellitus comorbidity (PTB-DM), so as to provide insights into improving the prevention and treatment measures for PTB-DM. Methods: Patients with PTB-DM who were admitted and discharged from 14 designated tuberculosis hospitals in Hangzhou City from 2018 to 2022 were selected. Basic information, and history of diagnosis and treatment were collected through hospital information systems. The adverse outcomes of PTB were defined as endpoints, and the proportions of adverse outcomes of PTB in urban and rural patients with PTB-DM were analyzed. Factors affecting the adverse outcomes of PTB were identified using a multivariable Cox proportional hazards regression model. Results: A total of 823 patients with PTB-DM were enrolled, including 354 (43.01%) urban and 469 (56.99%) rural patients. There were 112 (13.61%) patients with adverse outcomes of PTB. The proportions of adverse outcomes of PTB in urban and rural patients were 14.41% and 13.01%, respectively, with no statistically significant difference (P>0.05). Multivariable Cox proportional hazards regression analysis identified first diagnosed in county-level hospitals or above (HR=2.107, 95%CI: 1.181-3.758) and drug resistance (HR=3.303, 95%CI: 1.653-6.600) as the risk factors for adverse outcomes of PTB in urban patients with PTB-DM, while the treatment/observed management throughout the process (HR=0.470, 95%CI: 0.274-0.803) and fixed-dose combinations throughout the process (HR=0.331, 95%CI: 0.151-0.729) as the protective factors for adverse outcomes in rural patients with PTB-DM. Conclusions There are differences in influencing factors for adverse outcomes of PTB in urban and rural patients with PTB-DM. The adverse outcomes of PTB are associated with first diagnosed hospitals and drug resistance in urban patients, and are associated with the treatment/observed management and fixed-dose combinations throughout the process in rural patients.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

BACKGROUND:It has also been confirmed that ferroptosis is closely related to a variety of musculoskeletal diseases,such as rheumatoid arthritis,osteosarcoma,and osteoporosis.The pathophysiological mechanisms of ferroptosis and osteoporosis need to be further studied and elucidated to broaden our understanding of iron metabolism and osteoporosis.It will provide research ideas for the future elucidation of new mechanisms of osteoporosis and the development of new technologies and drugs for the treatment of osteoporosis. OBJECTIVE:To provide an overview of the current status of research on ferroptosis in osteoporosis,to provide a new direction for future research on the specific molecular mechanisms of osteoporosis,and to provide more effective and better options for osteoporosis treatment strategies. METHODS:The first author used the computer to search the literature published from 2000 to 2024 in CNKI,WanFang,VIP,and PubMed databases with search terms"ferroptosis,iron metabolism,osteoporosis,osteoblast,osteoclast,bone metabolism,signal pathway,musculoskeletal,review"in Chinese and English.A total of 68 articles were finally included according to the selection criteria. RESULTS AND CONCLUSION:(1)Ferroptosis is a new type of cell death discovered in recent years,which is usually accompanied by a large amount of iron accumulation and lipid peroxidation during cell death,and its occurrence is iron-dependent.This is distinctly different from several types of cell death that are currently being hotly studied(e.g.,cellular pyroptosis,necrotic apoptosis,cuproptosis,and autophagy).(2)Intracellular iron homeostasis is manifested as a balance between iron uptake,export,utilization,and storage.The body's iron regulatory system includes systemic and intracellular regulation.The main factor of systemic regulation is hepcidin produced by hepatic secretion,and cellular regulation depends on the iron regulatory protein/iron response element system.Of course,intracellular iron homeostasis can be controlled by other factors,such as hypoxia,cytokines,and hormones.(3)Lipid peroxidation causes oxidative damage to biological membranes(plasma membrane and internal organelle membranes),lipoproteins,and other lipid-containing molecules.Polyunsaturated fatty acid-containing phospholipids are important targets of lipid peroxidation.Free polyunsaturated fatty acid is an important substrate for lipid oxidation and can bind to the phospholipid bilayer,leading to over-oxidation and thus triggering lipid apoptosis.(4)Several studies have shown that osteoblasts are overloaded with iron in different ways,resulting in the accumulation of unstable ferrous iron and the generation of reactive oxygen species and lipid peroxides,causing ferroptosis of osteoblasts and ultimately a decrease in bone formation,affecting bone homeostasis and the development of osteoporosis.(5)Osteoclasts are large multinucleated cells formed by the fusion of mononuclear macrophage cell lines or bone marrow mesenchymal stem cells induced by nuclear factor-κB ligand receptor activator,and they have the function of bone resorption.Iron ions can promote osteoclast differentiation and bone resorption through the production of intracellular lipid reactive oxygen species,while iron chelators can inhibit osteoclast formation in vitro and thus affect the occurrence and development of osteoporosis.

ObjectiveBased on analytic hierarchy process（AHP）-criteria importance through intercriteria correlation（CRITIC） hybrid weighting method， grey relational analysis and backpropagation artificial neural network（BP-ANN）， to optimize the water extraction process of Qizhi prescription， so as to provide an experimental basis for optimization of the preparation process of this prescription and the establishment of quality standards. MethodsL₉（3⁴） orthogonal test was employed， and the AHP-CRITIC hybrid weighting method was utilized to determine the weight coefficients of the quality fractions of various components， including astragaloside Ⅳ， polygalaxanthone Ⅲ， calycosin-7-O-β-D-glucoside， tenuifolin， and 3，6′-disinapoylsucrose， as well as the dry extract yield. The comprehensive score of each factor level combination in the orthogonal test were calculated as evaluation indicator to select the optimal extraction process parameters. The effects of extraction times， extraction time， and solvent dosage on the aqueous extraction process of the formula were investigated through intuitive analysis， variance analysis， and grey relational analysis. Meanwhile， a BP-ANN model was established to reverse-predict the optimal extraction process parameters of Qizhi prescription， and the optimized process parameters were validated. ResultsThe weight coefficients of the five index components（astragaloside Ⅳ， tenuifolin， calycosin-7-O-β-D-glucoside， polygalaxanthone Ⅲ， and 3，6′-disinapoylsucrose） and dry extract yield were 25.7%， 20.82%， 16.41%， 12.45%， 15.96% and 8.67%， respectively. The optimized extraction process parameters were extracted 3 times with 8， 6， 6 times the amount of water， each time for 1 h. The network prediction results of BP-ANN test samples were consistent with the orthogonal test results， and the mean square error（MSE） of the predicted and measured values of the network was <1%. The water extraction process of Qizhi prescription analyzed and predicted by relevant mathematical models was stable and feasible， which could effectively improve the extraction efficiency of the active ingredients of Astragali Radix and Polygalae Radix， and the average comprehensive score of the validation test was 90.85 with the relative standard deviation（RSD） of 1.55%. ConclusionThis study establishes a water extraction process for compound Qizhi granules， and the optimized extraction process can effectively improve the extraction efficiency of active ingredients， which provides useful references for the optimization of preparation process and the establishment of quality standards for other clinical experience formulas.

Malignant tumors are one of the major diseases that endanger human life and health. Chinese medicine has unique advantages in clinical anti-tumor treatment. However， how to translate the anti-tumor effects of Chinese medicine into clinical practice is the core issue that must be addressed in the process of treating malignant tumors with traditional Chinese medicine （TCM）. Unlike modern chemical drugs， the compatibility application of Chinese medicine is the key factor that determines whether Chinese medicine can achieve optimal anti-tumor efficacy and realize the goal of "enhancing efficacy and reducing toxicity". The formulation structure based on this compatibility is the basic form for the safe， efficient， and rational clinical use of anti-tumor Chinese medicine， and it mainly includes three categories： herb pairs， tri-herbal combinations， and compound compatibility. Although herb pairs have the characteristics of a simple structure and strong targeting （enhancing efficacy and reducing toxicity）， they often have a single effect and cannot fully address the complex pathogenesis of tumors. As a result， herb pairs are rarely used alone in practice. Compared to herb pairs， tri-herbal combinations broaden the application scope of herbs in clinical treatment， but their therapeutic range remains limited. The traditional "sovereign， minister， assistant， and guide" compound prescription， which includes herb pairs and tri-herbal combinations， improves the efficacy of herbs in treating serious diseases， hypochondriasis， chronic diseases， and miscellaneous disorders. However， due to the limitations of its historical background， it has not been integrated with modern clinical practice and modern pharmacological research， which restricts the development of compound compatibility theory. With the emergence of modern medical technology， it has been combined with traditional compatibility theory of Chinese medicine to create an innovative modern compatibility theory. This includes the "aid medicine" theory derived from modern Chinese medicine pharmacology， which compensates for the inability of the "sovereign， minister， assistant， and guide" theory to accurately apply medicine. Additionally， the "state-targeted treatment based on syndrome differentiation" theory， developed from pharmacology and modern medicine， addresses the deficiency in disease cognition in the "sovereign， minister， assistant， and guide" theory. Under the guidance of these compatibility forms and theories， clinical anti-tumor Chinese medicine can exert its maximum anti-tumor efficacy， which is of great significance for the application of Chinese medicine in clinical tumor treatment.

Severe pneumonia is one of the most common and critical respiratory diseases in clinical practice. It is characterized by rapid progression， difficult treatment， high mortality， and many complications， posing a significant threat to the life and health of patients. The pathogenesis of severe pneumonia is highly complex， and studies have shown that its occurrence and development are closely related to multiple signaling pathways. Currently， the treatment of severe pneumonia mainly focuses on anti-infection， mechanical ventilation， and glucocorticoids， but clinical outcomes are often not ideal. Therefore， finding safe and effective alternative therapies is particularly important. In recent years， with the deepening of research into traditional Chinese medicine （TCM）， it has gained widespread attention in the treatment of severe pneumonia. This paper reviewed the relationship between severe pneumonia and relevant signaling pathways in recent years and how TCM regulated these pathways in the treatment of severe pneumonia. It was found that TCM could regulate the Toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor-κB （NF-κB）， Janus kinase （JAK）/signal transducer and activator of transcription （STAT）， phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， NOD-like receptor protein 3 （NLRP3）， and nuclear factor E₂-related factor 2 （Nrf2） signaling pathways， playing a role in reducing the inflammatory response， inhibiting cell apoptosis and pyroptosis， improving oxidative stress， and other effects in the treatment of severe pneumonia. Among these pathways， it was found that all of them regulated inflammation to treat severe pneumonia. Therefore， reducing inflammation is the core mechanism by which Chinese medicine treats severe pneumonia. This review provides direction for the clinical treatment of severe pneumonia and offers a scientific basis for the research and development of new drugs.