Objective To analyze the disease burden and trends of bladder cancer in China and globally from 1992 to 2021.  Methods Using the GBD 2021 database, the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) rates of bladder cancer in China and globally from 1992–2021 were analyzed. Average annual percentage change (AAPC) was calculated using Joinpoint regression. Subgroup analyses by sex and age were conducted, and a Bayesian age-period-cohort (BAPC) model was used to predict trends in age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) for the next 15 years.  Results In 2021, China reported 106 000 new cases (ASIR: 5.14/100 000), 571 000 prevalent cases (age-standardized prevalence rate, ASPR: 26.61/100 000), 43 000 deaths (ASMR: 2.34/100 000), and a DALY rate of 45.31/100 000. From 1992–2021, China showed upward trends in ASIR and ASPR but declines in ASMR and DALYs, while global ASIR, ASMR, and DALYs decreased overall with slow ASPR growth. The peak cases in China and globally were both concentrated in the 65-79 age group, with a significantly higher burden on males than females. In China, smoking-related ASMR and ASDR exceeded global averages and rose, whereas high glucose-related indexes were lower and declined. Projections for 2021–2036 indicated that the global incidence and mortality rates would be rising, but ASIR/ASPR would be declining, while in China, the incidence rate would continue to rise, and the mortality rate will stabilize, with a significant increase in ASIR and a gradual decrease in ASPR.  Conclusion From 1992 to 2021, the incidence of bladder cancer in China has shown a continuous upward trend and is projected to persist in the future, with significant gender and age differences. Particular attention should be given to elderly males aged 85-89. The disease burden of bladder cancer attributable to smoking continues to rise, highlighting the urgent need to strengthen tobacco control policies.

Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.

To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

Liver diseases, particularly cirrhosis and cancer, significantly threat human health for a long time, and their diagnosis and treatment are important topics in medical research. Tradi-tional diagnostic and therapeutic approaches have focused on biochemical properties of liver diseases, yet often neglecting the mechanical microenvironment of liver at molecular, cellular, and tissue levels. This oversight makes it difficult to meet clinical needs. Recent advancements in biomechanics and mechanobiology have underscored the importance of mechanical properties of liver in understanding disease mechanisms, presenting profound implications for both basic research and clinical practice. However, there is still a lack of in-depth exploration of the mechanical properties of liver in both basic research and clinical treatment, making it unclear of the specific mechanisms and application scenarios. The authors propose and introduce the emerging field of liver mechanomedicine, examine the biomechanical properties of liver and their alterations during disease progression, elucidate mechanobiological mechanisms of cellular mechanical responses and signal transduction, explore the application of mechanical characteristics in the diagnosis and treatment of liver diseases, aiming to provide a new perspective for basic research and clinical practice.

Aim To investigate the effect of Zhenwu decoction on inflammation,oxidative stress and apopto-sis of human glomerular epithelial cells(HGEC)in-duced by lipopolysaccharide(LPS)based on Nrf2/HO-1 signaling pathway,and to explore the underlying mechanism.Methods HGEC were treated with LPS(1.0 mg·L-1)for 24 h to construct an oxidative damage model.On this basis,2.5％,5％and 10％Zhenwu decoction-containing serum were added to the low,medium and high dose groups of Zhenwu decoc-tion,and a normal group was set up.The changes of cell activity were assessed by MTT method and LDH method.The contents of TNF-α,IL-6,IL-10,SOD,CAT,GSH-Px,ROS and MDA in each group were de-tected by ELISA.The apoptosis of each group was de-tected by flow cytometry.The mRNA and protein ex-pressions of Bax,Bcl-2,caspase-3,caspase-9 and Nrf2/HO-1 pathway were detected by RT-qPCR and Western blot,respectively.Results Compared to the normal group,the model group of HGEC exhibited increased levels of inflammatory cytokines,enhanced oxidative stress response and aggravated apoptosis;after inter-vention with various doses of Zhenwu decoction,the in-flammatory levels in HGEC were reduced,oxidative damage and apoptosis were effectively ameliorated,and the mRNA and protein expression levels of the Nrf2/HO-1 signaling pathway were upregulated.Conclu-sions Zhenwu decoction can protect HGEC from LPS-induced inflammation and oxidative damage and im-prove apoptosis.The mechanism may be related to the activation of Nrf2/HO-1 signaling pathway.

Objective:To understand the usage situation of e-cigarettes among residents aged 18-65 in Beijing, explore the relationship between e-cigarette use and cigarette smoking as well as smoking cessation behaviors, and provide scientific support for the developing and improving policies and measures related to e-cigarettes.Methods:Using 19 684 residents data from the Beijing Non-communication Chronic Disease and Risk Factors Surveillance in 2022, complex sampling weighted methods were used to estimate proportions, and complex sampling logistic regression analysis was applied to explore the relationship between e-cigarette use, cigarette smoking, and smoking cessation.Results:Among all study participants, the proportion of those who had ever used e-cigarettes was 3.36%, with the current e-cigarette use at 1.26%. The proportion of current e-cigarette users (1.87%) and the former e-cigarette users (3.47%) were higher ( χ2=64.70, P<0.001) among males compared to females (0.60% and 0.64% respectively). The top three reasons for using e-cigarettes were wanting to quit smoking, perceiving e-cigarettes as less harmful, and enjoying the flavors of e-cigarettes. 83.54% of e-cigarette users started with cigarettes. The results of the complex sampling multivariable logistic regression analysis showed that current smoking ( OR=61.35, 95% CI: 36.98-101.76) and former smoking ( OR=31.20, 95% CI: 15.52-62.71) were positively associated with e-cigarette, while current e-cigarette use ( OR=0.13, 95% CI: 0.04-0.39) was negatively associated with quitting cigarette smoking. Conclusions:The proportion of e-cigarette use in Beijing was relatively low. E-cigarette use was associated with cigarette use and was not conducive to smoking cessation. Therefore, stronger regulatory measures and health education campaigns regarding the risks of e-cigarettes should be implemented.

Objective:To analyze the prevalence trends and epidemiological characteristics of cardiometabolic multimorbidity(CMM) in Beijing from 2005 to 2022.Methods:A series of representative cross-sectional surveys were conducted in Beijing between 2005 and 2022 using a stratified multistage cluster random sampling method. A total of 110 496 permanent residents aged 18-79 years participated in face-to-face interviews, physical examinations, and laboratory testing. Complex sampling logistic regression models were employed to identify factors associated with CMM, and Joinpoint regression was used to assess temporal trends in prevalence. Results:The prevalence of CMM was 22.3% in 2005 and 24.3% in 2022, with an average annual percent change of 0.1%(95% CI -1.3%-1.3%, P>0.05). In rural areas, the prevalence increased by 1.3% per year(95% CI 0.2%-2.6%, P<0.05), while among obese individuals, it decreased by 1.0% annually( P<0.05). The most common CMM patterns were hypertension combined with dyslipidemia(13.2%), hypertension combined with diabetes(7.0%), and diabetes combined with dyslipidemia(5.8%). The prevalence of hypertension and dyslipidemia comorbidity showed a long-term decline among females, those aged 60-79 and obese individuals( P<0.05). In contrast, the prevalence of hypertension and diabetes comorbidity increased over time in rural residents and individuals with normal body weight( P<0.05). Furthermore, diabetes and dyslipidemia comorbidity rates increased significantly among males, adults aged 18-59 years, those with a college education or above, rural residents and individuals with normal body weight( P<0.05). Multivariable logistic regression indicated that male, older age, overweight, obese, and lower education level were independently associated with a higher risk of CMM( P<0.05). Conclusion:From 2005 to 2022, the prevalence of CMM remained high among adults in Beijing. While prevalence decreased among obese individuals, it increased significantly in rural areas. Hypertension combined with dyslipidemia was the most common multimorbidity pattern throughout the study period.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To develop an intelligent recognition and precise segmentation technique using ultrasonic images，and to enhance diagnostic efficiency and accuracy.Methods:A total of 1，430 patients diagnosed with uterine fibroids through transvaginal ultrasonography at the Maternal and Child Health Hospital of Guangming from November 2020 to October 2024 were retrospectively included. Ultrasonic images were manually annotated by two experienced physicians and reviewed by a senior expert. The Mask DINO deep learning model was used for lesion segmentation，and the segmentation results were optimized using ellipse fitting technology. Model performance was evaluated using the Dice coefficient，intraclass correlation coefficient（ICC），mean absolute error（MAE），and measurement accuracy.Results:In the test set of 286 cases，the average Dice coefficient of model prediction was 0.992，indicating extremely high segmentation accuracy. The average accuracy of lesion identification by the model was 0.909，with 241 correctly identified samples，19 basically correct samples，and 26 incorrect samples. In terms of long and short axis measurements，the ICC of the model's direct predictions were 0.871（short axis）and 0.784（long axis），with MAE of 0.436 cm（short axis）and 0.508 cm（long axis）. After optimization with ellipse fitting，the ICC increased to 0.893（short axis）and 0.866（long axis），and the MAE decreased to 0.191 cm（short axis）and 0.274 cm（long axis），the measurement accuracy improved significantly.Conclusions:The intelligent recognition and precise segmentation technique for uterine fibroids based on ultrasonic images constructed in this study performed excellently in lesion segmentation and measurement，it can significantly improve the efficiency and accuracy of diagnosis.