Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

ObjectivePhotoacoustic pump-probe imaging can effectively eliminate the interference of blood background signal in traditional photoacoustic imaging, and realize the imaging of weak phosphorescence molecules and their triplet lifetimes in deep tissues. However, background differential noise in photoacoustic pump-probe imaging often leads to large fitting results of phosphorescent molecule concentration and triplet lifetime. Therefore, this paper proposes a novel triplet lifetime fitting method for photoacoustic pump-probe imaging. By extracting the phase of the triplet differential signal and the background noise, the fitting bias caused by the background noise can be effectively corrected. MethodsThe advantages and feasibility of the proposed algorithm are verified by numerical simulation, phantom and in vivo experiments, respectively. ResultsIn the numerical simulation, under the condition of noise intensity being 10% of the signal amplitude, the new method can optimize the fitting deviation from 48.5% to about 5%, and has a higher exclusion coefficient (0.88>0.79), which greatly improves the fitting accuracy. The high specificity imaging ability of photoacoustic pump imaging for phosphorescent molecules has been demonstrated by phantom experiments. In vivo experiments have verified the feasibility of the new fitting method proposed in this paper for fitting phosphoometric lifetime to monitor oxygen partial pressure content during photodynamic therapy of tumors in nude mice. ConclusionThis work will play an important role in promoting the application of photoacoustic pump-probe imaging in biomedicine.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Cholelithiasis is a common biliary system disease with a high incidence worldwide. Abnormal lipid metabolism has been shown to play a key role in the mechanism of gallstones. Therefore, recent research literature on the genes, proteins, and molecular substances involved in lipid metabolism during the pathogenesis of gallstones has been conducted. This study aimed to determine the role of lipid metabolism in the pathogenesis of gallstones and provide insights for future studies using previous research in genomics, metabolomics, transcriptomics, and other fields.

Objective To propose a whole-life cycle management model for valvular heart disease (VHD), systematically elucidate its underlying logic and implementation pathways, and concurrently review and analyze its preliminary application outcomes. Methods Since 2020, West China Hospital of Sichuan University has established a management system encompassing "assessment-decision-intervention-follow-up", including: (1) a risk-stratified, tiered management pathway; (2) six core functions ("promotion, screening, prevention, diagnosis, treatment, and rehabilitation") coordinated by disease-specific managers; (3) an intelligent decision support information platform; and (4) a collaborative network of multidisciplinary teams and regional academic alliances. To evaluate the effectiveness of this management model, we retrospectively included three cohorts: (1) the population screened by echocardiography from 2020 to 2024, analyzing the detection rate of aortic valve disease and risk stratification; (2) patients enrolled in the whole-life cycle management from April 2021 to December 2024, assessing follow-up outcomes, hospital satisfaction, and changes in quality of life; (3) patients who underwent transcatheter aortic valve replacement (TAVR) from January 2022 to January 2024, evaluating the one-year all-cause mortality rate, perioperative complications, and improvements in New York Heart Association (NYHA) classification. Results Between 2020 and 2024, a total of 583 874 individuals underwent echocardiographic screening. A total of 48 089 patients with aortic valve disease were identified, including 3 401 (7.1%) high-risk patients, 18 657 (38.8%) moderate-risk patients, and 26 031 (54.1%) low-risk patients. Among them, 2 417 patients were enrolled in whole-life cycle management. Patient satisfaction scores showed a yearly increase, rising from 73.89 points before 2020 to 93.74 points in 2024. The 1-year mortality rate in the TAVR cohort decreased to 5.3%, significantly lower than the 8.2% observed under early standard management between 2014 and 2019 (P<0.01). Conclusion Through process optimization and resource integration, the VHD whole-life cycle management model has demonstrated significant effectiveness in standardizing diagnostic and follow-up procedures, enhancing patient satisfaction and quality of life, and reducing mortality. These outcomes highlight its practical value for broader implementation in China.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective:To establish a prediction method of diopter based on sequence of ocular biological parameters.Methods:A stratified random cluster sampling method was used to extract the dataset. The dataset consisted of data collected from January 2022 to January 2023 by the Eye & ENT Hospital, Fudan University, from children aged 5 to 13 years in 2 key schools and 2 general schools of Yangpu District, Shanghai. Children’s ocular biological parameters, including sex, age, diopter, axial length, corneal curvature, and anterior chamber depth were collected. The slope of the optimally fitted straight line was calculated using the least squares method. The least square-back propagation (BP) neural network model was established by combining baseline data and the pre-processed rate of the change of ocular biological parameters. The dataset was divided into the training set and the validation set according to the ratio of 8:2 for five-fold cross-validation. The model performance was evaluated by using the mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), correlation coefficient R, and coefficient of determination R2. Results:The optimal performances of R2, R, RMSE, MAE, and MSE of the least square-BP neural network model were 0.96, 0.981 9, 0.214 2, 0.139 9 D, 0.045 9, respectively. The regression equation between the predicted value and the true value of the diopter was y=0.97 x+ 0.014 8, R2=0.97, with good correlation. In the internal verification, MAE values of the diopter at three, six, nine, and twelve months of follow-up were 0.110 1, 0.136 0, 0.153 7, and 0.184 8 D, respectively, which achieved clinically acceptable performance (less than 0.25 D). In the external validation, the errors were less than 0.25 D at all ages. Conclusions:A prediction method of diopter based on sequence of ocular biological parameters was successfully developed.

Aim To explore the potential genes and mechanism of alisol B in the treatment of non-small cell lung cancer(NSCLC).Methods The proliferation and migration of NSCLC cells were detected by CCK-8 and Transwell.Genes of NSCLC and alisol B were col-lected through TCGA and compound gene prediction database,and their intersection genes were obtained.The network of protein-protein interaction(PPI)was constructed by using String database,and the top 20 key nodes were screened out,and the prognosis-related proteins related to the prognosis of NSCLC were screened out by using R language,and the intersection of them was obtained.The potential mechanism of ali-sol B on NSCLC was explored by KEGG and GO en-richment analysis and the relationship between related genes and immune cells,which was verified by cell-lev-el experiments.Results Alisol B inhibited the cell activity and migration ability of NSCLC cells.Five im-portant genes were identified by network pharmacologi-cal analysis:CCNE1,CDK1,COL1A1,COL1A2 and COL3A1.The results of cell experiment showed that al-isol B down-regulated the expression of Cyclin E1,CDK1 and COL1A2 in NSCLC cells.In addition,alisol B could inhibit the expression of COL1A2 and M2 macrophage marker CD206 in macrophages.Conclu-sions Alisol B may inhibit the proliferation of tumor cells by down-regulating CDK1 and Cyclin E1,and may affect the function of macrophages by inhibiting COL1A2,thus regulating the tumor immune microenvi-ronment and inhibiting NSCLC.