Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

Objective:It aimed to provide a review on health insurance literacy,including its definition,measurement tools,influencing factors,and its effects on insurance enrollment and the healthcare utilization.Methods:A literature review was conducted by searching for the keywords"health insurance literacy"(Chinese/English)in the CNKI and PubMed databases.Results:The findings indicated that international research had developed a relatively mature definition of health insurance literacy and measurement tools,while domestic research remained in early stage.Health insurance literacy was significantly effected by age,gender,education level,and income.Conclusion:It is suggested to enhance health insurance literacy,develop localized measurement tools,implement targeted educational programs,and promote broader public understanding of health insurance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

In recent years,the incidence and mortality rates of hepatocellular carcinoma in China have gradually become close to each other,reflecting persistent limitations in current diagnostic and therapeutic strategies.Given the close association between hepatocellular carcinoma development and the progression of precancerous lesions,the expert panel released the first edition of the Multidisciplinary Experts Consensus on Diagnosis and Treatment of Precancerous Lesions of Hepatocellular Carcinoma in 2020 and updated it in 2023,aiming to advance early-intervention strategies and improve overall patient survival rates.This article provides an in-depth interpretation of the key imaging diagnostic points outlined in the consensus,aiming to offer radiologists at all levels with a robust reference for early identification and diagnosis of hepatocellular carcinoma precancerous lesions,thereby facilitating critical support for timely patient intervention and treatment.

Aim To explore the effects of Kui Jie Kang(KJK)on modulating the farnesoid X receptor(FXR)pathway in the gut microbiota and bile acid metabolism in mice with ulcerative colitis(UC).Methods Mice were subjected to DSS-induced UC and randomly as-signed to the control(CON),model(MOD),and two KJK-dosed groups(KJK.H at 12.8 g·kg-1,KJK.L at 3.2 g·kg-1).Mouse body weight was recorded,and disease activity index(DAI)was scored.The his-topathological changes in colonic tissue were observed via HE staining,and the number of goblet cells and mucosal layer repair were assessed using PAS and Al-cian blue staining.Bile acid content in feces was measured using LC-MS/MS,gut microbiota composition was analyzed by 16S rRNA gene sequencing,and the expression of FXR target genes and related proteins was detected by RT-qPCR and Western blot.Results KJK significantly ameliorated colonic shortening,de-creased disease activity index in UC mice,reduced his-topathological scores,increased the number of goblet cells and mucus secretion,altered the levels of primary and secondary bile acids,and increased the relative a-bundance of beneficial bacteria such as Lactobacillus.Additionally,it significantly upregulated the expression of FXR and FGF15 mRNA and protein in colonic tissue and downregulated the expression of hepatic CYP7A1 mRNA,and the correlation analysis in this study clearly revealed a significant correlation between bile acid me-tabolism disorders and gut microbiota imbalance in UC.Conclusion KJK activates the intestinal FXR-FGF15-CYP7A1 pathway,thereby regulating bile acid metabolism and restoring gut microbiota balance,which may be key to its improvement of UC.

Objective To construct a hybrid decision-making model that integrates knowledge-driven and data-driven approaches,and to apply it to the etiological diagnosis of ventricular tachycardia(VT).Methods Clinical practice guidelines,expert consensus documents,and medical literature in the field of ar-rhythmia diseases from 2018 to 2023 were retrieved as knowledge sources.Retrospective electronic medical re-cord data of VT patients from Fuwai Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College,from 2013 to 2023 were collected as the dataset.A knowledge-driven model was constructed using a knowledge-rule-based approach to establish clinical pathways.A three-class machine learning model for VT eti-ology diagnosis was developed based on real-world data,and the best-performing model was selected as the rep-resentative of the data-driven approach.The machine learning model was embedded into the decision nodes of the clinical pathway in the form of custom operators,forming the hybrid model.The precision,recall,and F1 score of the three models were evaluated.Results Three clinical practice guidelines were included as knowl-edge sources for the knowledge-driven model.A total of 1305 patient records were collected as the dataset,and five machine learning models were constructed,with the XGBoost model performing the best.The hybrid model adopted a knowledge-driven decision-making framework,embedding the XGBoost model into the decision nodes of a two-level classification.The precision,recall,and F1 scores of the three models were as follows:the knowledge-driven model achieved 80.4％,79.1％,and 79.7％;the data-driven model achieved 88.4％,88.5％,and 88.4％;and the hybrid model achieved 90.4％,90.2％,and 90.3％.Conclusions The hybrid model integrating knowledge-driven and data-driven approaches demonstrated higher accuracy,and all its deci-sion outcomes were based on evidence-based practices,aligning more closely with the actual diagnostic reason-ing of clinicians.Further rigorous validation is needed to assess the feasibility of widely applying the hybrid model in the medical field.

Objective:To explore the diagnostic value of intratumoral area(ITA)and peritumoral area(PTA)ultrasound image-based bioin-formatics models for small breast cancer.Methods:We retrospectively analyzed data of 305 breast lesions from 292 patients with small breast nodules(diameter≤2 cm)who were treated at People's Hospital of Xinjiang Uygur Autonomous Region between January 2021 and January 2025.The lesions were randomly assigned into the training(214 lesions)and validation sets(91 lesions)in a 7:3 ratio.Radiomics fea-tures were extracted from the intertumoral area(ITA)and peritumoral area(PTA)regions at 2,4,6,and 8 mm,followed by feature selection and dimensionality reduction.A Logistic regression(LR)algorithm was used to construct a model.The performance of the models were eval-uated via receiver operating characteristic(ROC)curve analysis,Hosmer-Lemeshow test,and decision curve analysis(DCA).Results:In the training set,the areas under the ROC curves(AUC)for the ITA,2 mm PTA,and 2 mm fusion models were 0.869,0.897,and 0.909,respect-ively.In the test set,these respective AUC values were 0.813,0.825,and 0.840.For breast lesions≤2 cm,<1 cm,and 1-2 cm,the overall ac-curacies of the 2 mm fusion model were 81.0%,82.7%,and 80.1%,respectively,whereas the respective overall accuracies of BI-RADS were 76.4%,81.7%,and 73.6%.Conclusions:ITA and PTA ultrasound imaging-based radiomics models had a high diagnostic value for small breast cancers.The fusion model can effectively improve predictive performance,outperforming the BI-RADS classification in diagnosing small breast lesions of different diameters.Thus,these models have the potential to serve as an auxiliary diagnostic tool in clinical practice.

AIM To evaluate the quality of Croci Stigma from different producing areas.METHODS The analysis was performed on a 25 ℃ thermostatic Waters Acquity UPLC HSS T3 column(2.1 mm× 100 mm,1.8μm),with the mobile phase comprising of 0.1％phosphoric acid-acetonitrile flowing at 0.35 mL/min in a gradient elution manner,and the detection wavelengths were set at 254,440 nm.The UPLC fingerprints were established,after which orthogonal partial least squares discriminant analysis was performed,picrocrocin,crocin-Ⅰ,crocin-Ⅱ,crocin-Ⅲ,crocin-Ⅳ contents and chromaticity values(L*,a*,b*,E*ab)were determined,Pearson correlation analysis was adopted in the investigation of correlations between chromaticity values and internal constituent contents.RESULTS There were 14 common peaks in the fingerprints for 22 batches of medicinal materials with the similarities of more than 0.98.Various batches of medicinal materials were clustered into 2 types,7 quality difference components were screened.crocin-Ⅰ content in medicinal materials from different producing areas demonstrated significant differences(P＜0.05);the redder the color of medicinal material,the higher the contents of crocins.Picrocrocin,crocin-Ⅰ,crocin-Ⅱ,crocin-Ⅳ contents displayed highly significant correlations with colorimetric values(P＜0.01),while crocin-Ⅲ content exhibited no significant correlation with the latter(P＞0.05).CONCLUSION This accurate and reliable method can provide references for the quality control and color-quality relationship elucidation of Croci Stigma.