Objective To develop and validate an interpretable machine learning model for predicting coronary heart disease (CHD) in patients with type 2 diabetes mellitus (T2DM), providing a clinical decision support tool for early screening and risk stratification. Methods This multi-center retrospective cohort study included 2,187 patients with T2DM. The training cohort (n = 1,667) was randomly partitioned into training and internal validation sets at a 7:3 ratio, while 520 patients formed the external validation cohort. Feature selection was performed using Spearman correlation analysis, Variance Inflation Factor (VIF), univariate logistic regression, Boruta, and LASSO regression. Based on 11 identified variables, 15 machine learning models were developed and compared. Model performance was comprehensively evaluated via Area Under the Curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, Matthews Correlation Coefficient (MCC), Brier score, calibration curves, and Decision Curve Analysis (DCA). Model interpretability was addressed using SHAP (SHapley Additive exPlanations). A sensitivity analysis was performed to evaluate the robustness of the model. Results Among all candidate models, the Gated Recurrent Unit (GRU) model demonstrated the superior performance. It achieved an AUC of 0.7625 and a sensitivity of 0.7466 in the test set. In the external validation set, the AUC was 0.7188, accuracy was 0.7192, sensitivity was 0.5639, specificity was 0.7726, and the Brier score was 0.1876, indicating robust discrimination, calibration, and generalizability. SHAP analysis revealed that age, sex, and LDL-C were the most significant predictors, followed by fasting blood glucose (FBG), systolic blood pressure (SBP), sleep duration, and sweet food intake. These findings underscore the critical roles of demographic characteristics, lipid metabolism, and lifestyle factors in predicting CHD risk among T2DM patients. Conclusion This study established an interpretable GRU-based predictive model using multi-center real-world data, which effectively identifies high-risk CHD patients within the T2DM population. With its strong predictive performance, reliable calibration, and high interpretability, the model holds potential to support non-specialists in conducting early screening and personalized interventions.

Arthritis is jointly influenced by IR and inflammation, and the CTI is recognized as a novel marker for the comprehensive evaluation of inflammation and IR. Our study aimed to investigate the association between CTI and arthritis and to compare the differences in the TyG index and obesity indicators (including the TyG–BMI, TyG–WC, and TyG–WHtR) with respect to arthritis occurrence. Our cross-sectional study utilized data from the NHANES, which was conducted between 2001 and 2010. Arthritis diagnosis relies on self-reported confirmation by a physician. Weighted logistic regression models were used to examine the associations between CTI, TyG, and obesity-derived indicators and arthritis, and weighted RCS models were applied to explore nonlinear effects. Furthermore, threshold effect analysis, subgroup analyses, interaction tests, and ROC curve analyses were performed. The results indicated that after full adjustment for confounders, the CTI was positively associated with arthritis regardless of whether it was treated as a continuous or categorical variable (P < 0.05). TyG, TyG–BMI, TyG–WC, and TyG–WHtR were positively associated with arthritis. Notably, when treated as categorical variables, the associations between TyG, TyG–BMI, TyG–WC, and TyG–WHtR and arthritis were positive, but Q2 was not statistically significant (P > 0.05). Except for TyG, RCS regression analysis and subgroup analysis consistently confirmed positive associations. Threshold effect analysis indicates that the associations of CTI and TyG with arthritis exhibit significant threshold effects. The TyG–WHtR showed the best diagnostic efficacy (AUC = 0.629). CTI, TyG–BMI, TyG–WC, and TyG–WHtR are all positively associated with the occurrence of arthritis, among which TyG–WHtR shows a stronger predictive ability. CTI has the potential to become an effective biomarker for the early identification of arthritis risk and the improvement of patient prognosis in young adults.

Abstract Inflammatory bowel disease (IBD), including ulcerative colitis (UC), is a prevalent global chronic gastrointestinal disorder with rising incidence, burdening healthcare and patients; current treatments are often ineffective. Thus, there is an urgent need to find efficient therapeutic approaches or drugs. Tea polyphenols(TP), natural compounds derived from China's millennia-old tea culture, have demonstrated potent anti-inflammatory and antioxidant properties. Network pharmacology analysis revealed that tea polyphenols exert therapeutic effects on ulcerative colitis (UC) by modulating inflammation and reactive oxygen species (ROS) -mediated signaling pathways. However, their clinical application is severely limited by suboptimal targeting efficacy, low bioavailability and adverse effects. To address these challenges, we developed oral tea polyphenol nanoparticles (TP-NPs) to enhance therapeutic delivery efficiency. Thermodynamic analysis and release kinetics showed that TP-NPs significantly improve the stability and sustained-release properties of tea polyphenols, maintaining prolonged therapeutic concentrations. Both in vitro and in vivo studies demonstrated that TP-NPs exhibit strong resistance to gastric acid and preferentially accumulate at sites of intestinal inflammation. In a murine model of UC, TP-NPs effectively reduced colonic ROS levels, thereby mitigating oxidative stress-induced damage. These findings highlight the dual anti-inflammatory and antioxidant properties of TP-NPs, underscoring their potential as an innovative therapeutic strategy for UC.

Pharmaceutical and personal care products (PPCPs), emerging pollutants, may cause chronic inflammatory and metabolic diseases by inducing metabolic disorders. To explore the underlying mechanisms, this study used network toxicology and molecular docking, focusing on four representative diseases: digestive system diseases, rheumatoid arthritis (RA), non-alcoholic fatty liver disease (NAFLD), and Parkinson's disease. By integrating data from GeneCards, OMIM, and STRING databases, we found 255, 132, 128, and 117 intersection targets between PPCPs and these diseases respectively. Protein-protein interaction (PPI) networks highlighted core hubs like BCL2, IL1B, and PTGS2. Molecular docking showed strong binding affinities (e.g., IL1ß: -22.18 kcal/mol; CASP3: -23.23 kcal/mol). GO/KEGG analyses revealed PPCPs disrupt shared pathways, such as the AGE - RAGE signaling in digestive diseases and RA, PI3K-Akt-mediated insulin resistance in NAFLD, and neuroinflammation via PTGS2 inhibition in Parkinson's. Notably, 90 % of top hub genes (e.g., STAT3, AKT1) overlapped across diseases, forming an "inflammation-apoptosis" axis. Our findings suggest PPCPs may exert toxicity through cross-organ interactions via conserved molecular networks, offering insights for environmental risk assessment and cross-disease therapeutic strategy development.

The Metabolic Vulnerability Index (MVX), a novel composite marker of inflammation and malnutrition, has an undetermined prognostic value in stage 4 cardiovascular-kidney-metabolic (CKM) syndrome. To investigate the association between MVX and the risk of all-cause mortality among patients with stage 4 CKM syndrome. We included 20,927 participants with stage 4 CKM syndrome in the UK Biobank. Sex-specific MVX scores were calculated by aggregating six biomarkers (glycoprotein acetyls (GlycA), small high-density lipoprotein particles (sHDL), valine, leucine, isoleucine, and citrate). We applied Cox proportional-hazards models to investigate the association between MVX and all-cause mortality and evaluated the performance of MVX in predicting mortality. During a median 13.45 years of follow-up (IQR:12.54–14.32 years), a total of 4,613 participants (22.04

背景 肥胖可通过多种途径影响糖尿病视网膜病变（DR）的发生和发展，但目前关于肥胖通过非脂质代谢途径介导而影响DR的研究报道较少见。目的 探究非脂质代谢物在肥胖与DR间的中介作用。方法 2023年8月，基于全基因组关联数据（GWAS），通过孟德尔随机化（MR）探究非脂质代谢物在肥胖与DR间的中介作用。结果 BMI增大（OR=1.78,P=5.3E-12）、腰臀比（WHR）增大（OR=1.91,P=1.3E-10）均与DR发病风险升高有关。异亮氨酸（OR=0.62,P=0.039）、丙酮酸（OR=0.60,P=0.039）、白蛋白（OR=0.65,P=0.002）、糖蛋白（OR=0.92,P=0.002）、双烯基与双键的比率降低（OR=0.93,P=0.048）均与DR发病风险升高有关。BMI与异亮氨酸（OR=1.21,P=1.0E-08）、糖蛋白（OR=1.33,P=3.2E-14）、丙酮酸（OR=1.08,P=0.03）呈正向因果关联，与白蛋白（OR=0.93,P=0.04）、双烯基与双键的比率（OR=0.82,P=2.8E-05）呈负向因果关联；WHR与异亮氨酸（OR=1.34,P=3.4E-08）、糖蛋白（OR=1.26,P=1.2E-04）呈正向因果关联。异亮氨酸（β=-0.16,P=0.019）、糖蛋白（β=-0.05,P=0.029）、丙酮酸（β=-0.07,P=0.027）、双烯基与双键的比率（β=0.02,P=0.036）介导BMI与DR间的因果关联，异亮氨酸（β=-0.21,P=7.2E-04）、糖蛋白（β=-0.03,P=0.031）介导WHR与DR间的因果关联。结论 肥胖与DR有正向因果关联，其中BMI与DR的关联由异亮氨酸、糖蛋白、丙酮酸、双烯基与双键的比率介导，WHR与DR的关联由异亮氨酸、糖蛋白介导，非脂质代谢产物在肥胖与DR间具有中介作用。

ABSTRACT Autoimmune inflammatory diseases (AIDs) are genetically linked disorders with unclear causal links to brain functional networks. Using bidirectional two‐sample Mendelian randomization (MR) on GWAS data from 18 AIDs and 1,366 brain imaging‐derived phenotypes (n = 8,428), we identified significant associations, including reduced left striatal activity increasing multiple sclerosis risk (OR = 0.59), left uncinate fasciculus activity elevating systemic lupus erythematosus risk (OR = 3.72), and asymmetric cerebellar peduncle effects in cutaneous vasculitis (left: OR = 0.11; right: OR = 8.57) [exploratory finding with 24.8%–37.8% power]. Fibromyalgia suppressed cerebellar area VIIIa (β = −0.023). Sensitivity analyses, double machine learning, and >99% statistical power supported robustness. These findings suggest alterations in default mode, salience, and central executive networks contribute to AIDs pathogenesis, highlighting brain regions such as the striatum and cerebellar peduncles as potential therapeutic targets.

BACKGROUND:While dietary intervention was an important public health strategy for the prevention and intervention of metabolic dysfunction-associated fatty liver disease (MAFLD), the effect of diet-induced inflammation on MAFLD has not been studied in detail. Therefore, we aimed to analyze the relationship between dietary inflammatory index (DII) and MAFLD. METHODS:This study included data from the National Health and Nutrition Examination Survey 2017-2018. MAFLD was diagnosed based on the presence of hepatic steatosis, as determined by transient elastography, along with evidence of either overweight/obesity, type 2 diabetes mellitus, or metabolic dysfunction. DII was calculated using 27 dietary components collected through 24-hour dietary recall questionnaire. Weighted logistic regression was used to analyze the relationship between DII and MAFLD and its main components in three different models. Subgroup analyses were performed by age, sex, and alcohol use. RESULTS:A total of 1991 participants were included, and the MAFLD group had higher DII scores. After adjusting for age, sex, race, physical activity, smoking status, and alcohol use, the highest quartile of DII was associated with increased risk of MAFLD (OR:2.90, 95% CIs: 1.46, 5.75). Overweight/obesity, central obesity, low high density lipoprotein cholesterol (HDL-C) and high C-reactive protein (CRP) also shared the same characteristics in the main components of MAFLD. Results were consistent across subgroups (age, sex, and alcohol use). CONCLUSIONS:A higher DII diet was positively associated with the risk of MAFLD in American adults, particularly as related to overweight/obesity, central obesity, high CRP level, and low HDL-C level.

Vitamin C is an important micronutrient for human. Association between vitamin C and trouble sleeping was less studied. Therefore, the purpose of this study was to investigate the possible link between vitamin C in serum and trouble sleeping. The cross-sectional data was derived from the National Health and Nutrition Examination Survey (NHANES, 2017–2018). Trouble sleeping was measured by asking participants: “Have you ever told doctor had trouble sleeping”. Responses to this question was “yes” or “no”. vitamin C in serum was obtained by measuring the serum samples. We used multivariable binary logistic regressions to examine the possible link between vitamin C in serum and trouble sleeping, and then a subgroup analysis was performed. Moreover, the non-linear relationship between vitamin C in serum and trouble sleeping was further detected using a restricted cubic spline (RCS) model. A total of 3227 participants were included in the study. After adjusting all potential confounders, the results of multivariable logistic regression showed the significant negative association between vitamin C in serum and trouble sleeping(OR = 0.816; 95% CI:0.669 ~ 0.995). The significant inverse association was also found in female(OR = 0.713; 95% CI:0.546 ~ 0.931), age ≤ 65 years(OR = 0.773; 95% CI:0.600 ~ 0.996), and in participants with high cholesterol level(OR = 0.738; 95% CI:0.548 ~ 0.994). In addition, the RCS model demonstrated the significant non-linear relationship between vitamin C in serum and trouble sleeping (P value of nonlinear = 0.010). Our study demonstrates the significant negative association between vitamin C in serum and trouble sleeping.

BackgroundInflammation and obesity have been widely recognized to play a key role in Diabetes mellitus (DM), and there exists a complex interplay between them. We aimed to clarify the relationship between inflammation and DM, as well as the mediating role of obesity in the relationship.MethodsBased on the National Health and Nutrition Examination Survey (NHANES) 2005–2018. Univariate analyses of continuous and categorical variables were performed using t-test, linear regression, and χ2 test, respectively. Logistic regression was used to analyze the relationship between Systemic Immune-Inflammatory Index (SII) or natural logarithm (Ln)-SII and DM in three different models. Mediation analysis was used to determine whether four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product index (LAP), mediated the relationship between SII and DM.ResultsA total of 9,301 participants were included, and the levels of SII and obesity indicators (BMI, WC, LAP, and VAI) were higher in individuals with DM (p < 0.001). In all three models, SII and Ln-SII demonstrated a positive correlation with the risk of DM and a significant dose–response relationship was found (p-trend <0.05). Furthermore, BMI and WC were associated with SII and the risk of DM in all three models (p < 0.001). Mediation analysis showed that BMI and WC mediated the relationship between SII with DM, as well as Ln-SII and DM, with respective mediation proportions of 9.34% and 12.14% for SII and 10.23% and 13.67% for Ln-SII (p < 0.001).ConclusionOur findings suggest that increased SII levels were associated with a higher risk of DM, and BMI and WC played a critical mediating role in the relationship between SII and DM.

AbstractObjectiveTo examine the relationship between C‐reactive protein (CRP) and knee pain, and further explore whether this association is mediated by obesity.MethodsThe population was derived from 1999 to 2004 National Health and Nutrition Examination Survey. Logistic regression was used to analyze the relationship between CRP and knee pain in three different models, and the linear trend was analyzed. A restricted cubic spline model to assess the nonlinear dose−response relationship between CRP and knee pain. Mediation analyses were used to assess the potential mediating role of obesity. Subgroup analyses and sensitivity analyses were performed to ensure robustness.ResultsCompared with adults with lower CRP (first quartile), those with higher CRP had higher risks of knee pain (odds ratio 1.39, 95% confidence interval 1.12−1.72 in third quartile; 1.56, 1.25−1.95 in fourth quartile) after adjusting for covariates (except body mass index [BMI]), and the proportion mediated by BMI was 76.10% (p < .001). BMI and CRP were linear dose−response correlated with knee pain. The odds ratio for those with obesity compared with normal to knee pain was 2.27 (1.42−3.65) in the first quartile of CRP, 1.99 (1.38−2.86) in the second, 2.15 (1.38−3.33) in the third, and 2.92 (1.72−4.97) in the fourth.ConclusionObesity mediated the systemic inflammation results in knee pain in US adults. Moreover, higher BMI was associated with higher knee pain risk in different degree CRP subgroups, supporting an important role of weight loss in reducing knee pain caused by systemic inflammation.

OBJECTIVE:Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities.METHOD:Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis.RESULT:The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects.CONCLUSION:The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

背景　慢性肾脏病（CKD）是我国乃至全球不可忽视的公共卫生问题。目前，国内关于不同亚型CKD发病趋势预测的相关研究鲜见报道。目的　预测2020—2040年中国5种亚型CKD的发病趋势，为CKD的防控提供参考。方法　本研究与2023年4—5月，收集全球疾病负担研究（GBD）数据库1990—2019年我国5种亚型CKD的年龄标准化发病率（ASIR）和发病人数。采用变化率（%）和平均年度变化百分比（AAPC）描述和分析我国5种亚型CKD的发病现状。运用Prophet模型预测我国2020—2040年5种亚型CKD的ASIR和发病人数。结果　1990—2019年我国5种亚型CKD的ASIR和发病人数均呈现上升趋势，其中高血压肾病的上升趋势最为明显（AAPC=0.75，P<0.05）。2019年男性2型糖尿病肾病、1型糖尿病肾病、肾小球肾炎肾病和高血压肾病的ASIR和发病人数均高于女性，而女性其他类型肾病的ASIR和发病人数高于男性。2型糖尿病肾病、高血压肾病和其他类型肾病在65~74岁年龄组的发病人数较高。1型糖尿病肾病和肾小球肾炎肾病的发病人数多集中于小于5岁年龄组。本研究预测结果表明，预计到2040年，2型糖尿病肾病的ASIR和发病人数分别为23.27/105（80%UI=20.64/105~26.08/105）和755 375（80%UI=702 827~811 409）例，1型糖尿病肾病的ASIR和发病人数分别为0.60/105（80%UI=0.47/105~0.73/105）和10 625（80%UI=9519~11 787）例，肾小球肾炎肾病的ASIR和发病人数分别为3.88/105（80%UI=3.01/105~4.79/105）和87 050（80%UI=74 470~100 460）例，高血压肾病的ASIR和发病人数分别为15.35/105（80%UI=13.53/105~17.29/105）和470 214（80%UI=437 598~504 817）例，其他类型肾病的ASIR和发病人数分别为127.68/105（80%UI=102.41/105~154.68/105）和3 901 317（80%UI=3 622 415~4198 720）例。结论　1990—2019年我国5种亚型CKD的ASIR和发病人数均呈现上升趋势。2020—2040年中国2型糖尿病肾病、高血压肾病和其他类型肾病的ASIR和发病人数仍然呈现上升趋势，虽然1型糖尿病肾病和肾小球肾炎肾病的发病人数逐年增长，但总体的ASIR呈现下降趋势。未来应针对不同亚型的CKD制定相应的防控策略。

目的 基于机器学习算法构建冠心病风险评估模型,并比较极限梯度上升(eXtreme Gradient Boosting,XGBoost)模型和逻辑回归(Logistic Regression,LR)在预测冠心病患病风险中的效能,为冠心病的诊断提供计算机辅助方法.方法 通过对kaggle社区上发布的冠心病数据集进行预处理后,将特征变量纳入logistic和XGBoost模型中,对其查准率、召回率、ROC曲线下面积(AUC)进行对比,以验证模型性能.结果 XGBoost模型相对于传统的logistic回归模型预测性能更优,其中,年龄、性别、血糖水平、身体质量指数和收缩压为冠心病的主要危险因素.结论 冠心病风险预测模型能为冠心病早期预防控制及诊断提供参考依据.

Background:Bladder cancer (BCa) is one of the most common urological malignancies worldwide. This study examines the global epidemiological profile of BCa incidence and mortality in 2020 and the projected burden to 2040. Methods:The estimated number of BCa cases and deaths were extracted from the GLOBOCAN 2020 database. Age-standardised incidence rates (ASIRs) and age-standardised mortality rates (ASMRs) were calculated using the world standard. The predicted BCa incidence and mortality in 2040 was calculated based on demographic projections. Results:Globally, approximately 573 000 new BCa cases and 213 000 deaths occurred in 2020, corresponding to ASIRs and ASMRs of 5.6 and 1.9 per 100 000, respectively. The incidence and mortality rates were approximately 4-fold higher in men (9.5 and 3.3 per 100 000, respectively) than women (2.4 and 0.9, respectively). Across world regions, incidence rates varied at least 12-fold among men and 8-fold among women, with the highest ASIRs for both men and women detected in Southern Europe (26.5 and 5.8 per 100 000, respectively) and Western Europe (21.5 and 5.8, respectively) and the lowest in Middle Africa (2.2) in men and South-Central Asia (0.7) in women. The highest ASMRs for both men and women were found in Northern Africa (9.2 and 1.8 per 100 000, respectively). By 2040, the annual number of new BCa cases and deaths will increase to 991 000 (72.8% increase from 2020) and 397 000 (86.6% increase), respectively. Conclusions:Geographical distributions of BCa incidence and mortality uncovered higher risk of BCa incidence in Southern and Western European populations and higher risk of mortality in Northern African populations. Considering the predicted 73% and 87% increase in annual BCa cases and deaths by 2040 globally, respectively, there is an urgent need to develop and accelerate BCa control initiatives for high-risk populations to tackle global BCa burden and narrow its geographical disparities.

Objectives To compare the prediction effects of six models based on machine learning theories, which can provide a methodological reference for predicting the risk of type 2 diabetes mellitus (T2DM).Setting and participants This study was based on the monitoring data of chronic disease risk factors in Dongguan residents from 2016 to 2018. The multistage cluster random sampling method was adopted at each monitoring site, and 4157 people were finally selected. In the initial population, we excluded individuals with more than 20% missing data and eventually included 4106 subjects.Design K nearest neighbour algorithm and synthetic minority oversampling technique were used to process the data. Single factor analysis was used for preliminary selection of variables. The 10-fold cross-validation was used to optimise the parameters of some models. The accuracy, precision, recall and area under receiver operating characteristic curve (AUC) were used to evaluate the prediction effect of models, and Delong test was used to analyse the differences of AUC values of each model.Results After balancing data, the sample size increased to 8013, of which 4023 are patients with T2DM and 3990 in control group. The comparison results of the six models showed that back propagation neural network model has the best prediction effect with 93.7% accuracy, 94.6% accuracy, 92.8% recall and the AUC value of 0.977, followed by logistic model, support vector machine model, CART decision tree model and C4.5 decision tree model. Deep neural network has the worst prediction performance, with 84.5% accuracy, 86.1% precision, 82.9% recall and the AUC value of 0.845.Conclusions In this study, six types of risk prediction models for T2DM were constructed, and the predictive effects of these models were compared based on various indicators. The results showed that back propagation neural network based on the selected data set had the best prediction effect.

ObjectiveWe aimed to evaluate whether depression is associated with increased risk of dietary inflammatory index (DII) or energy-adjusted DII (E-DII) and whether the association is partly explained by insulin resistance (IR).MethodsBase on the National Health and Nutrition Examination Survey (NHANES) 2005–2018. Univariate analyses of continuous and categorical variables were performed using t-test, ANOVA, and χ2 test, respectively. Logistic regression was used to analyze the relationship between DII or E-DII and depression in three different models. Mediation analysis was used to assess the potential mediation effects of homeostatic model assessment-IR (HOMA-IR).ResultsA total of 70,190 participants were included, and the DII score was higher in the depressed group. DII score was related to all participant characteristics except age (p < 0.05). After being included in covariates (Model 3), participants in the highest quartile of DII score have increased odds of depression (OR: 1.82, 95% CI: 1.28–2.58) compared with those in the first quartile of DII score. And, a significant dose–response relationship was found (p-trend <0.05). No interaction between DII and HOMA-IR was observed in terms of the risk of depression, and HOMA-IR did not find to play a mediating role in the association between DII and depression. Similar results were obtained for the association between E-DII and depression.ConclusionOur results suggest that a higher pro-inflammatory diet increases the risk of depression in U.S. adults, while there was no evidence of a multiplicative effect of DII or E-DII and HOMA-IR on disease risk, nor of a mediating effect of HOMA-IR.

ObjectiveTo explore the protective effect and mechanism of salidroside on hypoxia-reoxygenationtreated human coronary endothelial cells（HCAEC）.MethodsThe HCAEC ischemia/reperfusion injury model was established by hypoxia-reoxygenation,and different doses of salidroside(10,20,40 μmol·L -1 )were given for intervention. Cell viability was detected by CCK-8,cell apoptosis and reactive oxygen species（ROS）levels were detected by flow cytometry. Superoxide dismutase（SOD）,glutathione（GSH）activity,malondialdehyde（MDA）content were detected by spectrophotometry. The acetylation levels of divalent metal ion transporter 1（DMT1）,ferroportin（FPN）,silent information regulator 1（SIRT1）,and forkhead box transcription factor 1（FoxO1）were detected by Western Blot. After siRNA was used to silence SIRT1, a high-dose of salidroside was given to intervene,and the contents of SOD,GSH and MDA were detected.ResultsCompared with the control group,cell viability, SOD and GSH activities and FoxO1 acetylation level decreased in the hypoxia-reoxygenation model group,and the apoptosis rate,MDA content,ROS level,SIRT1,DMT1,FPN expression increased（P＜0.05,P＜0.01）. Compared with the hypoxia-reoxygenation group,the above indicators in each group were reversed after the intervention of low-,medium-and high-doses of salidroside,and the difference between the medium-and high-dose groups was statistically significant（P＜0.05）,and there was a certain dose dependence（P＜0.05）.SIRT1-specific siRNA significantly down-regulated the expression of SIRT1（P＜0.01）. After SIRT1 was silenced,the up-regulating effect of salidroside on SOD and GSH activities and the down-regulating effect of MDA content were all reversed.ConclusionSalidroside could improve hypoxia-reoxygenation-induced damage in HCAEC by reducing apoptosis, improving iron metabolism, and inhibiting oxidative stress through SIRT1/FoxO1 signaling pathway.

目的:探索DIP支付方式改革对公立医院医生医疗行为的影响,为医院更好地适应DIP支付方式改革提供建议.方法:采用问卷调查法和专家访谈法了解临床医生对DIP支付方式改革的看法.通过单因素方差分析对心血管内科患者的人均检查费用、人均药品费用以及医院各病区平均住院时长等指标进行分析.结果:临床医生对DIP支付方式改革认同度较高,诊疗行为更加规范,临床科室控费能力进一步提高,患者平均住院时长显著降低.建议:公立医院应增强按病种分值付费理念,加强宣传培训;医保部门应进一步完善DIP支付方式改革,保证分值单价稳定,确保医保基金平稳运行.