Empagliflozin inhibits coronary microvascular dysfunction and reduces cardiac pericyte loss in db/db mice

BackgroundCoronary microvascular dysfunction (CMD) is a pathophysiological feature of diabetic heart disease. However, whether sodium-glucose cotransporter 2 (SGLT2) inhibitors protect the cardiovascular system by alleviating CMD is not known.ObjectiveWe observed the protective effects of empagliflozin (EMPA) on diabetic CMD.Materials and methodsThe mice were randomly divided into a db/db group and a db/db + EMPA group, and db/m mice served as controls. At 8 weeks of age, the db/db + EMPA group was given empagliflozin 10 mg/(kg⋅d) by gavage for 8 weeks. Body weight, fasting blood glucose and blood pressure were dynamically observed. Cardiac systolic and diastolic function and coronary flow reserve (CFR) were detected using echocardiography. The coronary microvascular structure and distribution of cardiac pericytes were observed using immunofluorescence staining. Picrosirius red staining was performed to evaluate cardiac fibrosis.ResultsEmpagliflozin lowered the increased fasting blood glucose levels of the db/db group. The left ventricular ejection fraction, left ventricular fractional shortening, E/A ratio and E/e′ ratio were not significantly different between the three groups. CFR was decreased in the db/db group, but EMPA significantly improved CFR. In contrast to the sparse and abnormal expansion of coronary microvessels observed in the db/db group, the number of coronary microvessels was increased, and the capillary diameter was decreased in the db/db + EMPA group. The number and microvascular coverage of cardiac pericytes were reduced in the db/db mice but were improved by EMPA. The cardiac fibrosis was increased in db/db group and may alleviate by EMPA.ConclusionEmpagliflozin inhibited CMD and reduced cardiac pericyte loss in diabetic mice

Optimization for Nonlinear Uncertain Switched Stochastic Systems with Initial State Difference in Batch Culture Process

Based on the deterministic description of batch culture expressed in form of switched ordinary differential equations, we introduce a switched stochastic counterpart system with initial state difference together with uncertain switching instants and system parameters to model the process of glycerol biodissimilation to 1,3-propanediol (1,3-PD) induced by Klebsiella pneumoniae (K. pneumoniae). Important properties of the stochastic system are discussed. Our aim is to obtain the unified switched instants and system parameters under the condition of different initial states. To do this, we will formulate a system identification problem in which these uncertain switched instants and system parameters are regarded as decision variables to be chosen such that the relative error between experimental data and computational results is minimized. Such problem governed by the stochastic system is subject to continuous state inequality constraints and box constraints. By performing a time-scaling transformation as well as introducing the constraint transcription and local smoothing approximation techniques, we convert such problem into a sequence of approximation subproblems. Considering both the difficulty of finding analytical solutions and the complex nature of these subproblems, we develop a parallelized differential evolution (DE) algorithm to solve these approximation subproblems. From an extensive simulation, we show that the obtained optimal switched instants and system parameters are satisfactory with initial state difference

PPI Network Analysis of mRNA Expression Profile of Ezrin Knockdown in Esophageal Squamous Cell Carcinoma

Ezrin, coding protein EZR which cross-links actin filaments, overexpresses and involves invasion, metastasis, and poor prognosis in various cancers including esophageal squamous cell carcinoma (ESCC). In our previous study, Ezrin was knock down and analyzed by mRNA expression profile which has not been fully mined. In this study, we applied protein-protein interactions (PPI) network knowledge and methods to explore our understanding of these differentially expressed genes (DEGs). PPI subnetworks showed that hundreds of DEGs interact with thousands of other proteins. Subcellular localization analyses found that the DEGs and their directly or indirectly interacting proteins distribute in multiple layers, which was applied to analyze the shortest paths between EZR and other DEGs. Gene ontology annotation generated a functional annotation map and found hundreds of significant terms, especially those associated with cytoskeleton organization of Ezrin protein, such as “cytoskeleton organization,” “regulation of actin filament-based process,” and “regulation of actin cytoskeleton organization.” The algorithm of Random Walk with Restart was applied to prioritize the DEGs and identified several cancer related DEGs ranked closest to EZR. These analyses based on PPI network have greatly expanded our comprehension of the mRNA expression profile of Ezrin knockdown for future examination of the roles and mechanisms of Ezrin

Predictive value of the stress hyperglycemia ratio in dialysis patients with acute coronary syndrome: insights from a multi-center observational study

Abstract Background Various studies have indicated that stress hyperglycemia ratio (SHR) can reflect true acute hyperglycemic status and is associated with poor outcomes in patients with acute coronary syndrome (ACS). However, data on dialysis patients with ACS are limited. The Global Registry of Acute Coronary Events (GRACE) risk score is a well-validated risk prediction tool for ACS patients, yet it underestimates the risk of major events in patients receiving dialysis. This study aimed to evaluate the association between SHR and adverse cardiovascular events in dialysis patients with ACS and explore the potential incremental prognostic value of incorporating SHR into the GRACE risk score. Methods This study enrolled 714 dialysis patients with ACS from January 2015 to June 2021 at 30 tertiary medical centers in China. Patients were stratified into three groups based on the tertiles of SHR. The primary outcome was major adverse cardiovascular events (MACE), and the secondary outcomes were all-cause mortality and cardiovascular mortality. Results After a median follow-up of 20.9 months, 345 (48.3%) MACE and 280 (39.2%) all-cause mortality occurred, comprising 205 cases of cardiovascular death. When the highest SHR tertile was compared to the second SHR tertile, a significantly increased risk of MACE (adjusted hazard ratio, 1.92; 95% CI, 1.48–2.49), all-cause mortality (adjusted hazard ratio, 2.19; 95% CI, 1.64–2.93), and cardiovascular mortality (adjusted hazard ratio, 2.70; 95% CI, 1.90–3.83) was identified in the multivariable Cox regression model. A similar association was observed in both diabetic and nondiabetic patients. Further restricted cubic spline analysis identified a J-shaped association between the SHR and primary and secondary outcomes, with hazard ratios for MACE and mortality significantly increasing when SHR was > 1.08. Furthermore, adding SHR to the GRACE score led to a significant improvement in its predictive accuracy for MACE and mortality, as measured by the C-statistic, net reclassification improvement, and integrated discrimination improvement, especially for those with diabetes. Conclusions In dialysis patients with ACS, SHR was independently associated with increased risks of MACE and mortality. Furthermore, SHR may aid in improving the predictive efficiency of the GRACE score, especially for those with diabetes. These results indicated that SHR might be a valuable tool for risk stratification and management of dialysis patients with ACS

The triglyceride-glucose index predicts 1-year major adverse cardiovascular events in end-stage renal disease patients with coronary artery disease

Abstract Background The triglyceride-glucose (TyG) index has been suggested as a dependable indicator for predicting major adverse cardiovascular events (MACE) in individuals with cardiovascular conditions. Nevertheless, there is insufficient data on the predictive significance of the TyG index in end-stage renal disease (ESRD) patients with coronary artery disease (CAD). Methods This study, conducted at multiple centers in China, included 959 patients diagnosed with dialysis and CAD from January 2015 to June 2021. Based on the TyG index, the participants were categorized into three distinct groups. The study’s primary endpoint was the combination of MACE occurring within one year of follow-up, including death from any cause, non-fatal myocardial infarction, and non-fatal stroke. We assessed the association between the TyG index and MACE using Cox proportional hazard models and restricted cubic spline analysis. The TyG index value was evaluated for prediction incrementally using C-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results The three groups showed notable variations in the risk of MACE (16.3% in tertile 1, 23.5% in tertile 2, and 27.2% in tertile 3; log-rank P = 0.003). Following complete adjustment, patients with the highest TyG index exhibited a notably elevated risk of MACE in comparison to those in the lowest tertile (hazard ratio [HR] 1.63, 95% confidence interval [CI] 1.14–2.35, P = 0.007). Likewise, each unit increase in the TyG index correlated with a 1.37-fold higher risk of MACE (HR 1.37, 95% CI 1.13–1.66, P = 0.001). Restricted cubic spline analysis revealed a connection between the TyG index and MACE (P for nonlinearity > 0.05). Furthermore, incorporating the TyG index to the Global Registry of Acute Coronary Events risk score or baseline risk model with fully adjusted factors considerably enhanced the forecast of MACE, as demonstrated by the C-statistic, continuous NRI, and IDI. Conclusions The TyG index might serve as a valuable and dependable indicator of MACE risk in individuals with dialysis and CAD, indicating its potential significance in enhancing risk categorization in clinical settings

Association between the triglyceride glucose index and in-hospital and 1-year mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit

Abstract Objective This study aimed to explore the association between the triglyceride glucose index (TyG) and the risk of in-hospital and one-year mortality in patients with chronic kidney disease (CKD) and cardiovascular disease (CAD) admitted to the intensive care unit (ICU). Methods The data for the study were taken from the Medical Information Mart for Intensive Care-IV database which contained over 50,000 ICU admissions from 2008 to 2019. The Boruta algorithm was used for feature selection. The study used univariable and multivariable logistic regression analysis, Cox regression analysis, and 3-knotted multivariate restricted cubic spline regression to evaluate the association between the TyG index and mortality risk. Results After applying inclusion and exclusion criteria, 639 CKD patients with CAD were included in the study with a median TyG index of 9.1 [8.6,9.5]. The TyG index was nonlinearly associated with in-hospital and one-year mortality risk in populations within the specified range. Conclusion This study shows that TyG is a predictor of one-year mortality and in-hospital mortality in ICU patients with CAD and CKD and inform the development of new interventions to improve outcomes. In the high-risk group, TyG might be a valuable tool for risk categorization and management. Further research is required to confirm these results and identify the mechanisms behind the link between TyG and mortality in CAD and CKD patients

Association between platelet counts and morbidity and mortality after endovascular repair for type B aortic dissection

This study aimed to assess the association of postoperative platelet counts with early and late outcomes after thoracic endovascular aortic repair (TEVAR) for type B aortic dissection (TBAD). We retrospectively evaluated 892 patients with TBAD who underwent TEVAR from a prospectively maintained database. Postoperative nadir platelet counts were evaluated as a continuous variable, and a categorical variable (thrombocytopenia), which was defined as platelet count≤ the lowest 10% percentile (108 × 109/l). Multivariable logistic regression analyses were conducted to assess the impact of postoperative thrombocytopenia on early outcomes, and multivariable cox regression analyses on long-term mortality. Patients with postoperative thrombocytopenia experienced significantly higher rates of postoperative mortality, prolonged intensive care unit stay, death, stroke, limb ischemia, mesenteric ischemia, acute kidney injury (AKI), and puncture-related hematoma (P< .05 for each), but similar rates of immediate type I endoleak and spinal cord ischemia. Multivariable logistic analyses showed that postoperative thrombocytopenia was independently associated with postoperative stroke, limb ischemia, and AKI. Similar results were observed when postoperative nadir platelet count was modeled as a continuous predictor (P< .05 for each). By multivariable Cox analyses, postoperative thrombocytopenia was an independent predictor for long-term all-cause mortality (hazard ratio 2.72, 95% CI, 1.72–4.29, P< .001). For every 30 × 109/L decrease in postoperative platelet count, the risk of long-term all-cause mortality increased by 15% (HR 1.15; 95% CI 1.07–1.25; P< .001). Therefore, postoperative thrombocytopenia might be a useful tool for risk stratification after TEVAR

The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models

Abstract Objective Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the in-hospital mortality of that kind of patient in the ICU using machine learning methods. Methods Data of CKD patients with CAD were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Boruta algorithm was conducted for the feature selection process. Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM), Neural Network (NN), and Extreme Gradient Boosting (XGBoost), were conducted to construct the predictive model for in-hospital mortality and performance was evaluated by average precision (AP) and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) algorithm was applied to explain the model visually. Moreover, data from the Telehealth Intensive Care Unit Collaborative Research Database (eICU-CRD) were acquired as an external validation set. Results 3590 and 1657 CKD patients with CAD were acquired from MIMIC-IV and eICU-CRD databases, respectively. A total of 78 variables were selected for the machine learning model development process. Comparatively, GBDT had the highest predictive performance according to the results of AUC (0.946) and AP (0.778). The SHAP method reveals the top 20 factors based on the importance ranking. In addition, GBDT had good predictive value and a certain degree of clinical value in the external validation according to the AUC (0.865), AP (0.672), decision curve analysis, and calibration curve. Conclusion Machine learning algorithms, especially GBDT, can be reliable tools for accurately predicting the in-hospital mortality risk for CKD patients with CAD in the ICU. This contributed to providing optimal resource allocation and reducing in-hospital mortality by tailoring precise management and implementation of early interventions

Ticagrelor vs. clopidogrel for coronary microvascular dysfunction in patients with STEMI: a meta-analysis of randomized controlled trials

PurposeApproximately half of ST-segment elevation myocardial infarction (STEMI) patients who undergo revascularization present with coronary microvascular dysfunction. Dual antiplatelet therapy, consisting of aspirin and a P2Y12 inhibitor (e.g., clopidogrel or ticagrelor), is recommended to reduce rates of cardiovascular events after STEMI. The present study performed a pooled analysis of randomized controlled trials (RCTs) to compare effects of ticagrelor and clopidogrel on coronary microcirculation dysfunction in STEMI patients who underwent the primary percutaneous coronary intervention.MethodsThe PubMed, Embase, Cochrane Library, and Web of Science databases were searched for eligible RCTs up to September 2022, with no language restriction. Coronary microcirculation indicators included the corrected thrombolysis in myocardial infarction (TIMI) frame count (cTFC), myocardial blush grade (MBG), TIMI myocardial perfusion grade (TMPG), coronary flow reserve (CFR), and index of microcirculatory resistance (IMR).ResultsSeven RCTs that included a total of 957 patients (476 who were treated with ticagrelor and 481 who were treated with clopidogrel) were included. Compared with clopidogrel, ticagrelor better accelerated microcirculation blood flow [cTFC = −2.40, 95% confidence interval (CI): −3.38 to −1.41, p &lt; 0.001] and improved myocardial perfusion [MBG = 3, odds ratio (OR) = 1.99, 95% CI: 1.35 to 2.93, p &lt; 0.001; MBG ≥ 2, OR = 2.57, 95% CI: 1.61 to 4.12, p &lt; 0.001].ConclusionsTicagrelor has more benefits for coronary microcirculation than clopidogrel in STEMI patients who undergo the primary percutaneous coronary intervention. However, recommendations for which P2Y12 receptor inhibitor should be used in STEMI patients should be provided according to results of studies that investigate clinical outcomes