Long-term prognosis for individuals with hypertension undergoing coronary artery calcium scoring

To examine the performance of coronary artery calcification (CAC) for stratifying long-term risk of death in asymptomatic hypertensive patients

Boosted output performance of triboelectric nanogenerator via electric double layer effect

For existing triboelectric nanogenerators ( TENGs), it is important to explore unique methods to further enhance the output power under realistic environments to speed up their commercialization. We report here a practical TENG composed of three layers, in which the key layer, an electric double layer, is inserted between a top layer, made of Al/polydimethylsiloxane, and a bottom layer, made of Al. The efficient charge separation in the middle layer, based on Volta's electrophorus, results from sequential contact configuration of the TENG and direct electrical connection of the middle layer to the earth. A sustainable and enhanced output performance of 1.22mA and 46.8 mW cm(-2) under low frequency of 3 Hz is produced, giving over 16-fold enhancement in output power and corresponding to energy conversion efficiency of 22.4%. Finally, a portable power-supplying system, which provides enough d.c. power for charging a smart watch or phone battery, is also successfully developed.ope

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry.

AimsIn patients without obstructive coronary artery disease (CAD), we examined the prognostic value of risk factors and atherosclerotic extent.Methods and resultsPatients from the long-term CONFIRM registry without prior CAD and without obstructive (≥50%) stenosis were included. Within the groups of normal coronary computed tomography angiography (CCTA) (N = 1849) and non-obstructive CAD (N = 1698), the prognostic value of traditional clinical risk factors and atherosclerotic extent (segment involvement score, SIS) was assessed with Cox models. Major adverse cardiac events (MACE) were defined as all-cause mortality, non-fatal myocardial infarction, or late revascularization. In total, 3547 patients were included (age 57.9 ± 12.1 years, 57.8% male), experiencing 460 MACE during 5.4 years of follow-up. Age, body mass index, hypertension, and diabetes were the clinical variables associated with increased MACE risk, but the magnitude of risk was higher for CCTA defined atherosclerotic extent; adjusted hazard ratio (HR) for SIS >5 was 3.4 (95% confidence interval [CI] 2.3-4.9) while HR for diabetes and hypertension were 1.7 (95% CI 1.3-2.2) and 1.4 (95% CI 1.1-1.7), respectively. Exclusion of revascularization as endpoint did not modify the results. In normal CCTA, presence of ≥1 traditional risk factors did not worsen prognosis (log-rank P = 0.248), while it did in non-obstructive CAD (log-rank P = 0.025). Adjusted for SIS, hypertension and diabetes predicted MACE risk in non-obstructive CAD, while diabetes did not increase risk in absence of CAD (P-interaction = 0.004).ConclusionAmong patients without obstructive CAD, the extent of CAD provides more prognostic information for MACE than traditional cardiovascular risk factors. An interaction was observed between risk factors and CAD burden, suggesting synergistic effects of both

Coronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients.

AIMS:We aimed to compare semiquantitative coronary computed tomography angiography (CCTA) risk scores - which score presence, extent, composition, stenosis and/or location of coronary artery disease (CAD) - and their prognostic value between patients with and without diabetes mellitus (DM). Risk scores derived from general chest-pain populations are often challenging to apply in DM patients, because of numerous confounders. METHODS:Out of a combined cohort from the Leiden University Medical Center and the CONFIRM registry with 5-year follow-up data, we performed a secondary analysis in diabetic patients with suspected CAD who were clinically referred for CCTA. A total of 732 DM patients was 1:1 propensity-matched with 732 non-DM patients by age, sex and cardiovascular risk factors. A subset of 7 semiquantitative CCTA risk scores was compared between groups: 1) any stenosis ≥50%, 2) any stenosis ≥70%, 3) stenosis-severity component of the coronary artery disease-reporting and data system (CAD-RADS), 4) segment involvement score (SIS), 5) segment stenosis score (SSS), 6) CT-adapted Leaman score (CT-LeSc), and 7) Leiden CCTA risk score. Cox-regression analysis was performed to assess the association between the scores and the primary endpoint of all-cause death and non-fatal myocardial infarction. Also, area under the receiver-operating characteristics curves were compared to evaluate discriminatory ability. RESULTS:A total of 1,464 DM and non-DM patients (mean age 58 ± 12 years, 40% women) underwent CCTA and 155 (11%) events were documented after median follow-up of 5.1 years. In DM patients, the 7 semiquantitative CCTA risk scores were significantly more prevalent or higher as compared to non-DM patients (p ≤ 0.022). All scores were independently associated with the primary endpoint in both patients with and without DM (p ≤ 0.020), with non-significant interaction between the scores and diabetes (interaction p ≥ 0.109). Discriminatory ability of the Leiden CCTA risk score in DM patients was significantly better than any stenosis ≥50% and ≥70% (p = 0.003 and p = 0.007, respectively), but comparable to the CAD-RADS, SIS, SSS and CT-LeSc that also focus on the extent of CAD (p ≥ 0.265). CONCLUSION:Coronary atherosclerosis scoring with semiquantitative CCTA risk scores incorporating the total extent of CAD discriminate major adverse cardiac events well, and might be useful for risk stratification of patients with DM beyond the binary evaluation of obstructive stenosis alone

Novel Approaches for the Use of Cardiac/Coronary Computed Tomography Angiography

Recent developments in the novel imaging technology of cardiac computed tomography (CT) not only permit detailed assessment of cardiac anatomy but also provide insight into cardiovascular physiology. Foremost, coronary CT angiography (CCTA) enables direct noninvasive examination of both coronary artery stenoses and atherosclerotic plaque characteristics. Calculation of computational fluid dynamics by cardiac CT allows the noninvasive estimation of fractional flow reserve, which increases the diagnostic accuracy for detection of hemodynamically significant coronary artery disease. In addition, a combination of myocardial CT perfusion and CCTA can provide simultaneous anatomical and functional assessment of coronary artery disease. Finally, detailed anatomical evaluation of atrial, ventricular, and valvular anatomy provides diagnostic information and guidance for procedural planning, such as for transcatheter aortic valve replacement. The clinical applications of cardiac CT will be extended with the development of these novel modalities

Cyclin E and CDK2 Repress the Terminal Differentiation of Quiescent Cells after Asymmetric Division in C. elegans

Coordination between cell proliferation and differentiation is important in normal development and oncogenesis. These processes usually have an antagonistic relationship, in that differentiation is blocked in proliferative cells, and terminally differentiated cells do not divide. In some instances, cyclins, cyclin-dependent kinases (CDKs) and their inhibitors (CKIs) play important roles in this antagonistic regulation. However, it is unknown whether CKIs and cyclin/CDKs regulate the uncommitted state in quiescent cells where CDK activities are likely to be low. Here, we show in C. elegans that cye-1/cyclin E and cdk-2/CDK2 repress terminal differentiation in quiescent cells. In cye-1 mutants and cdk-2(RNAi) animals, after asymmetric division, certain quiescent cells adopted their sister cells' phenotype and differentiated at some frequency. In contrast, in cki-1(RNAi) animals, these cells underwent extra divisions, while, in cki-1(RNAi); cdk-2(RNAi) or cki-1(RNAi); cye-1 animals, they remained quiescent or differentiated. Therefore, in wild-type animals, CKI-1/CKI in these cells maintained quiescence by inhibiting CYE-1/CDK-2, while sufficient CYE-1/CDK-2 remained to repress the terminal differentiation. The difference between sister cells is regulated by the Wnt/MAP kinase pathway, which causes asymmetric expression of CYE-1 and CKI-1. Our results suggest that the balance between the levels of CKI and cyclin E determines three distinct cell states: terminally differentiated, quiescent and uncommitted, and proliferating