Step-edge-induced resistance anisotropy in quasi-free-standing bilayer chemical vapor deposition graphene on SiC

Cataloged from PDF version of article.The transport properties of quasi-free-standing (QFS) bilayer graphene on SiC depend on a range of scattering mechanisms. Most of them are isotropic in nature. However, the SiC substrate morphology marked by a distinctive pattern of the terraces gives rise to an anisotropy in graphene's sheet resistance, which may be considered an additional scattering mechanism. At a technological level, the growth-preceding in situ etching of the SiC surface promotes step bunching which results in macro steps similar to 10 nm in height. In this report, we study the qualitative and quantitative effects of SiC steps edges on the resistance of epitaxial graphene grown by chemical vapor deposition. We experimentally determine the value of step edge resistivity in hydrogen-intercalated QFS-bilayer graphene to be similar to 190 Omega mu m for step height h(S) = 10 nm and provide proof that it cannot originate from mechanical deformation of graphene but is likely to arise from lowered carrier concentration in the step area. Our results are confronted with the previously reported values of the step edge resistivity in monolayer graphene over SiC atomic steps. In our analysis, we focus on large-scale, statistical properties to foster the scalable technology of industrial graphene for electronics and sensor applications. (C) 2014 AIP Publishing LLC

Machine-learning with 18F-sodium fluoride PET and quantitative plaque analysis on CT angiography for the future risk of myocardial infarction

Coronary (18)F-sodium fluoride ((18)F-NaF) PET and CT angiography–based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary (18)F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and (18)F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40–59) months of follow-up. On univariable receiver-operator-curve analysis, only (18)F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68–0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53–0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60–0.84). After inclusion of all available data (clinical, quantitative plaque and (18)F-NaF PET), we achieved a substantial improvement (P = 0.008 versus (18)F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79–0.91). Conclusion: Both (18)F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model

Temporal trends of transcatheter aortic valve implantation in a high-volume academic center over 10 years

Background: Indications for transcatheter aortic valve implantation (TAVI) have gradually expanded since its introduction.Aims: The aim was to analyze temporal trends in TAVI characteristics based on the experience of a high-volume academic center over the period of 10 years.Methods: Five hundred and six consecutive (n = 506) patients with 1-year follow-up were divided into early (G1, years 2010–2013, n = 130), intermediate (G2, 2014–2016, n = 164) and recent (G3, 2017–2019, n = 212) experience groups.Results: Patient’s age remained constant over time (mean [SD]; G1 = 79.1 [7.1] years vs G2 = 79.1 [7.1] years vs G3 = 79.7 [6.6] years, P = 0.73) but surgical risk in G3 was lower (log Euroscore, median [IQR]: G1 = 14.0 [8.4–20.2] vs G2 = 12.0 [7.0–22.2] vs G3 = 5.1 [3.5–8.5]; P <0.001). Major/life-threatening bleeding (G1 = 26.9% vs G2 = 12.8% vs G3 = 9.4%; P <0.001), major vascular complications (G1 = 15.4% vs G2 = 8.5% vs G3 = 5.7%; P = 0.02) and moderate/severe paravalvular leak (G1 = 16.2% vs G2 = 11% vs G3 = 7.5%; P = 0.046) were decreasing with time. There was a significant drop in all-cause 1-year mortality in G3 (G1 = 20% vs G2 = 17.7% vs G3 = 9.1%; log rank = 0.01).Conclusions: The age of TAVI recipients remained unchanged over the last decade. Decreasing surgical risk coupled with improvements in procedural technique and care resulted in fewer periprocedural complications and better 1-year survival

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

Familial hematuria

Hematuria is a common presenting complaint in pediatric nephrology clinics and often has a familial basis. This teaching article provides an overview of causes, diagnosis, and management of the major forms of familial hematuria, Alport syndrome, and thin basement membrane nephropathy