ABSTRACT
Objectives: CT-derived fractional flow reserve (CT-FFR) can improve the specificity of coronary CT-angiography (cCTA) for ruling out relevant coronary artery disease (CAD) prior to transcatheter aortic valve replacement (TAVR). However, little is known about the reproducibility of CT-FFR and the influence of diffuse coronary artery calcifications or segment location. The objective was to assess the reliability of machine-learning (ML)-based CT-FFR prior to TAVR in patients without obstructive CAD and to assess the influence of image quality, coronary artery calcium score (CAC), and the location of measurement within the coronary tree. Methods: Patients assessed for TAVR, without obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers with differing experience. Differences in absolute values and categorization into hemodynamically relevant CAD (CT-FFR ≤ 0.80) were compared. Results in regard to CAD were also compared against invasive coronary angiography. The influence of segment location, image quality, and CAC was evaluated. Results: Of the screened patients, 109/388 patients did not have obstructive CAD on cCTA and were included. The median (interquartile range) difference of CT-FFR values was -0.005 (-0.09 to 0.04) (p = 0.47). Differences were smaller with high values. Recategorizations were more frequent in distal segments. Diagnostic accuracy of CT-FFR between both observers was comparable (proximal: Δ0.2%; distal: Δ0.5%) but was lower in distal segments (proximal: 98.9%/99.1%; distal: 81.1%/81.6%). Image quality and CAC had no clinically relevant influence on CT-FFR. Conclusions: ML-based CT-FFR evaluation of proximal segments was more reliable. Distal segments with CT-FFR values close to the given threshold were prone to recategorization, even if absolute differences between observers were minimal and independent of image quality or CAC.
ABSTRACT
Despite the immense functional relevance of GPR56 (gene ADGRG1) in highly diverse (patho)physiological processes such as tumorigenesis, immune regulation, and brain development, little is known about its exact tissue localization. Here, we validated antibodies for GPR56-specific binding using cells with tagged GPR56 or eliminated ADGRG1 in immunotechniques. Using the most suitable antibody, we then established the human GPR56 tissue expression profile. Overall, ADGRG1 RNA-sequencing data of human tissues and GPR56 protein expression correlate very well. In the adult brain especially, microglia are GPR56-positive. Outside the central nervous system, GPR56 is frequently expressed in cuboidal or highly prismatic secreting epithelia. High ADGRG1 mRNA, present in the thyroid, kidney, and placenta is related to elevated GPR56 in thyrocytes, kidney tubules, and the syncytiotrophoblast, respectively. GPR56 often appears in association with secreted proteins such as pepsinogen A in gastric chief cells and insulin in islet ß-cells. In summary, GPR56 shows a broad, not cell-type restricted expression in humans.