Angiotensin-(1-5) is a potent endogenous angiotensin AT(2)-receptor agonist

BACKGROUND: The renin-angiotensin system involves many more enzymes, receptors and biologically active peptides than originally thought. With this study, we investigated whether angiotensin-(1-5) [Ang-(1-5)], a 5-amino acid fragment of angiotensin II, has biological activity, and through which receptor it elicits effects. METHODS: The effect of Ang-(1-5) (1µM) on nitric oxide release was measured by DAF-FM staining in human aortic endothelial cells (HAEC), or Chinese Hamster Ovary (CHO) cells stably transfected with the angiotensin AT(2)-receptor (AT(2)R) or the receptor Mas. A potential vasodilatory effect of Ang-(1-5) was tested in mouse mesenteric and human renal arteries by wire myography; the effect on blood pressure was evaluated in normotensive C57BL/6 mice by Millar catheter. These experiments were performed in the presence or absence of a range of antagonists or inhibitors or in AT(2)R-knockout mice. Binding of Ang-(1-5) to the AT(2)R was confirmed and the preferred conformations determined by in silico docking simulations. The signaling network of Ang-(1-5) was mapped by quantitative phosphoproteomics. RESULTS: Key findings included: (1) Ang-(1-5) induced activation of eNOS by changes in phosphorylation at (Ser1177)eNOS and (Tyr657)eNOS and thereby (2) increased NO release from HAEC and AT(2)R-transfected CHO cells, but not from Mas-transfected or non-transfected CHO cells. (3) Ang-(1-5) induced relaxation of preconstricted mouse mesenteric and human renal arteries and (4) lowered blood pressure in normotensive mice – effects which were respectively absent in arteries from AT(2)R-KO or in PD123319-treated mice and which were more potent than effects of the established AT(2)R-agonist C21. (5) According to in silico modelling, Ang-(1-5) binds to the AT(2)R in two preferred conformations, one differing substantially from where the first five amino acids within angiotensin II bind to the AT(2)R. (6) Ang-(1-5) modifies signaling pathways in a protective RAS-typical way and with relevance for endothelial cell physiology and disease. CONCLUSIONS: Ang-(1-5) is a potent, endogenous AT(2)R-agonist

Assessment of image quality on the diagnostic performance of clinicians and deep learning models : Cross-sectional comparative reader study

BACKGROUND: Skin cancer is a prevalent and clinically significant condition, with early and accurate diagnosis being crucial for improved patient outcomes. Dermoscopy and artificial intelligence (AI) hold promise in enhancing diagnostic accuracy. However, the impact of image quality, particularly high dynamic range (HDR) conversion in smartphone images, on diagnostic performance remains poorly understood.OBJECTIVE: This study aimed to investigate the effect of varying image qualities, including HDR-enhanced dermoscopic images, on the diagnostic capabilities of clinicians and a convolutional neural network (CNN) model.METHODS: Eighteen dermatology clinicians assessed 303 images of 101 skin lesions that were categorized into three image quality groups: low quality (LQ), high quality (HQ) and enhanced quality (EQ) produced using HDR-style conversion. Clinicians participated in a two part reader study that required their diagnosis, management and confidence level for each image assessed.RESULTS: In the binary classification of lesions, clinicians had the greatest diagnostic performance with HQ images, with sensitivity (77.3%; CI 69.1-85.5), specificity (63.1%; CI 53.7-72.5) and accuracy (70.2%; CI 61.3-79.1). For the multiclass classification, the overall performance was also best with HQ images, attaining the greatest specificity (91.9%; CI 83.2-95.0) and accuracy (51.5%; CI 48.4-54.7). Clinicians had a superior performance (median correct diagnoses) to the CNN model for the binary classification of LQ and EQ images, but their performance was comparable on the HQ images. However, in the multiclass classification, the CNN model significantly outperformed the clinicians on HQ images (p < 0.01).CONCLUSION: This study highlights the importance of image quality on the diagnostic performance of clinicians and deep learning models. This has significant implications for telehealth reporting and triage