| 1. |
- Eberhardt, Mirjam, et al.
(författare)
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H2S and NO cooperatively regulate vascular tone by activating a neuroendocrine HNO-TRPA1-CGRP signalling pathway.
- 2014
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 5:Jul 15
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Tidskriftsartikel (refereegranskat)abstract
- Nitroxyl (HNO) is a redox sibling of nitric oxide (NO) that targets distinct signalling pathways with pharmacological endpoints of high significance in the treatment of heart failure. Beneficial HNO effects depend, in part, on its ability to release calcitonin gene-related peptide (CGRP) through an unidentified mechanism. Here we propose that HNO is generated as a result of the reaction of the two gasotransmitters NO and H2S. We show that H2S and NO production colocalizes with transient receptor potential channel A1 (TRPA1), and that HNO activates the sensory chemoreceptor channel TRPA1 via formation of amino-terminal disulphide bonds, which results in sustained calcium influx. As a consequence, CGRP is released, which induces local and systemic vasodilation. H2S-evoked vasodilatatory effects largely depend on NO production and activation of HNO-TRPA1-CGRP pathway. We propose that this neuroendocrine HNO-TRPA1-CGRP signalling pathway constitutes an essential element for the control of vascular tone throughout the cardiovascular system.
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| 2. |
- Elfer, Katherine, et al.
(författare)
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Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models
- 2024
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Ingår i: Modern Pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. - 1530-0285. ; 37:4
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Tidskriftsartikel (refereegranskat)abstract
- This work advances and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklists and the proposed AI extensions to the STARD (Standards for Reporting Diagnostic Accuracy) and TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing datasets. Prior work by Wahab et al. proposed an annotation workflow and quality checklist for computational pathology annotations. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as CLEARR-AI: The Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence.
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