Dataset Information

A Consensus Definitive Classification of Scavenger Receptors and Their Roles in Health and Disease.

ABSTRACT: Scavenger receptors constitute a large family of proteins that are structurally diverse and participate in a wide range of biological functions. These receptors are expressed predominantly by myeloid cells and recognize a diverse variety of ligands including endogenous and modified host-derived molecules and microbial pathogens. There are currently eight classes of scavenger receptors, many of which have multiple names, leading to inconsistencies and confusion in the literature. To address this problem, a workshop was organized by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, to help develop a clear definition of scavenger receptors and a standardized nomenclature based on that definition. Fifteen experts in the scavenger receptor field attended the workshop and, after extensive discussion, reached a consensus regarding the definition of scavenger receptors and a proposed scavenger receptor nomenclature. Scavenger receptors were defined as cell surface receptors that typically bind multiple ligands and promote the removal of nonself or altered-self targets. They often function by mechanisms that include endocytosis, phagocytosis, adhesion, and signaling that ultimately lead to the elimination of degraded or harmful substances. Based on this definition, nomenclature and classification of these receptors into 10 classes were proposed. This classification was discussed at three national meetings and input from participants at these meetings was requested. The following manuscript is a consensus statement that combines the recommendations of the initial workshop and incorporates the input received from the participants at the three national meetings.

SUBMITTER: PrabhuDas MR

PROVIDER: S-EPMC5671342 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A Consensus Definitive Classification of Scavenger Receptors and Their Roles in Health and Disease.

PrabhuDas Mercy R MR Baldwin Cynthia L CL Bollyky Paul L PL Bowdish Dawn M E DME Drickamer Kurt K Febbraio Maria M Herz Joachim J Kobzik Lester L Krieger Monty M Loike John J McVicker Benita B Means Terry K TK Moestrup Soren K SK Post Steven R SR Sawamura Tatsuya T Silverstein Samuel S Speth Robert C RC Telfer Janice C JC Thiele Geoffrey M GM Wang Xiang-Yang XY Wright Samuel D SD El Khoury Joseph J

Journal of immunology (Baltimore, Md. : 1950) 20170501 10

Scavenger receptors constitute a large family of proteins that are structurally diverse and participate in a wide range of biological functions. These receptors are expressed predominantly by myeloid cells and recognize a diverse variety of ligands including endogenous and modified host-derived molecules and microbial pathogens. There are currently eight classes of scavenger receptors, many of which have multiple names, leading to inconsistencies and confusion in the literature. To address this ...[more]

PMID: 28483986

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Project description:Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Dataset Information

A Consensus Definitive Classification of Scavenger Receptors and Their Roles in Health and Disease.

Publications

A Consensus Definitive Classification of Scavenger Receptors and Their Roles in Health and Disease.

Similar Datasets

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