Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models

Non-invasive techniques to assess the progression of type 1 diabetes prior to clinical onset are needed. Here the authors apply a contrast-enhanced ultrasound measurement of mouse pancreatic blood flow to detect changes in the islet microvasculature that undergoes rearrangements during diabetes and...

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Autores principales:	Joshua R. St Clair, David Ramirez, Samantha Passman, Richard K. P. Benninger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e5ffc27a8776409ea3d318e4d002166a
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spelling	oai:doaj.org-article:e5ffc27a8776409ea3d318e4d002166a2021-12-02T17:33:03ZContrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models10.1038/s41467-018-03953-y2041-1723https://doaj.org/article/e5ffc27a8776409ea3d318e4d002166a2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-03953-yhttps://doaj.org/toc/2041-1723Non-invasive techniques to assess the progression of type 1 diabetes prior to clinical onset are needed. Here the authors apply a contrast-enhanced ultrasound measurement of mouse pancreatic blood flow to detect changes in the islet microvasculature that undergoes rearrangements during diabetes and predict disease progression.Joshua R. St ClairDavid RamirezSamantha PassmanRichard K. P. BenningerNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Joshua R. St Clair David Ramirez Samantha Passman Richard K. P. Benninger Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
description	Non-invasive techniques to assess the progression of type 1 diabetes prior to clinical onset are needed. Here the authors apply a contrast-enhanced ultrasound measurement of mouse pancreatic blood flow to detect changes in the islet microvasculature that undergoes rearrangements during diabetes and predict disease progression.
format	article
author	Joshua R. St Clair David Ramirez Samantha Passman Richard K. P. Benninger
author_facet	Joshua R. St Clair David Ramirez Samantha Passman Richard K. P. Benninger
author_sort	Joshua R. St Clair
title	Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
title_short	Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
title_full	Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
title_fullStr	Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
title_full_unstemmed	Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
title_sort	contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/e5ffc27a8776409ea3d318e4d002166a
work_keys_str_mv	AT joshuarstclair contrastenhancedultrasoundmeasurementofpancreaticbloodflowdynamicspredictstype1diabetesprogressioninpreclinicalmodels AT davidramirez contrastenhancedultrasoundmeasurementofpancreaticbloodflowdynamicspredictstype1diabetesprogressioninpreclinicalmodels AT samanthapassman contrastenhancedultrasoundmeasurementofpancreaticbloodflowdynamicspredictstype1diabetesprogressioninpreclinicalmodels AT richardkpbenninger contrastenhancedultrasoundmeasurementofpancreaticbloodflowdynamicspredictstype1diabetesprogressioninpreclinicalmodels
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Contrast-enhanced ultrasound measurement of pancreatic blood flow dynamics predicts type 1 diabetes progression in preclinical models

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