2022
DOI: 10.1186/s12876-022-02103-5
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Near-infrared fluorescence imaging with indocyanine green for assessment of donor livers in a rat model of ischemia–reperfusion

Abstract: Background Although marginal donor livers expand the donor pool, an ideal method for quantitatively evaluating the quality of donor livers has not been developed. This study aimed to explore the feasibility of indocyanine green (ICG) fluorescence imaging for estimating liver function in an ischemia–reperfusion model. Methods Forty-eight rats were randomly and evenly divided into 8 groups: the control group and the experimental groups (I-VII). The p… Show more

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Cited by 3 publications
(2 citation statements)
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“…The application of ICG in LT and cancer-targeted fluorescence molecular imaging still requires a pilot study. Timeline visualization maps showing that liver function is related to recovery and protection [ 43 , 44 ] may be a trend of future research. Our research shows that the application of the ICG in determining LT survival and prognosis is currently the main research focus, and we anticipate breakthroughs in this field soon.…”
Section: Discussionmentioning
confidence: 99%
“…The application of ICG in LT and cancer-targeted fluorescence molecular imaging still requires a pilot study. Timeline visualization maps showing that liver function is related to recovery and protection [ 43 , 44 ] may be a trend of future research. Our research shows that the application of the ICG in determining LT survival and prognosis is currently the main research focus, and we anticipate breakthroughs in this field soon.…”
Section: Discussionmentioning
confidence: 99%
“…Histopathological changes of each liver section (5 sections per each animal ( n = 5–6)) were blindly evaluated in ≥ 10 randomly selected non-overlapping fields using an Olympus BX43 (Olympus 114 Corp., Tokyo, Japan) microscope equipped with an Olympus DP12 camera at 200× magnifications. In each liver sample, the percentage area of sinusoidal congestion, necrosis, and vacuolization was assessed with the ImageJ software version 1.53c (NIH, Bethesda, MD, USA) using Suzuki’s classification according to the following criteria [ 22 ]:…”
Section: Methodsmentioning
confidence: 99%