Nonalcoholic fatty liver disease (NAFLD) is currently one of most common forms of chronic liver disease globally. NAFLD represents a wide spectrum of liver involvement from nonprogressive isolated steatosis to nonalcoholic steatohepatitis (NASH), characterized by liver necroinflammation and fibrosis and currently one of the top causes of end‐stage liver disease and hepatocellular carcinoma. At present, there is a lack of effective treatments, and a central barrier to the development of therapies is the requirement for an invasive liver biopsy for diagnosis of NASH. Discovery of reliable, noninvasive biomarkers are urgently needed. In this study, we tested whether circulating extracellular vesicles (EVs), cell‐derived small membrane‐surrounded structures with a rich cargo of bioactive molecules, may serve as reliable noninvasive “liquid biopsies” for NASH diagnosis and assessment of disease severity. Total circulating EVs and hepatocyte‐derived EVs were isolated by differential centrifugation and size‐exclusion chromatography from serum samples of healthy individuals, patients with precirrhotic NASH, and patients with cirrhotic NASH. EVs were further characterized by flow cytometry, electron microscopy, western blotting, and dynamic light scattering assays before performing a proteomics analysis. Our findings suggest that levels of total and hepatocyte‐derived EVs correlate with NASH clinical characteristics and disease severity. Additionally, using proteomics data, we developed understandable, powerful, and unique EV‐based proteomic signatures for potential diagnosis of advanced NASH. Conclusion: Our study shows that the quantity and protein constituents of circulating EVs provide strong evidence for EV protein–based liquid biopsies for NAFLD/NASH diagnosis.
The raphidophyte Chattonella spp. and diatom Skeletonema spp. are the dominant harmful algal species of summer blooms in Ariake Sea, Japan. A new bio-optical algorithm based on backscattering features has been developed to differentiate harmful raphidophyte blooms from diatom blooms using MODIS imagery. Bloom waters were first discriminated from other water types based on the distinct spectral shape of the remote sensing reflectance R r s (λ) data derived from MODIS. Specifically, bloom waters were discriminated by the positive value of Spectral Shape, SS (645), which arises from the R r s (λ) shoulder at 645 nm in bloom waters. Next, the higher cellular-specific backscattering coefficient, estimated from MODIS data and quasi-analytical algorithm (QAA) of raphidophyte, Chattonella spp., was utilized to discriminate it from blooms of the diatom, Skeletonema spp. A new index b b p − i n d e x ( 555 ) was calculated based on a semi-analytical bio-optical model to discriminate the two algal groups. This index, combined with a supplemental Red Band Ratio (RBR) index, effectively differentiated the two bloom types. Validation of the method was undertaken using MODIS satellite data coincident with confirmed bloom observations from local field surveys, which showed that the newly developed method, based on backscattering features, could successfully discriminate the raphidophyte Chattonella spp. from the diatom Skeletonema spp. and thus provide reliable information on the spatial distribution of harmful blooms in Ariake Sea.
The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.
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