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Nagasato, D.

Publications and source records attributed to Nagasato, D..

2 recordsLinked to original sources

GPI-anchoring is required for the proper transport and glycosylation of arabinogalactan protein precursor

Arabinogalactan proteins (AGPs) are extracellular proteoglycans with many O-linked glycan chains. Precursors to many AGPs contain a C-terminal signal for the addition of a glycosylphosphatidylinositol (GPI)-anchor, but the role of this modification has not been elucidated. NtAGP1, a tobacco precursor to AGP, comprises a signal peptide, an AGP-coding region, and a GPI-anchoring signal, and it is classified as a member of the classical AGP family. Using green fluorescent protein (GFP) and sweet potato sporamin (SPO) as tags and tobacco BY-2 cells as the host, we analysed the transport and modification of NtAGP1. The fusion protein of GFP or SPO and NtAGP1 expressed in BY-2 cells migrated as a large smear on SDS-polyacrylamide gel. A confocal microscopic analysis indicated that the GFP and NtAGP1 fusion protein localized to the plasma membrane (PM) and intracellular structures. Fractionation studies of microsomes indicated that most of the fusion protein of SPO and NtAGP1 (SPO-AGP) localized to the PM. In contrast, the expression of mutants without a GPI-anchoring signal yielded several forms. The largest forms migrating as large smears on the gel were secreted into the culture medium, whereas other forms were recovered in the endomembrane organelles. A comparison of the glycan structures of the microsomal SPO-AGP and the secreted mutant SPO-AGP without a GPI-anchoring signal using antibodies against AGP glycan epitopes indicated that the glycan structures of these proteins differ. These observations indicate that a GPI-anchoring signal is required for both the proper transport and glycosylation of the AGP precursor.

plant biology↗

Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography

This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 796 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DLs renowned model, Visual Geometry Group-16: nHM, 107 images; HM, 456 images; mCNV, 122 images; and RS, 111 images (n = 796). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined. The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.983; sensitivity, 0.953; specificity, 0.940. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 0.976; sensitivity, 0.940; specificity, 0.941. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 93.7%; mCNV images, 82.4%; and RS, 92.3% with mean, 91.4%. Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.

bioengineering↗