Norovirus is the leading cause of nonbacterial foodborne disease outbreaks. Human noroviruses (HuNoVs) bind to histo-blood group antigens as the host receptor for infection. In this study, the inhibitory effects of fucoidans from brown algae, Laminaria japonica (LJ), Undaria pinnatifida and Undaria pinnatifida sporophyll, were evaluated against murine norovirus (MNoV), feline calicivirus (FCV) and HuNoV. Pretreatment of MNoV or FCV with the fucoidans at 1 mg/mL showed high antiviral activities, with 1.1 average log reductions of viral titers in plaque assays. They also showed significant inhibition on the binding of the P domains of HuNoV GII.4 and GII.17 to A- or O-type saliva and the LJ fucoidan was the most effective, reaching 54–72% inhibition at 1 mg/mL. In STAT1−/− mice infected with MNoV, oral administration of the LJ fucoidan, composed of mainly sulfated fucose and minor amounts of glucose and galactose, improved the survival rates of mice and significantly reduced the viral titers in their feces. Overall, these results provide the LJ fucoidan can be used to reduce NoV outbreaks.

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Norovirus is a major cause of acute gastroenteritis globally, resulting in enormous health and societal costs. In this study, the antiviral activities of Mori Cortex Radicis (MCR) extract and its bioactive flavonoids, morusin and kuwanon G, were tested against murine norovirus (MNV), a human norovirus surrogate, using plaque assay. The antiviral activity was confirmed in simulated digestive conditions, including simulated saliva fluid (SSF), simulated gastric fluid (SGF), and simulated intestinal fluid (SIF). Pre-treatment of MNV with MCR extract at 1000 µg/mL showed antiviral activity with a 1.1-log reduction. Morusin and kuwanon G also demonstrated a 1.0-log and 0.6-log reductions of MNV titers, respectively, at 100 µM. MCR extract at a concentration of 2 mg/mL in SSF, SGF, and SIF markedly reduced MNV titers by 1.8, 1.9, and 1.5 logs, respectively. Therefore, these data suggest that MCR extract can be used to control norovirus infectivity.

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson’s disease (PD) and Parkinson’s plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ($$n=105$$ n = 105 ) and patients with PD ($$n=105$$ n = 105 ), multiple systemic atrophy ($$n=132$$ n = 132 ), and progressive supranuclear palsy ($$n=69$$ n = 69 ) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.