Inhibition of repulsive guidance molecule-a ameliorates compromised blood-spinal cord barrier integrity associated with neuromyelitis optica in rats.

The influx of pathogenic aquaporin-4 antibodies (AQP4-Abs) across the blood-spinal cord barrier (BSCB) is crucial for the development and exacerbation of neuromyelitis optica (NMO). We examined whether prophylactic intravenous administration of anti-repulsive guidance molecule-a antibodies (RGMa-Abs) has disease-modifying effects on BSCB dysfunction using an NMO model elicited by peripheral administration of AQP4-Abs to rats. RGMa-Ab treatment attenuated the acute exacerbation of perivascular astrocytopathy in the spinal cord and clinical symptoms, which were highly correlated with neurofilament light chain levels in both the cerebrospinal fluid (CSF) and serum. Additionally, RGMa-Ab treatment suppressed the expression of proinflammatory cytokines/chemokines and the infiltration of inflammatory cells into the spinal cord. CSF analysis of NMO rats revealed that RGMa-Ab treatment improved the CSF/serum albumin ratio and suppressed AQP4-Abs influx. RGMa inhibition using RGMa-Abs is suggested as a potential therapeutic option for BSCB dysfunction associated with NMO.

Automated recognition of glomerular lesions in the kidneys of mice by using deep learning.

Background: In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as much as in clinical settings. We have been performing automated recognition of various pathological animal tissues and quantitative analysis of pathological findings, including liver and lung. In this study, we attempted to construct an artificial intelligence (AI)-based trained model that can automatedly recognize glomerular lesions in mouse kidneys that are characterized by complex structures. Materials and methods: By using hematoxylin and eosin (HE)-stained whole slide images (WSI) from Col4a3 KO mice as variation data, normal glomeruli and glomerular lesions were annotated, and deep learning (DL) was performed with the use of the neural network classifier DenseNet system in HALO AI. The trained model was refined by correcting the annotation of misrecognized tissue area and reperforming DL. The accuracy of the trained model was confirmed by comparing the AI-obtained results with the pathological grades evaluated by pathologists. The generality of the trained model was also confirmed by analyzing the WSI of adriamycin (ADR)-induced nephropathy mice, which is a different disease model. Results: Glomerular lesions (including mesangial proliferation, crescent formation, and sclerosis) observed in Col4a3 KO mice and ADR mice were detected by our trained model. The number of glomerular lesions detected by our trained model were also highly correlated with that of counted by pathologists. Conclusion: In this study, we constructed a trained model allowing us to automatedly recognize glomerular lesions in the mouse kidney with the use of the HALO AI system. The findings and insights of this study will facilitate the development of digital pathology in non-clinical research and improve the probability of success in drug discovery research.