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Borna disease virus (BoDV) is a neurotropic virus that causes several infections in humans and neurological diseases in a wide range of animals worldwide. BoDV-1 has been molecularly and serologically detected in many domestic and wild animals in Japan; however, the genetic diversity of this virus and the origin of its infection are not fully understood. In this study, we investigated BoDV-1 infection and genetic diversity in samples collected from animals in Hokkaido between 2006 and 2020. The analysis was performed by focusing on the P region of BoDV-1 for virus detection. The presence of BoDV-1 RNA was observed in samples of brain tissue and various organs derived from persistently infected cattle. Moreover, after inoculation, BoDV-positive brains were isolated from neonatal rats. The gene sequences of the P region of BoDV obtained from the rat brain were in the same cluster as the P region of the virus isolated from the original bovine. Thus, genetic variation in BoDV-1 was extremely low. The phylogenetic analysis revealed that BoDV-1 isolates obtained in this study were part of the same cluster, which suggested that BoDV-1 of the same cluster was widespread among animals in Hokkaido.
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http://dx.doi.org/10.1292/jvms.21-0155 | DOI Listing |
Aging Ment Health
September 2025
Department of Psychiatry, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Objectives: Early detection of neuropsychiatric symptoms is critical for timely intervention in cognitive decline. Mild Behavioral Impairment (MBI) represents late-life behavioral changes preceding or accompanying neurodegenerative processes. This study aimed to translate, culturally adapt, and psychometrically validate the Persian version of the Mild Behavioral Impairment Checklist (MBI-C) in older Iranian adults.
View Article and Find Full Text PDFNat Commun
August 2025
Division of Structural Biology, Centre for Human Genetics, University of Oxford, Oxford, UK.
Borna disease virus 1 (BoDV-1) is a non-segmented RNA virus with one of the smallest known RNA virus genomes. BoDV-1 replicates in the nucleus of infected cells using a virally encoded polymerase complex composed of the large protein and phosphoprotein. Here, we present the BoDV-1 polymerase complex at resolutions up to 2.
View Article and Find Full Text PDFPLoS Pathog
August 2025
Department of Neuropathology, Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany.
Borna disease virus 1 (BoDV-1) has long been recognized as a cause of fatal encephalitis in animals and was only recently identified as a zoonotic pathogen causing a similar disease in humans. This study provides the first comprehensive comparative analysis of BoDV-1-induced neuropathology in human and animal end hosts, including horses, sheep, and alpacas. Using immunohistochemical analyses, we investigated the topographical distribution of BoDV-1 and inflammatory responses in the central nervous system across 19 cases.
View Article and Find Full Text PDFInfect Genet Evol
July 2025
Center for Virology, Medical University Vienna, 1090 Vienna, Austria.
Background: Borna disease virus 1 (BoDV-1) is a zoonotic virus with a recently confirmed potential to cause rare but severe cases of encephalitis in humans. While the bicolored white-toothed shrew (Crocidura leucodon), which represents the reservoir, is widely distributed over eastern, central, and southern Europe as well as south-west Asia, human infections have so far only been reported from Germany. As infections in sentinels such as horses indicate the endemic circulation of the virus also in circumscribed regions of neighboring countries (Austria, Liechtenstein, Switzerland), we initiated a retrospective case-finding study to investigate whether there were so far undetected human infections in Austria.
View Article and Find Full Text PDFFront Physiol
July 2025
Fertility and Infertility Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Introduction: Ovarian diseases, including Polycystic Ovary Syndrome (PCO) and Dominant Follicle irregularities, present significant diagnostic challenges in clinical practice. Traditional diagnostic methods, reliant on subjective ultrasound interpretation, often lead to variability in accuracy. Recent advancements in artificial intelligence (AI) and transfer learning offer promising opportunities to improve diagnostic consistency and accuracy in ovarian disease detection.
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