One free-ranging Gray fox (Urocyon cinereoargenteus) underwent autopsy following neurologic disease, with findings including morbilliviral inclusions and associated lesions in numerous tissues, adenoviral intranuclear inclusions in bronchial epithelial cells, and septic pleuropneumonia, hepatitis, splenitis, and meningoencephalitis. Molecular diagnostics on fresh lung identified a strain within a distinct clade of canine distemper that is currently unique to wildlife in New England, as well as the emerging multi-host viral pathogen skunk adenovirus-1. Bacterial culture of fresh liver resulted in a pure growth of Listeria monocytogenes, with whole genome sequencing indicating that the isolate had a vast array of antimicrobial resistance and virulence-associated genes. One year later, a second fox was euthanized for inappropriate behavior in a residential area, and diagnostic workup revealed canine distemper and septic L. monocytogenes, with the former closely related to the distemper virus found in the previous fox and the latter divergent from the L. monocytogenes from the previous fox.

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

Atypical Brucella inopinata–Like Species in 2 Marine Toads

Chuviruses (family Chuviridae ), which are in the recently discovered order Jingchuvirales , were first identified in arthropods in 2015 and have been detected through metagenomics in numerous invertebrates, but only a few vertebrates. With only few metagenomically based detections in vertebrates, their replication competency in vertebrates remained questioned, let alone their pathological significance. This study identified three novel chuviruses as the etiology of lymphocytic meningoencephalomyelitis in three wild aquatic turtles: an alligator snapping turtle ( Macrochelys sp.), a Kemp’s ridley turtle ( Lepidochelys kempii ), and a loggerhead turtle ( Caretta caretta ). The application of random, deep sequencing successfully assembled the complete snapping turtle chuvirus-1 [STCV-1], Kemp’s ridley turtle chuvirus-1 [KTCV-1] genome, and loggerhead turtle chuvirus-1 [LTCV-1]) genome. The STCV-1 and KTCV-1 sequences were used to create custom RNAscope ^™ probes for in situ hybridization, which confirmed STCV-1, KTCV-1, and LTCV-1 (cross reactivity to the KTCV-1 probe) RNA within the inflamed region of the brain and spinal cord. STCV-1 and KTCV-1 were isolated on several turtle-origin cell lines. Phylogenetic analysis illustrated that all three viruses clustered with other vertebrate chuviruses, all within the genus Piscichuvirus . With more than 91% pairwise amino acid identity of the polymerase proteins, STCV-1, KTCV-1, and LTCV-1 belong to the same novel species, putatively named Piscichuvirus testudinae. This study demonstrates the first in situ evidence of chuviral pathogenicity in animals and only the second instance of jingchuviral isolation. The association of these chuviruses in three different chelonians with neurologic disease suggests the possibility that chuviruses are a significant, previously unrecognized cause of lymphocytic meningoencephalomyelitis in freshwater and marine turtles. Additional studies of these viruses are needed to elucidate their role in chelonians and the possibility of related viruses in other related hosts. Importance: Chuviruses have been identified in multiple animal species, including humans. However, most were identified metagenomically, and detection was not strongly associated with disease. This study provides the first evidence of chuviral disease in animals in diseased tissue: mononuclear meningoencephalomyelitis in three chelonians from three different genera, two distinct families. These pathogenic turtle chuviruses belong to the genus Piscichuvirus containing other non-mammalian vertebrate chuviruses and were classified together within a novel chuviral species. This study supports the need for further investigations into chuviruses to understand their biology, pathogenic potential, and their association with central nervous system inflammation in chelonians, other reptiles, and other vertebrates.

Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.