Novel rat tail discitis model using bioluminescent Staphylococcus aureus

Management of spondylodiscitis is a challenging clinical problem requiring medical and surgical treatment strategies. The purpose of this study was to establish a rat model of spondylodiscitis that utilizes bioluminescent Staphylococcus aureus, thus permitting in-vivo surveillance of infection intensity. Inocula of the bioluminescent S. aureus strain XEN36 were created in concentrations of 102 CFU/0.1 mL, 104 CFU/0.1 mL, and 106 CFU/0.1 mL. Three groups of rats were injected with the bacteria in the most proximal intervertebral tail segment. The third most proximal tail segment was injected with saline as a control. Bioluminescence was measured at baseline, 3 days, and weekly for a total of 6 weeks. Detected bioluminescence for each group peaked at day three and returned to baseline at 21 days. The average intensity was highest for the experimental group injected with the most concentrated bacterial solution (106 CFU/0.1 mL). Radiographic analysis revealed loss of intervertebral disc space and evidence of osseous bridging. Saline injected spaces exhibited no decrease in intervertebral spacing as compared to distal sites. Histologic analysis revealed neutrophilic infiltrates, destruction of the annulus fibrosus and nucleus pulposus, destruction of vertebral endplates, and osseous bridging. Saline injected discs exhibited preserved annulus fibrosus and nucleus pulposus on histology. This study demonstrates that injection of bioluminescent S. aureus into the intervertebral disc of a rat tail is a viable animal model for spondylodiscitis research. This model allows for real-time, in-vivo quantification of infection intensity, which may decrease the number of animals required for infection studies of the intervertebral disc

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials