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Background: Several magnetic resonance (MR) techniques have been suggested for radiation‐free imaging of osseous structures. Purpose: To compare the diagnostic value of ultra‐short echo time and gradient echo T1‐weighted MRI for the assessment of vertebral pathologies using histology and computed tomography (CT) as the reference standard. Study Type: Prospective. Subjects: Fifty‐nine lumbar vertebral bodies harvested from 20 human cadavers (donor age 73 ± 13 years; 9 male). Field Strength/Sequence: Ultra‐short echo time sequence optimized for both bone (UTEb) and cartilage (UTEc) imaging and 3D T1‐weighted gradient‐echo sequence (T1GRE) at 3 T; susceptibility‐weighted imaging (SWI) gradient echo sequence at 1.5 T. CT was performed on a dual‐layer dual‐energy CT scanner using a routine clinical protocol. Assessment: Histopathology and conventional CT were acquired as standard of reference. Semi‐quantitative and quantitative morphological features of degenerative changes of the spines were evaluated by four radiologists independently on CT and MR images independently and blinded to all other information. Features assessed were osteophytes, endplate sclerosis, visualization of cartilaginous endplate, facet joint degeneration, presence of Schmorl's nodes, and vertebral dimensions. Vertebral disorders were assessed by a pathologist on histology. Statistical Tests: Agreement between T1GRE, SWI, UTEc, and UTEb sequences and CT imaging and histology as standard of reference were assessed using Fleiss' κ and intra‐class correlation coefficients, respectively. Results: For the morphological assessment of osteophytes and endplate sclerosis, the overall agreement between SWI, T1GRE, UTEb, and UTEc with the reference standard (histology combined with CT) was moderate to almost perfect for all readers (osteophytes: SWI, κ range: 0.68–0.76; T1GRE: 0.92–1.00; UTEb: 0.92–1.00; UTEc: 0.77–0.85; sclerosis: SWI, κ range: 0.60–0.70; T1GRE: 0.77–0.82; UTEb: 0.81–0.92; UTEc: 0.61–0.71). For the visualization of the cartilaginous endplate, UTEc showed the overall best agreement with the reference standard (histology) for all readers (κ range: 0.85–0.93). Data Conclusions: Morphological assessment of vertebral pathologies was feasible and accurate using the MR‐based bone imaging sequences compared to CT and histopathology. T1GRE showed the overall best performance for osseous changes and UTEc for the visualization of the cartilaginous endplate. Level of Evidence: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Simple Summary: Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists. The complex interplay of a pathogen with its virulence and fitness factors, the host's immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001. [ABSTRACT FROM AUTHOR]