Exposure to tobacco smoke (TS) has been considered a risk factor for osteonecrosis of the femoral head (ONFH). Soluble epoxide hydrolase inhibitors (sEHIs) have been found to reduce inflammation and oxidative stress in a variety of pathologies. This study was designed to assess the effect of sEHI on the development of ONFH phenotypes induced by TS exposure in spontaneously hypertensive (SH) rats. SH and normotensive Wistar Kyoto (WKY) rats were exposed to filtered air (FA) or TS (80 mg/m3 particulate concentration) 6 h/day, 3 days/week for 8 weeks. During this period, sEHI was delivered through drinking water at a concentration of 6 mg/L. Histology, immunohistochemistry, and micro-CT morphometry were performed for phenotypic evaluation. As results, TS exposure induced significant increases in adipocyte area, bone specific surface (BS/BV), and trabecular separation (Tb.SP), as well as significant decreases in bone mineral density (BMD), percent trabecular area (Tb.Ar), HIF-1a expression, bone volume fraction (BV/TV), trabecular numbers (Tb.N), and trabecular thickness (Tb.Th) in both SH and WKY rats. However, the protective effects of sEHI were mainly observed in TS-exposed SH rats, specifically in the density of osteocytes, BMD, Tb.Ar, HIF-1a expression, BV/TV, BS/BV, Tb.N, and Tb.SP. Our study confirms that TS exposure can induce ONFH especially in SH rats, and suggests that sEHI therapy may protect against TS exposure-induced osteonecrotic changes in the femoral head.

Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of 'positive', i.e. fractured posterior-elements and 'negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.