BackgroundIntracranial aneurysms (IA) are increasingly recognized as a disease driven by chronic inflammation. Recent research has identified key mediators and processes underlying IA pathogenesis, but mechanistic understanding remains incomplete. Lymphocytic infiltrates have been demonstrated in patient IA tissue specimens and have also been shown to play an important role in abdominal aortic aneurysms (AAA) and related diseases such as atherosclerosis. However, no study has systematically examined the contribution of lymphocytes in a model of IA.MethodsLymphocyte-deficient (Rag1) and wild-type (WT; C57BL/6 strain) mice were subjected to a robust IA induction protocol. Rates of IA formation and rupture were measured, and cerebral artery tissue was collected and utilized for histology and gene expression analysis.ResultsAt 2 weeks, the Rag1 group had significantly fewer IA formations and ruptures than the WT group. Histological analysis of unruptured IA tissue showed robust B and T lymphocyte infiltration in the WT group, while there were no differences in macrophage infiltration, IA diameter, and wall thickness. Significant differences in interleukin-6 (IL-6), matrix metalloproteinases 2 (MMP2) and 9 (MMP9), and smooth muscle myosin heavy chain (MHC) were observed between the groups.ConclusionsLymphocytes are key contributors to IA pathogenesis and provide a novel target for the prevention of IA progression and rupture in patients.

Inflammatory processes are implicated in many diseases of the vasculature and have been shown to play a key role in the formation of intracranial aneurysms (IAs). Although the specific mechanisms underlying these processes have been thoroughly investigated in related pathologies, such as atherosclerosis, there remains a paucity of information regarding the immunopathology of IA. Cells such as macrophages and lymphocytes and their effector molecules have been suggested to be players in IA, but their specific interactions and the role of other components of the inflammatory response have yet to be determined. Drawing parallels between the pathogenesis of IA and other vascular disorders could provide a roadmap for developing a mechanistic understanding of the immunopathology of IA and uncovering useful targets for therapeutic intervention. Future research should address the presence and function of leukocyte subsets, mechanisms of leukocyte recruitment and activation, and the role of damage-associated molecular patterns in IA.

Oxidative stress and chronic inflammation in arterial walls have been implicated in intracranial aneurysm (IA) formation and rupture. Dimethyl fumarate (DMF) exhibits immunomodulatory properties, partly via activation of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway which reduces oxidative stress by inducing the antioxidant response element (ARE). This study evaluated the effects of DMF both in vitro, using tumor necrosis factor (TNF)-α-treated vascular smooth muscle cells (VSMC), and in vivo, using a murine elastase model to induce aneurysm formation. The mice were treated with either DMF at 100 mg/kg/day P.O. or vehicle for two weeks. DMF treatment protected VSMCs from TNF-α-induced inflammation as demonstrated by its downregulation of cytokines and upregulation of Nrf2 and smooth muscle cell markers. At higher doses, DMF also inhibited the pro-proliferative action of TNF-α by increasing apoptosis which protected the cells from aponecrosis. In mice, DMF treatment significantly decreased the incidence of aneurysm formation and rupture, at the same time increasing Nrf2 levels. DMF demonstrated a neuroprotective effect in mice with a resultant inhibition of oxidative stress, inflammation, and fibrosis in the cerebrovasculature. This suggests a potential role for DMF as a rescue therapy for patients at risk for formation and rupture of IAs.

PET using FDG is a critical tool for evaluation of dementias, with characteristic patterns of hypometabolism suggesting specific diagnoses. Hypometabolism in the occipital region is recognized as an important finding associated with dementia with Lewy bodies and posterior cortical atrophy. We describe here the novel "occipital tunnel" sign, which results from relative sparing of FDG uptake in the medial occipital (primary visual) cortex compared with more severe loss in the surrounding lateral occipital (visual association) cortex. This sign is useful for recognizing the occipital findings of dementia with Lewy bodies and posterior cortical atrophy, especially when viewing sagittal projections.

Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluate a set of algorithms to segment OCT images of mouse ovaries. We examine five preprocessing techniques and seven segmentation algorithms. While all preprocessing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% AE 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 94.8% AE 1.2% compared with manual segmentation. Even so, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.

Dementia is an important cause of morbidity and mortality worldwide and encompasses a very heterogenous group of disease processes. Positron emission tomography (PET) of the brain using fluorodeoxyglucose (FDG) is a useful modality for differentiating types of dementia. Because FDG does not bind to pathologic proteins, FDG-PET requires that the reader recognize characteristic patterns of glucose hypometabolism to identify pathology. These patterns have been documented in the literature for both primary neurodegenerative disorders and secondary causes of dementia. This article presents an algorithm for organizing these findings and systematically applying them to interpret FDG-PET brain imaging for dementia.

No abstract

Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluate a set of algorithms to segment OCT images of mouse ovaries. We examine five preprocessing techniques and seven segmentation algorithms. While all preprocessing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% AE 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 94.8% AE 1.2% compared with manual segmentation. Even so, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.