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Cardiac tissue remodeling in the course of chronic left ventricular hypertrophy requires phagocytes which degrade cellular debris, initiate and maintain tissue inflammation and reorganization. The dynamics of phagocytes in left ventricular hypertrophy have not been systematically studied. Here, we characterized the temporal accumulation of leukocytes in the cardiac immune response by flow cytometry and fluorescence microscopy at day 3, 6 and 21 following transverse aortic constriction (TAC). Cardiac hypertrophy due to chronic pressure overload causes cardiac immune response and inflammation represented by an increase of immune cells at all three time points among which neutrophils reached their maximum at day 3 and macrophages at day 6. The cardiac macrophage population consisted of both Ly6Clow and Ly6Chigh macrophages. Ly6Clow macrophages were more abundant peaking at day 6 in response to pressure overload. During the development of cardiac hypertrophy the expression pattern of adhesion molecules was investigated by qRT-PCR and flow cytometry. CD11b, CX3CR1 and ICAM-1 determined by qRT-PCR in whole cardiac tissue were up-regulated in response to pressure overload at day 3 and 6. CD11b and CX3CR1 were significantly increased by TAC on the surface of Ly6Clow but not on Ly6Chigh macrophages. Furthermore, ICAM-1 was up-regulated on cardiac endothelial cells. In fluorescence microscopy Ly6Clow macrophages could be observed attached to the intra- and extra-vascular vessel-wall. Taken together, TAC induced the expression of adhesion molecules, which may explain the accumulation of Ly6Clow macrophages in the cardiac tissue, where these cells might contribute to cardiac inflammation and remodeling in response to pressure overload.

computed tomography (ct) and magnetic resonance imaging (MRi) can quantify muscle mass and quality. However, it is still unclear if ct and MRi derived measurements can be used interchangeable. In this prospective study, fifty consecutive participants of a cancer screening program underwent same day low-dose chest ct and MRi. cross-sectional areas (cSA) of the paraspinal skeletal muscles were obtained. CT and MRI muscle fat infiltration (MFI) were assessed by mean radiodensity in Hounsfield units (HU) and proton density fat fraction (MRi pDff), respectively. cSA and Mfi were highly correlated between CT and MRI (CSA: r = 0.93, P < 0.001; MFI: r = − 0.90, P < 0.001). Mean CSA was higher in CT compared to MRI (46.6cm 2 versus 43.0cm 2 ; P = 0.05) without significance. Based on MRI pDff , a linear regression model was established to directly estimate skeletal muscle fat content from ct. Bland-Altman plots showed a difference between measurements of − 0.5 cm 2 to 7.6 cm 2 and − 4.2% to 2.4% regarding measurements of cSA and Mfi, respectively. in conclusion, the provided results indicate interchangeability of ct and MRi derived imaging biomarkers of skeletal muscle quantity and quality. comparable to MRi pDff , skeletal muscle fat content can be quantified from CT, which might have an impact of analyses in larger cohort studies, particularly in sarcopenia patients. Decrease in skeletal muscle quantity and quality, commonly termed sarcopenia, is known as a strong risk factor for adverse outcomes in several chronic and malignant diseases. Sarcopenia was shown to have high socioeconomic and personal burden and leads to impaired activity in daily life, decreased mobility, loss of independency and a higher mortality 1-3. Initially, sarcopenia was considered to be an age-related phenomenon 4. However, it is now increasingly realized that sarcopenia may also occur in younger patients, for example secondary due to systemic diseases. Moreover, it was realized that sarcopenia may not be captured by conventional anthropometric measurements such as body mass index (BMI) or waist-to-hip-ratio, particularly in obese patients 1,5. Amount and quality of skeletal muscles can be assessed by cross-sectional imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Previous studies indicate that both modalities may provide imaging based quantitative biomarkers of sarcopenia, and that these biomarkers may reveal prognostic information in various severe diseases 6-10. Thereby, cross sectional areas (CSA) of skeletal muscles at distinct anatomical landmarks were shown to provide accurate surrogates of total skeletal muscle amount and therefore may be used to identify patients with low muscle mass 1,6,7. The most common approaches to estimate skeletal muscle amount are determination of circumferential skeletal muscle area or psoas muscle area, both typically obtained at lumbar vertebral levels 1. However, these landmarks are frequently not captured in several imaging protocols, although sarcopenia is known to b...

BACKGROUND AND PURPOSE:The rupture of an intracranial aneurysm is a serious incident, causing subarachnoid hemorrhage associated with high fatality and morbidity rates. Because the demand for radiologic examinations is steadily growing, physician fatigue due to an increased workload is a real concern and may lead to mistaken diagnoses of potentially relevant findings. Our aim was to develop a sufficient system for automated detection of intracranial aneurysms. MATERIALS AND METHODS:In a retrospective study, we established a system for the detection of intracranial aneurysms from 3D TOF-MRA data. The system is based on an open-source neural network, originally developed for segmentation of anatomic structures in medical images. Eighty-five datasets of patients with a total of 115 intracranial aneurysms were used to train the system and evaluate its performance. Manual annotation of aneurysms based on radiologic reports and critical revision of image data served as the reference standard. Sensitivity, false-positives per case, and positive predictive value were determined for different pipelines with modified pre-and postprocessing. RESULTS:The highest overall sensitivity of our system for the detection of intracranial aneurysms was 90% with a sensitivity of 96% for aneurysms with a diameter of 3-7 mm and 100% for aneurysms of Ͼ7 mm. The best location-dependent performance was in the posterior circulation. Pre-and postprocessing sufficiently reduced the number of false-positives. CONCLUSIONS:Our system, based on a deep learning convolutional network, can detect intracranial aneurysms with a high sensitivity from 3D TOF-MRA data.ABBREVIATIONS: CNN ϭ convolutional neural network; DSC ϭ Dice similarity coefficient; FPs/case ϭ false-positives per case

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