Management of extrapulmonary sarcoidosis: challenges and solutions.

BACKGROUND: Sarcoidosis is a chronic multisystem disease of unknown etiology characterized by noncaseating granulomas that most often involves the lungs, but frequently has extrapulmonary manifestations, which might be difficult to treat in individual patients. OBJECTIVE: To review different disease manifestations, focusing on extrapulmonary organ systems, and to provide treatment options for refractory cases. MATERIALS AND METHODS: We performed a literature search using Medline and Google Scholar for individual or combined keywords of "sarcoidosis, extrapulmonary, treatment, kidney, neurosarcoidosis, cardiovascular, gastrointestinal, transplantation, musculoskeletal, rheumatology, arthritis, and skin". Peer-reviewed articles, including review articles, clinical trials, observational trials, and case reports that were published in English were included. References from retrieved articles were also manually searched for relevant articles. RESULTS AND CONCLUSION: Isolated involvement of a single organ or organ system is rare in sarcoidosis, and thus all patients must be thoroughly evaluated for additional disease manifestations. Cardiac sarcoidosis and neurosarcoidosis may be life-threatening. Clinicians need to assess patients comprehensively using clinical, laboratory, imaging, and histopathological data to recommend competently the best and least toxic treatment option for the individual patient.

Selective cytokine inhibitory drugs with enhanced antiangiogenic activity control tumor growth through vascular inhibition.

Selective cytokine inhibitory drugs (SelCIDs) are a novel class of phosphodiesterase 4 inhibitors discovered during a thalidomide analog discovery program. These analogs were evaluated for their ability to inhibit tumor angiogenesis, vascularity, and growth. Two analogs (CC-7034 and CC-9088) were identified that had enhanced antiangiogenic activity in Matrigel assays compared with parental thalidomide. These analogs also inhibited the growth of established K1735 and RENCA murine tumors. Tumors whose growth was suppressed by SelCID treatment exhibited decreased vessel density together with increased tumor cell hypoxia and death. The decrease in vascularity produced by SelCID treatment is attributed to a selective loss of vessels devoid of pericyte coverage, suggesting that these agents target immature tumor vessels. That tumor cell death was localized to relatively avascular or hypoxic areas, coupled with the fact that none of the analogs was cytotoxic in vitro against the tumor cells, demonstrates that these analogs are novel antivascular agents with potent antitumor activity.

Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series.

Automated methods are described for tracing and analysis of changes in angiogenic vasculature imaged by a multiphoton laser-scanning confocal microscope. Utilizing chronic animal window models, time series of in vivo 3-D images were acquired on approximately the same target volume of the same specimen while undergoing angiogenic change (typically every 24 h for 7 days). Objective, precise, 3-D, rapid, and fully automated vessel morphometry was performed using an adaptive tracing algorithm that is based on a generalized irregular cylinder model of the vasculature. This algorithm was found to be not only adaptive enough for tracing angiogenic vasculature, but also very efficient in its use of computer memory, and fast, taking less than 1 min to trace a 768 x 512 x 32, 8-bit/pixel 3-D image stack on a Dell Pentium III 1-GHz computer. The automatically traced centerlines were manually validated on six image stacks and the average spatial error was measured to be 2 pixels, with an average concordance of 81% between manual and automated traces on a voxel basis. The tracing output includes geometrical statistics of traced vasculature and serves as the basis of statistical change analysis. The computer methods described here are designed to be scalable to much larger hypothesis testing studies involving quantitative measurements of tumor angiogenesis, gene expression relative to known vascular structures, and impact of drug delivery.

Median-based robust algorithms for tracing neurons from noisy confocal microscope images.

This paper presents a method to exploit rank statistics to improve fully automatic tracing of neurons from noisy digital confocal microscope images. Previously proposed exploratory tracing (vectorization) algorithms work by recursively following the neuronal topology, guided by responses of multiple directional correlation kernels. These algorithms were found to fail when the data was of lower quality (noisier, less contrast, weak signal, or more discontinuous structures). This type of data is commonly encountered in the study of neuronal growth on microfabricated surfaces. We show that by partitioning the correlation kernels in the tracing algorithm into multiple subkernels, and using the median of their responses as the guiding criterion improves the tracing precision from 41% to 89% for low-quality data, with a 5% improvement in recall. Improved handling was observed for artifacts such as discontinuities and/or hollowness of structures. The new algorithms require slightly higher amounts of computation, but are still acceptably fast, typically consuming less than 2 seconds on a personal computer (Pentium III, 500 MHz, 128 MB). They produce labeling for all somas present in the field, and a graph-theoretic representation of all dendritic/axonal structures that can be edited. Topological and size measurements such as area, length, and tortuosity are derived readily. The efficiency, accuracy, and fully-automated nature of the proposed method makes it attractive for large-scale applications such as high-throughput assays in the pharmaceutical industry, and study of neuron growth on nano/micro-fabricated structures. A careful quantitative validation of the proposed algorithms is provided against manually derived tracing, using a performance measure that combines the precision and recall metrics.

Rapid automated three-dimensional tracing of neurons from confocal image stacks.

Algorithms are presented for fully automatic three-dimensional (3-D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of 4 x N2 directional kernels (e.g., N = 32), guided by a generalized 3-D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3-D space. Since the centerlines are of primary interest, the 3-D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70-MB image on a 500-MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project.