114 research outputs found
In vivo CHI3L1 (YKL-40) expression in astrocytes in acute and chronic neurological diseases
<p>Abstract</p> <p>Background</p> <p>CHI3L1 (YKL-40) is up-regulated in a variety of inflammatory conditions and cancers. We have previously reported elevated CHI3L1 concentration in the cerebrospinal fluid (CSF) of human and non-human primates with lentiviral encephalitis and using immunohistochemistry showed that CHI3L1 was associated with astrocytes.</p> <p>Methods</p> <p>In the current study CHI3L1 transcription and expression were evaluated in a variety of acute and chronic human neurological diseases.</p> <p>Results</p> <p>ELISA revealed significant elevation of CHI3L1 in the CSF of multiple sclerosis (MS) patients as well as mild elevation with aging. <it>In situ </it>hybridization (ISH) showed CHI3L1 transcription mostly associated with reactive astrocytes, that was more pronounced in inflammatory conditions like lentiviral encephalitis and MS. Comparison of CHI3L1 expression in different stages of brain infarction showed that YKL40 was abundantly expressed in astrocytes during acute phases and diminished to low levels in chronic infarcts.</p> <p>Conclusions</p> <p>Taken together, these findings demonstrate that CHI3L1 is induced in astrocytes in a variety of neurological diseases but that it is most abundantly associated with astrocytes in regions of inflammatory cells.</p
Convolutional Neural Networks for Segmentation of Malignant Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance)
Malignant pleural mesothelioma (MPM) is the most common form of mesothelioma.
To assess response to treatment, tumor measurements are acquired and evaluated
based on a patient's longitudinal computed tomography (CT) scans. Tumor volume,
however, is the more accurate metric for assessing tumor burden and response.
Automated segmentation methods using deep learning can be employed to acquire
volume, which otherwise is a tedious task performed manually. The deep
learning-based tumor volume and contours can then be compared with a standard
reference to assess the robustness of the automated segmentations. The purpose
of this study was to evaluate the impact of probability map threshold on MPM
tumor delineations generated using a convolutional neural network (CNN).
Eighty-eight CT scans from 21 MPM patients were segmented by a VGG16/U-Net CNN.
A radiologist modified the contours generated at a 0.5 probability threshold.
Percent difference of tumor volume and overlap using the Dice Similarity
Coefficient (DSC) were compared between the standard reference provided by the
radiologist and CNN outputs for thresholds ranging from 0.001 to 0.9. CNN
annotations consistently yielded smaller tumor volumes than radiologist
contours. Reducing the probability threshold from 0.5 to 0.1 decreased the
absolute percent volume difference, on average, from 43.96% to 24.18%. Median
and mean DSC ranged from 0.58 to 0.60, with a peak at a threshold of 0.5; no
distinct threshold was found for percent volume difference. No single output
threshold in the CNN probability maps was optimal for both tumor volume and
DSC. This work underscores the need to assess tumor volume and spatial overlap
when evaluating CNN performance. While automated segmentations may yield
comparable tumor volumes to that of the reference standard, the spatial region
delineated by the CNN at a specific threshold is equally important.Comment: 10 pages, 7 figures, 2 table
Computerâassisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma
BackgroundRadiolabeled metaiodobenzylguanidine (MIBG) is sensitive and specific for detecting neuroblastoma. The extent of MIBGâavid disease is assessed using Curie scores. Although Curie scoring is prognostic in patients with highârisk neuroblastoma, there is no standardized method to assess the response of specific sites of disease over time. The goal of this study was to develop approaches for Curie scoring to facilitate the calculation of scores and comparison of specific sites on serial scans.ProcedureWe designed three semiautomated methods for determining Curie scores, each with increasing degrees of computer assistance. Method A was based on visual assessment and tallying of MIBGâavid lesions. For method B, scores were tabulated from a schematic that associated anatomic regions to MIBGâpositive lesions. For method C, an anatomic mesh was used to mark MIBGâpositive lesions with automatic assignment and tallying of scores. Five imaging physicians experienced in MIBG interpretation scored 38 scans using each method, and the feasibility and utility of the methods were assessed using surveys.ResultsThere was good reliability between methods and observers. The userâinterface methods required 57 to 110 seconds longer than the visual method. Imaging physicians indicated that it was useful that methods B and C enabled tracking of lesions. Imaging physicians preferred method B to method C because of its efficiency.ConclusionsWe demonstrate the feasibility of semiautomated approaches for Curie score calculation. Although more time was needed for strategies B and C, the ability to track and document individual MIBGâpositive lesions over time is a strength of these methods.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/1/pbc27417.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/2/pbc27417_am.pd
A unique role for IL-13 in inducing esophageal eosinophilia through MID-1 and STAT6
IntroductionEosinophilic esophagitis (EoE) is associated with allergen-driven inflammation of the esophagus and an upregulated Th2 cytokine signature. Recombinant interleukin (IL)-13 (rIL-13) administration to mice induces some of the hallmark features of EoE, including increased eotaxin expression and eosinophil recruitment. Inflammation in EoE has previously been shown to depend on the expression of TRAIL and MID-1, which reduced protein phosphatase 2A (PP2A) activity. The relationship between IL-13 and TRAIL signalling in esophageal eosinophilia is currently unknown.ObjectiveTo investigate the interaction between IL-13-driven eosinophil infiltration and TRAIL or MID-1 in the esophagus.MethodWe administered rIL-13 to wild type (WT), TRAIL-deficient (Tnsf10â/â) or STAT6-deficient (STAT6â/â) mice and targeted MID-1 with small interfering RNA.ResultsrIL-13 administration to mice increased TRAIL and MID-1 expression in the esophagus while reducing PP2A activity. TRAIL deficient, but not STAT6 deficient mice demonstrated increased MID-1 expression and PP2A reduction upon IL-13 challenge which correlated with eosinophil infiltration into the esophagus. Silencing MID-1 expression with siRNA completely ablated IL-13 induced eosinophil infiltration of the esophagus, restored PP2A activity, and reduced eotaxin-1 expression.ConclusionIL-13-driven eosinophil infiltration of the esophagus induced eosinophilia and eotaxin-1 expression in a STAT6-dependent and MID-1-dependent manner. This study highlights a novel mechanism employed by IL-13 to perpetuate eosinophil infiltration
Evaluation of lung MDCT nodule annotation across radiologists and methods
RATIONALE AND OBJECTIVES: Integral to the mission of the National Institutes of Healthâsponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary. MATERIALS AND METHODS: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologistsâ spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects. RESULTS: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively. CONCLUSION: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume
Dome: Distributed object migration environment
Dome is an object based parallel programming environment for heterogeneous distributed networks of machines. This paper gives a brief overview of Dome. We show that Dome programs are easy to write. A description of the load balancing performed in Dome is presented along with performance measurements on a cluster of DEC Alpha workstations connected by a DEC Gigaswitch. A Dome program is compared with a sequential version and one written in PVM. We also present an overview of architecture independent checkpoint and restart in Dome. This research was sponsored by the National Science Foundation and the Defense Advanced Research Projects Agency under Cooperative Agreement NCR-8919038 with the Corporation for National Research Initiatives. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of NSF, CNRI, ARPA, or the U.S. Government. Keywords: Heterogeneous parallel ..
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