Renal effects of drugs that inhibit prostaglandin synthesis

There is extensive, clinical use of antiinflammatory drugs that inhibit prostaglandin synthesis in most, if not all, organs in the body. Much of the therapeutic efficacy of these agents depends on a reduction of prostaglandin synthesis at the site of inflammation. Not surprisingly, many of the side-effects of these drugs are secondary to decreased prostaglandin synthesis in brain, vasculature, stomach, lung, and kidney. In this review, we will focus attention on the effects of these antiinflammatory compounds on renal function, with particular emphasis on renin secretion, control of renal blood flow (RBF), and glomerular filtration rate (GFR). To describe these renal consequences of prostaglandin inhibition, we will briefly review the biochemistry of prostaglandin synthesis and the major known physiologic actions of prostaglandins in the kidney

Segmentation of biological images containing multitarget labeling using the jelly filling framework

Biomedical imaging when combined with digital image analysis is capable of quantitative morphological and physiological characterizations of biological structures. Recent fluorescence microscopy techniques can collect hundreds of focal plane images from deeper tissue volumes, thus enabling characterization of three-dimensional (3-D) biological structures at subcellular resolution. Automatic analysis methods are required to obtain quantitative, objective, and reproducible measurements of biological quantities. However, these images typically contain many artifacts such as poor edge details, nonuniform brightness, and distortions that vary along different axes, all of which complicate the automatic image analysis. Another challenge is due to "multitarget labeling," in which a single probe labels multiple biological entities in acquired images. We present a "jelly filling" method for segmentation of 3-D biological images containing multitarget labeling. Intuitively, our iterative segmentation method is based on filling disjoint tubule regions of an image with a jelly-like fluid. This helps in the detection of components that are "floating" within a labeled jelly. Experimental results show that our proposed method is effective in segmenting important biological quantities

Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction

Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy based on the use of three dimensional (3D) active contours with inhomogeneity correction. The correction information utilizes 3D volume information while addressing intensity inhomogeneity across vertical and horizontal directions. Experimental results demonstrate that the proposed method achieves better performance than other reported methods

Geographic Variation in Informed Consent Law: Two Standards for Disclosure of Treatment Risks

We analyzed 714 jury verdicts in informed consent cases tried in 25 states in 1985–2002 to determine whether the applicable standard of care (“patient” vs. “professional” standard) affected the outcome. Verdicts for plaintiffs were significantly more frequent in states with a patient standard than in states with a professional standard (27 percent vs. 17 percent, P = 0.02). This difference in outcomes did not hold for other types of medical malpractice litigation (36 percent vs. 37 percent, P = 0.8). The multivariate odds of a plaintiff’s verdict were more than twice as high in states with a patient standard than in states with a professional standard (odds ratio = 2.15, 95% confidence interval = 1.32–3.50). The law’s expectations of clinicians with respect to risk disclosure appear to vary geographically

Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation

Recent Decisions

Comments on recent decisions by Charles T. Dunn, Edward J. Flattery, Frank P. Salierno, Lawrence Turner, John M. Anderton, George S. Stratigos, John E. Cosgrove, Richard H. Keen, F. Gerard Feeney, and John C. Mowbray

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods

Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D

Increased expression of programmed death ligand 1 (PD-L1) in human pituitary tumors

PURPOSE: Subsets of pituitary tumors exhibit an aggressive clinical courses and recur despite surgery, radiation, and chemotherapy. Because modulation of the immune response through inhibition of T-cell checkpoints has led to durable clinical responses in multiple malignancies, we explored whether pituitary adenomas express immune-related biomarkers that could suggest suitability for immunotherapy. Specifically, programmed death ligand 1 (PD-L1) has emerged as a potential biomarker whose expression may portend more favorable responses to immune checkpoint blockade therapies. We thus investigated the expression of PD-L1 in pituitary adenomas. METHODS: PD-L1 RNA and protein expression were evaluated in 48 pituitary tumors, including functioning and non-functioning adenomas as well as atypical and recurrent tumors. Tumor infiltrating lymphocyte populations were also assessed by immunohistochemistry. RESULTS: Pituitary tumors express variable levels of PD-L1 transcript and protein. PD-L1 RNA and protein expression were significantly increased in functioning (growth hormone and prolactin-expressing) pituitary adenomas compared to non-functioning (null cell and silent gonadotroph) adenomas. Moreover, primary pituitary adenomas harbored higher levels of PD-L1 mRNA compared to recurrent tumors. Tumor infiltrating lymphocytes were observed in all pituitary tumors and were positively correlated with increased PD-L1 expression, particularly in the functional subtypes. CONCLUSIONS: Human pituitary adenomas harbor PD-L1 across subtypes, with significantly higher expression in functioning adenomas compared to non-functioning adenomas. This expression is accompanied by the presence of tumor infiltrating lymphocytes. These findings suggest the existence of an immune response to pituitary tumors and raise the possibility of considering checkpoint blockade immunotherapy in cases refractory to conventional management

A genomic analysis and transcriptomic atlas of gene expression in Psoroptes ovis reveals feeding- and stage-specific patterns of allergen expression

Background: Psoroptic mange, caused by infestation with the ectoparasitic mite, Psoroptes ovis, is highly contagious, resulting in intense pruritus and represents a major welfare and economic concern for the livestock industry Worldwide. Control relies on injectable endectocides and organophosphate dips, but concerns over residues, environmental contamination, and the development of resistance threaten the sustainability of this approach, highlighting interest in alternative control methods. However, development of vaccines and identification of chemotherapeutic targets is hampered by the lack of P. ovis transcriptomic and genomic resources. Results: Building on the recent publication of the P. ovis draft genome, here we present a genomic analysis and transcriptomic atlas of gene expression in P. ovis revealing feeding- and stage-specific patterns of gene expression, including novel multigene families and allergens. Network-based clustering revealed 14 gene clusters demonstrating either single- or multi-stage specific gene expression patterns, with 3075 female-specific, 890 male-specific and 112, 217 and 526 transcripts showing larval, protonymph and tritonymph specific-expression, respectively. Detailed analysis of P. ovis allergens revealed stage-specific patterns of allergen gene expression, many of which were also enriched in "fed" mites and tritonymphs, highlighting an important feeding-related allergenicity in this developmental stage. Pair-wise analysis of differential expression between life-cycle stages identified patterns of sex-biased gene expression and also identified novel P. ovis multigene families including known allergens and novel genes with high levels of stage-specific expression. Conclusions: The genomic and transcriptomic atlas described here represents a unique resource for the acarid-research community, whilst the OrcAE platform makes this freely available, facilitating further community-led curation of the draft P. ovis genome