Estimation of bladder wall location in ultrasound images

A method of automatically estimating the location of the bladder wall in ultrasound images is proposed. Obtaining this estimate is intended to be the first stage in the development of an automatic bladder volume calculation system. The first step in the bladder wall estimation scheme involves globally processing the images using standard image processing techniques to highlight the bladder wall. Separate processing sequences are required to highlight the anterior bladder wall and the posterior bladder wall. The sequence to highlight the anterior bladder wall involves Gaussian smoothing and second differencing followed by zero-crossing detection. Median filtering followed by thresholding and gradient detection is used to highlight as much of the rest of the bladder wall as was visible in the original images. Then a ‘bladder wall follower’—a line follower with rules based on the characteristics of ultrasound imaging and the anatomy involved—is applied to the processed images to estimate the bladder wall location by following the portions of the bladder wall which are highlighted and filling in the missing segments. The results achieved using this scheme are presented.     

Magnetic resonance imaging of the humpback scaphoid: the technique and a mathematical performance evaluation

Surgical treatment of scaphoid nonunion and malunion with excessive intrascaphoid angulation (humpback deformity) usually involves a bone graft that is intended to correct the deformity. The volar bone graft length determines the degree of angular correction and scaphoid elongation. It is recommended that the length of the graft be determined by careful preoperative measurement of the deformity. Previous imaging techniques are inherently limited. The present paper describes a technique using three-dimensional magnetic resonance imaging. Scaphoid fracture angulation is calculated from measurements of comparable sagittal slices of the patient's fractured and normal scaphoid. Optimal bone graft length is determined by using simple trigonometric principles. Magnetic resonance imaging provides additional important information regarding vascularity of the proximal pole and the status of the periscaphoid ligaments and hyaline cartilage. Mathematical performance evaluation indicates that this technique is a promising method for planning reconstructive surgery of the scaphoid.     

In vivo hematopoietic effects of recombinant interleukin-1 alpha in mice: stimulation of granulocytic, monocytic, megakaryocytic, and early erythroid progenitors, suppression of late-stage erythropoiesis, and reversal of erythroid suppression with erythropoietin

Interleukin-1 alpha (IL-1 alpha) is a macrophage-derived, multifunctional cytokine that broadly potentiates myelopoiesis and induces the synthesis of hematopoietic colony-stimulating factors (CSF) in vitro and in vivo. To evaluate the possibility for use of IL-1 alpha in ameliorating in vivo bone marrow suppression induced by drugs or radiation, we examined the in vivo effects of the cytokine on erythropoietic and other hematopoietic progenitor cells. Normal mice were treated with a single intraperitoneal (IP) injection of recombinant human IL-1 alpha at varying doses and were assayed at various times post-treatment. By six hours postinjection, a significant suppression of mature erythroid progenitors (CFU-E) was observed in animals treated with IL-1 alpha (0.5 micrograms/mouse), with maximum suppression of CFU-E and peripheral blood reticulocyte counts occurring at 24 hours. Decreases in peripheral blood hematocrit did not occur after a single IL-1 alpha injection but were observed after multiple injections of the cytokine. The suppressive effects of IL-1 alpha on late-stage erythropoiesis were abrogated by simultaneous administration of erythropoietin (EPO). At 48 hours post-treatment, a marked stimulation was observed in the numbers of spleen and marrow immature erythroid (BFU-E), macrophage (CFU-M), granulocyte (CFU-G), granulocyte- macrophage (CFU-GM), and megakaryocyte (CFU-meg) progenitor cells. These results demonstrate the potential use of IL-1 alpha as a generalized stimulator of hematopoiesis and show that the cytokine- induced suppression of late-stage erythropoiesis can be prevented by EPO.     

Comparison of the roles of insulin and insulin-like growth factor I in casein gene expression and in the development of alpha-lactalbumin and glucose transport activities in the mouse mammary epithelial cell

The concentration-activity profiles for insulin and insulin-like growth factor I (IGF-I; in the presence of and insulin-like growth factor I (IGF-I; in the presence of hydrocortisone and PRL) have been compared in terms of the accumulation of beta-casein mRNA, total casein synthesis, and alpha-lactalbumin and basal carrier-mediated glucose transport activities in mammary epithelial cells from midpregnant mice. For the accumulation of the casein mRNA and the induction of casein synthesis and alpha-lactalbumin activity, the insulin ED50 is 1-2 ng/ml, while that for IGF-I is 10- to 20-fold greater. The effects of insulin and IGF-I are not additive in these instances. For the induction of basal carrier-mediated glucose transport, the insulin ED50 is 8 ng/ml, and that for IGF-I is 16 ng/ml. Either factor can induce transport activity up to the level present in the cells from 2-day lactating mice. In this instance the effects are additive; insulin and IGF-I together can induce the transport up to the 10-day lactating level.     

LEAP: Using machine learning to support variant classification in a clinical setting

Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogenicity (LEAP), a machine learning model that utilizes a variety of feature categories to classify variants, and achieves high performance in multiple genes and different health conditions. Feature categories include functional predictions, splice predictions, population frequencies, conservation scores, protein domain data, and clinical observation data such as personal and family history and covariant information. L2‐regularized logistic regression and random forest classification models were trained on missense variants detected and classified during the course of routine clinical testing at Color Genomics (14,226 variants from 24 cancer‐related genes and 5,398 variants from 30 cardiovascular‐related genes). Using 10‐fold cross‐validated predictions, the logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 97.8% (cancer) and 98.8% (cardiovascular), while the random forest model achieved 98.3% (cancer) and 98.6% (cardiovascular). We demonstrate generalizability to different genes by validating predictions on genes withheld from training (96.8% AUROC). High accuracy and broad applicability make LEAP effective in the clinical setting as a high‐throughput quality control layer.