Microwave techniques for diagnostic laboratories

Microwaves (MWs) were first introduced as a method of fixation just over 20 years ago. In recent years their use has extended far beyond that of a safe, clean and rapid method of fixation of tissue blocks and large specimens, including brains. MWs accelerate the action of cross-linking fixatives and can greatly accelerate the various stages of tissue processing to produce a paraffin block in 30 min. An extensive range of ultrafast MW-stimulated special stains has been developed, and immunohistochemical procedures can be completed in 20 min by employing MWs. Cellular antigens are distinctly better preserved in tissues fixed by MWs than by conventional cross-linking fixatives. Also, the cytomorphology of cryostat sections irradiated in Wolman's solution is clearly improved. MWs can similarly be applied for fixation and staining of preparations for transmission and scanning electron microscopy, and they also greatly accelerate polymerisation of resins. In the current climate of cost containment, this wide range of applications makes the MW oven an invaluable addition to the diagnostic laboratory.     

Knowledge about asthma among Danish primary school teachers. Results of a questionnaire study

An anonymous questionnaire inquiry performed among 324 school teachers in the Randers area with the purpose of elucidating teachers' knowledge about asthma is presented. To a series of statements about asthma, the teachers should answer yes, no or don't know. A limited knowledge about asthma in children was found. The knowledge about medicamental treatment was particularly limited. Only 57 per cent knew that wheezing after physical exertion is a strong indicator of asthma. Only five per cent had received proper instruction. This latter group had a significantly better knowledge of medical treatment (p < 0.0001-0.05). It is recommended that instruction in children's diseases, especially asthma should be reintroduced in teachers' training colleges.     

Granulocyte colony-stimulating factor worsens the outcome of experimental Klebsiella pneumoniae pneumonia through direct interaction with the bacteria

Besides its well-established effects on granulocytopoiesis, granulocyte colony-stimulating factor (G-CSF) has been shown to have direct effects on the recruitment and bactericidal ability of neutrophils, resulting in improved survival of experimentally infected animals. We studied the effect of G-CSF on the course of experimental pneumonia induced by Klebsiella pneumoniae, an important gram-negative bacillary pulmonary pathogen. Using a highly reproducible murine model, we here show the paradoxical finding that mortality from infection was significantly increased when animals received G-CSF before induction of pneumonia. Administration of G-CSF promoted replication of bacteria in the liver and spleen, thus indicating an impairment rather than an enhancement of antibacterial mechanisms. By contrast, a monoclonal antibody against Klebsiella K2 capsule significantly reduced bacterial multiplication in the lung, liver, and spleen, and abrogated the increased mortality caused by G-CSF. In vitro studies showed a direct effect of G-CSF on K pneumoniae resulting in increased capsular polysaccharide (CPS) production. When bacteria were coincubated with therapeutically achievable concentrations of G-CSF, phagocytic uptake and killing by neutrophils was impaired. Western blot analysis showed three binding sites of G-CSF to K pneumoniae. Binding of 125I-G-CSF to K pneumoniae was displaced by an excess of unlabeled G-CSF, whereas an unrelated cytokine, interleukin-1alpha, did not compete with G-CSF binding to the bacteria. Thus, in this model, the direct effect of G-CSF on a bacterial virulence factor, CPS production, outweighed any beneficial effect of G-CSF on recruitment and stimulation of leukocytes.     

Radical radiotherapy for squamous cell carcinoma of the larynx, oropharynx and hypopharynx: patterns of recurrence, treatment and survival

The present study investigates 1085 previously untreated patients with squamous cell carcinoma of the head and neck for factors that influence recurrence following treatment with definitive radiotherapy. In the second part of this study, those diagnosed with recurrence were studied with regard to treatment and were further analysed with regard to identification of prognostic factors which may affect outcome in the patient who actually develops a recurrence.     

Pathways and crossroads to institutional leadership.

When upwardly mobile persons are promoted from lower individual contributor roles to higher, managerial roles, they are confronted by the challenge of negotiating a series of 135-degree "crossroads" or shifts in their careers. Those who make the complete journey must traverse five pathways and four crossroads. These critical career crossroads consist of discontinuous and unprecedented changes in the role responsibilities and account abilities to which managers in transition must respond. At each crossroads, people are confronted by a triple challenge: letting go of anachronistic responsibilities and competencies, preserving those that continue to be useful, and adding new discontinuous responsibilities and consequences. Managers in transition can cope with these demands by making adaptive changes in their preferred activities, behavior patterns, and style. A detailed examination of the unprecedented discontinuities that ambitious, upwardly mobile managers must bridge is presented. The adaptation process that is required at each career crossroads is described, followed by a detailed set of recommended shifts in behavior patterns for the future executive at each crossroads. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prediction of loss characteristics in built-up areas with variousbuildings' overlay profiles

We continue the analysis of a probabilistic approach and the corresponding stochastic multi-parametric model of wave propagation in built-up areas with randomly distributed buildings. We concentrate on the influence of buildings' overlay profiles on signal spatial decay, and on path-loss dependence in the frequency domain within UHF/X-band urban propagation channels. Using different buildings' overlay profiles, the field-intensity attenuation along radio paths is examined, taking into account single-scattering and multiple-scattering phenomena, and diffraction from buildings' corners and rooftops, for various positions of receiver and transmitter antennas with respect to surrounding obstacles. The comparison between experimental and theoretical predictions was motivated by the proposed stochastic multi-parametric model and the experimental data obtained for actual areas in Jerusalem (Israel) and Lisbon (Portugal), as well as by other models of multiple diffraction. The discussion is presented for realistic elevations of both terminal antennas to assess the accuracy and limits of the proposed stochastic model. A sensitivity analysis of the influence on the path loss of built-up terrain parameters and the elevation of antennas relative to the buildings surrounding them is presented     

A multiple circular path convolution neural network system for detection of mammographic masses

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

Reduction of false positives in lung nodule detection using a two-level neural classification

The authors have developed a neural-digital computer-aided diagnosis system, based on a parameterized two-level convolution neural network (CNN) architecture and on a special multilabel output encoding procedure. The developed architecture was trained, tested, and evaluated specifically on the problem of diagnosis of lung cancer nodules found on digitized chest radiographs. The system performs automatic "suspect" localization, feature extraction, and diagnosis of a particular pattern-class aimed at a high degree of "true-positive fraction" detection and low "false-positive fraction" detection. In this paper, the authors aim at the presentation of the two-level neural classification method in reducing false-positives in their system. They employed receiver operating characteristics (ROC) method with the area under the ROC curve (A(z)) as the performance index to evaluate all the simulation results. The two-level CNN showed superior performance (A(z)=0.93) to the single-level CNN (A(z)=0.85). The proposed two-level CNN architecture is proven to be promising and to be extensible, problem-independent, and therefore, applicable to other medical or difficult diagnostic tasks in two-dimensional (2-D) image environments.