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H A Kestler

Publications and source records attributed to H A Kestler.

14 recordsLinked to original sources
PubMed2003

[Effect of molsidomine on rheological parameters and the incidence of cardiovascular events].

BACKGROUND AND OBJECTIVE: In-vitro studies revealed that nitric oxide (NO) may affect rheological parameters. We studied the effect of highly-dosed NO-donor molsidomine on blood rheology and the impact of rheological parameters on the incidence of severe cardiovascular events. PATIENTS AND METHODS: In this randomized, placebo-controlled and double-blind trial 166 patients (60 +/- 10 years) with stable angina pectoris and coronary intervention received molsidomine 3 x 8 mg t. i. d. (controlled release tablets) or placebo for 6 months. Patients with inflammatory/neoplastic disorders or elevated values of C-reactive protein were excluded from analysis. A rheological profile (plasma viscosity, blood viscosity, aggregation and flexibility of erythrocytes, filtrability of leukocytes, fibrinogen levels) was done initially and after 6 months. Adverse cardiovascular events (death, myocardial infarction, stroke, coronary/peripheral revascularization) were recorded during 12 months. Furthermore, the impact of rheological parameters regarding the occurrence of severe cardiovascular events (death, myocardial infarction, stroke) was evaluated during a follow-up of median 38 months. RESULTS: The data of 137 patients (n = 71 placebo, n = 66 molsidomine) were analysed. The difference of rheological parameters between the two measurements did not vary between the two groups. Analysis of event-free survival with Kaplan-Meier technique revealed no difference between the two groups. Multivariate Cox regression analysis with adjustment for diabetes mellitus, smoking and therapy with statin showed a significant association of fibrinogen and plasma viscosity with the occurrence of severe cardiovascular events. CONCLUSION: Treatment with molsidomine 3 x 8 mg/day for 6 months does not improve blood rheology or reduce cardiovascular events. But elevated levels of fibrinogen and plasma viscosity were associated with the occurrence of severe cardiovascular events.

Blood Viscosity↗
PubMed2003

DNA microarray analysis in malignant lymphomas.

Recently, DNA microarray technology has opened new avenues for the understanding of lymphomas. By hybridization of cDNA to arrays containing >10,000 different DNA fragments, this approach allows the simultaneous evaluation of the mRNA expression of thousands of genes in a single experiment. Using sophisticated bioinformatic tools, the huge amount of raw data can be clustered resulting in (1) tumor subclassification, (2) identification of pathogenetically relevant genes, or (3) biological predictors for the clinical course. This approach already has provided novel insights into different entities of B-cell non-Hodgkin's lymphomas. Genomic DNA chip hybridization (matrix-CGH) is a complementary approach focussing on genomic aberrations. In this review, we discuss the impact of this new technology both with regard to methodological aspects as well as to novel findings influencing our understanding of lymphomas.

Gene Expression Profiling↗
PubMed2001

ROC with confidence - a Perl program for receiver operator characteristic curves.

Receiver operator characteristic (ROC) curves are recommended to assess the diagnostic value of tests depending on a single cut-off value of a continuous variable. These ROC curves show the true-positive rate (sensitivity) against the false-positive rate (1-specificity). It is desirable, especially in situations with small samples of observations, to display confidence bounds of the ROC curve. This paper presents a Perl program which calculates the ROC curve and its distribution-free confidence bounds. A simple user interface also written in Perl permits their display.

Confidence Intervals↗
PubMed2001

Three learning phases for radial-basis-function networks.

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Algorithms↗
PubMed2001

Cluster analysis of comparative genomic hybridization (CGH) data using self-organizing maps: application to prostate carcinomas.

Comparative genomic hybridization (CGH) is a modern genetic method which enables a genome-wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more) metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20-30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self-organizing map (Genecluster) as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self-organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data). We studied a group of 40 recent cases without follow-up, an older group of 20 cases with follow-up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance.

Carcinoma↗
PubMed2001

Prediction of postoperative prostatic cancer stage on the basis of systematic biopsies using two types of artificial neural networks.

OBJECTIVE: The choice of therapy for prostatic cancer should depend on a rational preoperative estimate of tumor stage. Artificial neural networks were used to predict postoperative staging of prostatic cancer from sextant biopsies and routinely available preoperative data. METHODS: In group I (97 cases), nonorgan confinement (tumor stage > or =pT3a) was predicted on the basis of age and six histopathological variables from sextant biopsies. In group II (77 cases), nonorgan confinement and extraprostatic organ infiltration (tumor classification > or =pT3b) were predicted from age, four histopathological variables, the preoperative PSA level, and the total prostate volume estimated by preoperative ultrasonography. Learning vector quantization (LVQ) networks were applied for this purpose and compared to multilayer perceptrons (MLP) and linear discriminant analysis (LDA). RESULTS: Nonorgan confinement could be predicted correctly in 90% of newly presented cases from sextant biopsy histopathology alone. A similar accuracy of predicting nonorgan confinement (83%) was obtained by combining preoperative biopsy histology with clinical data. Extraprostatic organ infiltration could be predicted correctly in 82%. The best results were obtained by LVQ networks, followed by MLP networks and LDA. CONCLUSION: The postoperative tumor stage of prostatic cancer can be estimated with high accuracy, sensitivity and specificity from preoperative routine parameters using artificial neural networks, especially LVQ networks. The results suggest that this methodology should be evaluated in a larger prospective study.

Aged↗
PubMed2000

Cardiac vulnerability assessment from electrical microvariability of high-resolution electrocardiogram.

Patients susceptible to malignant arrhythmias often have an increased beat-to-beat variation of the T-wave of the electrocardiogram. Variability analysis of the T-wave is increasingly used for non-invasive risk assessment. The aim of this study is to evaluate intra-QRS beat-to-beat signal variation and to compare it to ST-T variation. The beat-to-beat, microvolt variation of the QRS and the ST-T segment from 44 patients with coronary heart disease at high risk of suffering from malignant arrhythmias and from 51 healthy volunteers are compared. Variation analysis is carried out on 250 consecutive sinus beats from high-resolution electrocardiograms. The individual beats are filtered using a waveform-independent, cubic spline-filter. A variability index of the QRS and ST-T segments is calculated as the integrated standard deviation of corresponding samples inside the area of interest. Patients at risk of suffering from malignant arrhythmias have a significantly higher variability index of both the QRS (median 44.5 ms against 34.7 ms, p < 0.001) and the ST-T segment (median 20.5 ms against 9.8 ms, p < 0.001) compared to the group of healthy subjects. The discriminative ability of the odds variability indices of the QRS and ST-T segments are not statistically different, the ratios being 7.8 (QRS) and 12.6 (ST-T). We conclude that patients at high risk of suffering from malignant arrhythmias are characterised by an increased beat-to-beat microvolt variation of both the QRS and the ST-T segment. Further studies are necessary to evaluate the prognostic potential of depolarisation variability.

Adult↗
PubMed2000

Classification of spatial textures in benign and cancerous glandular tissues by stereology and stochastic geometry using artificial neural networks.

Stereology and stochastic geometry can be used as auxiliary tools for diagnostic purposes in tumour pathology. Whether first-order parameters or stochastic-geometric functions are more important for the classification of the texture of biological tissues is not known. In the present study, volume and surface area per unit reference volume, the pair correlation function and the centred quadratic contact density function of epithelium were estimated in three case series of benign and malignant lesions of glandular tissues. The information provided by the latter functions was summarized by the total absolute areas between the estimated curves and their horizontal reference lines. These areas are considered as indicators of deviation of the tissue texture from a completely uncorrelated volume process and from the Boolean model with convex grains, respectively. We used both areas and the first-order parameters for the classification of cases using artificial neural networks (ANNs). Learning vector quantization and multilayer feedforward networks with backpropagation were applied as neural paradigms. Applications included distinction between mastopathy and mammary cancer (40 cases), between benign prostatic hyperplasia and prostatic cancer (70 cases) and between chronic pancreatitis and pancreatic cancer (60 cases). The same data sets were also classified with linear discriminant analysis. The stereological estimates in combination with ANNs or discriminant analysis provided high accuracy in the classification of individual cases. The question of which category of estimator is the most informative cannot be answered globally, but must be explored empirically for each specific data set. Using learning vector quantization, better results could often be obtained than by multilayer feedforward networks with backpropagation.

Breast↗
PubMed1999

Prediction of prostatic cancer progression after radical prostatectomy using artificial neural networks: a feasibility study.

OBJECTIVE: To report a methodological feasibility study in a small series of patients with node-negative organ-confined prostatic cancer, using artificial neural networks to predict tumour progression after radical prostatectomy and thus help to identify high-risk patients who would benefit from adjuvant treatment. PATIENTS AND METHODS: A group of 20 patients with pT2N0 prostatic cancer and postoperative tumour progression was compared with a control group of 20 patients with no progression, matched for age, duration of follow-up and preoperative serum prostate-specific antigen level. Histopathological data were obtained from the radical prostatectomy specimens, i.e. the Gleason score, World Health Organisation (WHO) grade and maximum diameter of the tumour transects. The volume and surface area of the epithelial tumour component and of the lumina of the neoplastic glands per unit tissue volume were estimated by morphometric methods. To predict recurrence, multilayer feedforward networks with backpropagation (MLFF-BP), two implementations of learning vector quantization (LVQ), and linear discriminant analysis (LDA) were applied. The ability of these models to correctly classify new cases was tested using the 'leave-one-out' technique. RESULTS: Progression was predicted correctly in 85% of newly presented cases from the three routine histopathological variables alone. On the basis of the four morphometric variables alone progression was predicted correctly in 93% of cases. The use of all seven variables as input data only slightly improved the quality of prediction. The best results were obtained by the LVQ networks and LDA, followed by MLFF-BP networks. CONCLUSIONS: In this methodological feasibility study, the progression of pT2N0 prostatic cancer after radical prostatectomy could be predicted with good accuracy, sensitivity and specificity from routine variables or morphometric texture variables using artificial neural networks. These results suggest that this approach should be assessed in a prospective study with more cases.

Biopsy↗
PubMed1998

A remark on the high-conductance calcium-activated potassium channel in human endothelial cells.

The patch-clamp technique was used to examine the presence of large conductance calcium-activated potassium channels (BKCa) in human endothelial cells and to characterize their properties in terms of voltage dependence, ion conduction and blockade by iberiotoxin (IbTX). Experiments were performed using cell-attached and outside-out configurations on human umbilical vein endothelial cells (HUVEC). For the experiments HUVECs, which were passaged 6-19 times, were used. In early passages channel activities were absent suggesting the appearance of BKCa depending on cell culture time. The inverse logarithmic voltage sensitivity was 10.17 mV (median) for cell-attached recordings and 12.10 mV (median) for outside-out patches (membrane voltage range: 60-120 mV, symmetrical 140 mM K+ solutions). The I/V relationship was quasilinear in the range of 0-80 mV and exhibited a nonlinear behaviour under further depolarization suggesting some kind of saturation mechanism. Using a sigmoid function to fit the data, channel conductance was calculated as 172.9 pS (median) for cell-attached patches and as 262.1 pS (median) for outside-out patches. IbTX, known as one of the most selective blockers of BKCa was perfused to outside-out patches. In two out of three experiments there was complete block of the ion channel after 1 min.

Cells, Cultured↗
PubMedDate not supplied

[Classification of cytological smears of the cervix with neuronal methods].

BACKGROUND: Cytological smears obtained from the cervix are routinely examined under the microscope as part of screening programs for the early detection of cervical cancer. The aim of the present study was to investigate whether a simple feature extraction approach using only standard image processing techniques combined with a neural classifier would lead to acceptable results that might serve as a starting point for the development of a fully automated screening system. MATERIALS AND METHODS: Gray-value images of 106 cervical smears (512 x 512 pixels) divided into two groups--inconspicuous (57) and atypical (49)--by an experienced pathologist on the basis of the original smears were employed to evaluate the method. From these images, 31 features quantifying properties of either the cell nucleus or the cytoplasm were extracted. These features were categorized with three different architectures of a neural classifier: learning vector quantization (LVQ), multilayer perceptron (MLP) and a single perceptron. CONCLUSIONS: The results show a reclassification accuracy of about 91% for all three algorithms. Sensitivity was uniform at approximately 78%, and specificity varied between 75% and 91% in the leave-one-out evaluation. These very good results provide strong encouragement for further studies involving PAP scores and colour images.

Artificial Intelligence↗