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Novel Parameters (novel + parameter)

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Medical Sciences	83%

Selected Abstracts

Novel parameter for the diagnosis of distal middle cerebral artery stenosis with transcranial Doppler sonography

JOURNAL OF CLINICAL ULTRASOUND, Issue 8 2010
Suk-Won Ahn MD
Abstract Purpose Transcranial Doppler sonography (TCD) is commonly used for the diagnosis of middle cerebral artery (MCA) stenosis. However, TCD indices to predict distal MCA (M2) stenosis have not yet been established. We compared TCD and magnetic resonance angiography (MRA) to validate a new index for the diagnosis of M2 stenosis. Methods Consecutive patients who underwent TCD and MRA were included. Based on MRA, M2 stenosis was defined as >50% narrowing beyond the bifurcation area. TCD index of the M2/M1 ratio was defined as the ratio between the mean flow velocity (MFV) obtained at a depth of 30,44 mm (M2) and a depth of 45,65 mm (M1). Sensitivity and specificity of the M2/M1 ratio were calculated from the receiver operating characteristic curve. The diagnostic yield of elevated MFV (>80 cm/s) and asymmetry index of >30% for M2 stenosis were also investigated. Results Among the consecutive patients, 105 with M2 stenosis were compared with 123 without MCA stenosis. The M2/M1 ratio was significantly higher in the M2 stenosis group (1.10 versus 0.86, p < 0.001). Sensitivity and specificity for M2 stenosis were most satisfying when the M2/M1 ratio of 0.97 was adopted as the cutoff value. Diagnostic yield of the M2/M1 ratio was better than MFV or asymmetry index. Conclusions The M2/M1 ratio may be a highly specific parameter for assessing M2 stenosis with TCD. © 2010 Wiley Periodicals, Inc. J Clin Ultrasound 38:420,425, 2010 [source]

Thrombin generation time is a novel parameter for monitoring enoxaparin therapy in patients with end-stage renal disease

JOURNAL OF THROMBOSIS AND HAEMOSTASIS, Issue 2 2006
D. F. BROPHY
Summary.,Background:,Patients with end-stage renal disease (ESRD) who receive enoxaparin are at increased risk for adverse bleeding episodes. This phenomenon appears to occur despite judicious monitoring of antifactor Xa (aFXa) activity. Better monitoring parameters are needed to quantify the anticoagulant effects of enoxaparin in the ESRD population. Objectives:,The objective of this study was to determine the utility of using thrombin generation time (TGT), platelet contractile force (PCF) and clot elastic modulus (CEM) to monitor the degree of anticoagulation in ESRD subjects, and to compare these results to aFXa activity, the current gold-standard monitoring parameter. Methods:,Eight healthy volunteers without renal dysfunction and eight ESRD subjects were enrolled into this study. Subjects received a single dose of enoxaparin 1 mg kg,1 subcutaneously, and blood samples were obtained for the determination of aFXa activity, TGT, PCF and CEM at baseline, 4, 8, and 12 h postdose. Results:,Baseline, 4, 8, and 12-h aFXa activity concentrations were not different between groups. However, the corresponding TGT at 8 and 12 h was significantly prolonged in the ESRD group (P = 0.04, and P = 0.008, respectively). The 4-h peak TGT trended toward significance (P = 0.06). There were no differences in PCF or CEM across time. Conclusions:,These data suggest that the parameter aFXa activity is a poor predictor of the anticoagulant effect of enoxaparin in patients with ESRD. Thrombin generation time appears to be more sensitive to the antithrombotic effects of enoxaparin in this population. Further large-scale trials are needed to corroborate these data. [source]

Clinical Value of the Tissue Doppler S Wave to Characterize Left Ventricular Hypertrophy as Defined by Echocardiography

ECHOCARDIOGRAPHY, Issue 4 2010
Demian Chejtman M.D.
Left ventricular hypertrophy (LVH) may be a physiological finding and may also be associated with different disease entities and hence, with different outcomes. Regional myocardial function can be assessed with color Doppler tissue imaging, specifically by the waveform of the isovolumic contraction (IC) period and the regional systolic wave ("s"). Methods and Results: We studied five groups (G): healthy, sedentary young volunteers (G1, n:10); healthy sedentary adult volunteers (G2, n:8); and subjects with LVH (left ventricular mass index >125 g/m2) including: high performance athletes (G3, n:21), subjects with hypertension (G4, n:21), subjects with hypertrophic cardiomyopathy (HCM) (G5, n:18). We measured peak "s" wave velocity (cm/sec) at the basal and mid septum, the IC/s ratio, and basal to mid-septal velocity difference (BMVD) of the "s" wave. Regional "s" wave values (cm/sec) were G1 = 5.6 ± 1; G2 = 5.4 ± 0.8; G3 = 5.7 ± 0.6; G4 = 5.3 ± 1.1; G5 = 4.2 ± 1.1 (P < 0.0001). The IC/s ratio was G1 = 0.28 ± 0.18; G2 = 0.39 ± 0.21; G3 = 0.23 ± 0.10; G4 = 0.42 ± 0.15; G5 = 0.64 ± 0.15 (P < 0.0001). The BMVD (cm/sec) was G1 = 2 ± 0.51; G2 = 1.71 ± 0.29; G3 = 1.78 ± 0.44; G4 = 1.26 ± 0.96; G5 = 0.45 ± 0.4 (P < 0.0001). IC/s < 0.38 discriminated physiological from pathological forms of hypertrophy (sensitivity 90%; specificity 88%). Peak "s" wave velocity discriminated HCM from other causes of hypertrophy, with a cutoff value of 4.46 cm/sec (sensitivity 72%; specificity 90%). BMVD <0.98 cm/sec detected HCM with 89% sensitivity and 86% specificity. Conclusions: Peak "s" wave velocity and two indices: IC/s and BMDV are novel parameters that may allow to discriminate physiological from pathological forms of hypertrophy as well as different subtypes of hypertrophy. (ECHOCARDIOGRAPHY 2010;27:370-377) [source]

Machine learning approaches for prediction of linear B-cell epitopes on proteins

JOURNAL OF MOLECULAR RECOGNITION, Issue 3 2006
Johannes Söllner
Abstract Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Snail, Slug, and Smad-interacting protein 1 as novel parameters of disease aggressiveness in metastatic ovarian and breast carcinoma,,

CANCER, Issue 8 2005
Sivan Elloul M.Sc.
Abstract BACKGROUND It was demonstrated previously that the Snail family of transcription factors and Smad-interacting protein 1 (Sip1) regulate E-cadherin and matrix metalloproteinase 2 (MMP-2) expression, cellular morphology, and invasion in carcinoma. For the current study, the authors analyzed the relation between the expression of Snail, Slug, and Sip1; the expression of MMP-2 and E-cadherin; and clinical parameters in patients with metastatic ovarian and breast carcinoma. METHODS One hundred one fresh-frozen, malignant effusions from patients who were diagnosed with gynecologic carcinomas (78 ovarian carcinomas and 23 breast carcinomas) were studied for mRNA expression of Snail, Slug, Sip1, MMP-2, and E-cadherin using reverse transcriptase-polymerase chain reaction analysis. Snail mRNA and E-cadherin protein expression levels also were studied in ovarian carcinoma effusions using in situ hybridization and immunocytochemistry. The results were analyzed for possible correlation with clinicopathologic parameters in both tumor types. RESULTS E-cadherin mRNA expression was lower in breast carcinoma (P = 0.001), whereas Snail expression was higher (P = 0.003). The Snail/E-cadherin ratio (P < 0.001) and the Sip1/E-cadherin ratio (P = 0.002) were higher in breast carcinomas. Sip1 mRNA expression (P < 0.001) and Slug mRNA expression (P < 0.001) were correlated with the expression of MMP-2 in ovarian carcinomas. The Sip1/E-cadherin ratio was higher in primary ovarian carcinomas at the time of diagnosis compared with postchemotherapy ovarian carcinoma effusions (P = 0.003), higher in Stage IV tumors compared with Stage III tumors (P = 0.049), and higher in pleural effusions compared with peritoneal effusions (P = 0.044). In a univariate survival analysis of patients with ovarian carcinoma, a high Sip1/E-cadherin ratio predicted poor overall survival (P = 0.018). High E-cadherin mRNA expression predicted better disease-free survival (P = 0.023), with a similar trend for a low Slug/E-cadherin ratio (P = 0.07). High Snail mRNA expression predicted shorter effusion-free survival (P = 0.008), disease-free survival (P = 0.03), and overall survival (P = 0.008) in patients with breast carcinoma. CONCLUSIONS Transcription factors that regulate E-cadherin were expressed differentially in metastatic ovarian and breast carcinoma. Snail may predict a poor outcome in patients who have breast carcinoma metastatic to effusions. E-cadherin expression generally was conserved in effusions from patients with ovarian carcinoma, but the subset of patients with postulated Sip1-induced repression of this adhesion molecule had a significantly worse outcome. This finding was in agreement with the stronger suppression of E-cadherin by Snail and Sip1 in breast carcinoma effusions, a clinical condition associated with extremely poor survival. Cancer 2005. © 2005 American Cancer Society. [source]

Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT

ACTA OPHTHALMOLOGICA, Issue 1 2010
Dimitrios Bizios
Abstract. Purpose:, To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters. Methods:, We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated. Results:, There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966,0.999) and SVM (0.989, 95% CI: 0.979,1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p , 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters. Conclusion:, No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis. [source]