Quantitative EEG in early Alzheimer's disease patients - power spectrum and complexity features.

The goal of this study was to investigate the EEG signs of early stage Alzheimer's disease (AD) by conventional analyses and by methods quantifying linear and nonlinear EEG-complexity. The EEG was recorded in 12 mild AD patients and in an age-matched healthy control group (24 subjects) in both eyes open and eyes closed conditions. Frequency spectra, Omega-complexity and Synchronization likelihood were calculated on the data. In the patients a significant decrease of the relative alpha and increase of the theta power were found. Remarkably increased Omega-complexity and lower Synchronization likelihood were observed in AD in the 0.5-25 Hz frequency ranges. It is concluded that both spectral- and EEG-complexity changes can be found already in the early stage of AD in a wide frequency range. Application of conventional EEG analysis methods in combination with quantification of EEG-complexity may improve the chances of early diagnosis of AD.     

Quantification of plasma lipids and apolipoproteins by use of proton NMR spectroscopy, multivariate and neural network analysis

New approaches for quantification of human blood plasma lipids and apolipoproteins are presented. One method is based on multivariate analysis of proton nuclear magnetic resonance spectra of human blood plasma. Although similar approaches have been developed previously, this is the first time principal component analysis (PCA) and partial least squares regression (PLS) have been applied to this particular task. Further, a large proportion of the subjects in this study were cancer patients undergoing treatment, which introduced a new dimension to the quantification of lipoprotein distributions. Calibration models for prediction of lipids and apolipoproteins were constructed by use of PLS, and blind samples were used to test the predictive ability. Comparison of the predicted vs observed data obtained by standard clinical chemical procedures gave good agreement; the correlation coefficient for total plasma triglyceride was 0.99, for total plasma cholesterol 0.98, for LDL cholesterol 0. 97, and for HDL cholesterol 0.88. These results are comparable with those obtained with other methods. The quantitative analysis of 14 components (including total cholesterol and total triglyceride) of human blood plasma was also undertaken using various neural network (NN) analyses of selected portions of the spectra. Conventional fully connected backpropagation neural network topologies were capable of providing excellent predictions for the majority of the variables, confirming and reinforcing literature related to this approach. However HDL triglycerides were poorly predicted, while intermediate-quality results were obtained for the LDL cholesterol, plasma apoA1 and LDL apoB variables. In these instances, applying significantly different neural network algorithms involving either general regression or polynomial neural networks in combination with genetic adaptive components for parameter optimisation made improved predictions.     

Quantitative CT: technique dependence of volume estimation on pulmonary nodules

Current estimation of lung nodule size typically relies on uni- or bi-dimensional techniques. While new three-dimensional volume estimation techniques using MDCT have improved size estimation of nodules with irregular shapes, the effect of acquisition and reconstruction parameters on accuracy (bias) and precision (variance) of the new techniques has not been fully investigated. To characterize the volume estimation performance dependence on these parameters, an anthropomorphic chest phantom containing synthetic nodules was scanned and reconstructed with protocols across various acquisition and reconstruction parameters. Nodule volumes were estimated by a clinical lung analysis software package, LungVCAR. Precision and accuracy of the volume assessment were calculated across the nodules and compared between protocols via a generalized estimating equation analysis. Results showed that the precision and accuracy of nodule volume quantifications were dependent on slice thickness, with different dependences for different nodule characteristics. Other parameters including kVp, pitch, and reconstruction kernel had lower impact. Determining these technique dependences enables better volume quantification via protocol optimization and highlights the importance of consistent imaging parameters in sequential examinations.     

Quantification of endometriotic lesions in a murine model by fluorimetric and morphometric analyses

BACKGROUND: In animal models of endometriosis, the identification and quantification of lesions originating from human endometrium is often hampered by the small size of the implants and their embedding in murine tissue. The purpose of the present study was to develop two new methods of quantifying endometriosis-like lesions in a nude mouse model: fluorimetry and morphometry. METHODS: Human menstrual endometrium was labelled using a fluorescent tracker, carboxyfluorescein diacetate, succinimidyl ester (CFDA-SE), and transplanted into the pelvic cavity of mice by injection through the peritoneum after performing a cutaneous incision. After 5 days, lesions were recovered by laparotomy. The fluorescence of the recovered endometriotic lesions was measured. Endometrial stroma and glands were immunostained in lesion sections with anti-CD10 and anti-CK22 antibodies, and their surface area was evaluated by morphometric analysis. RESULTS: Fluorescent labelling allows identification of lesions not visible macroscopically. A good correlation was observed between fluorimetry and morphometry (r=0.88) applied for lesion quantification. CONCLUSIONS: Fluorimetric evaluation combined with morphometric analysis of endometriosis-like lesions allows objective and reliable recording of endometriosis development in a nude mouse model. This quantification method could therefore be useful for future pharmacological and toxicological studies.     

Tandem mass spectrometric quantification of 8-iso-prostaglandin F2alpha and its metabolite 2,3-dinor-5,6-dihydro-8-iso-prostaglandin F2alpha in human urine

Whole body synthesis of F2-isoprostanes, a family of cyclooxygenase-independent eicosanoids formed by free-radical catalysed peroxidation, should be best assessed by quantifying their urinary metabolites. Two methods for the quantitative determination of F2-isoprostane metabolites in human urine performing either thin-layer chromatography (TLC) (method A) or high-performance liquid chromatography (HPLC) (method B) prior to GC-tandem MS are described. Method A allows for simultaneous quantification of 8-iso-PGF2alpha, one prominent member of the F2-isoprostane family, and its major urinary metabolite, 2,3-dinor-5,6-dihydro-8-iso-PGF2alpha. Mean excretion was found to be 223 and 506 pg/mg creatinine of 8-iso-PGF2alpha and 2,3-dinor-5,6-dihydro-8-iso-PGF2alpha, respectively (n=14). A tight correlation existed between the urinary excretion of these two isoprostanes (r=0.86). Method B enables quantification of dinor-dihydro metabolites of various F2-isoprostanes including 8-iso-PGF2alpha. 2,3-Dinor-5,6-dihydro-8-iso-PGF2alpha was found to be an abundant dinor-dihydro F2-isoprostane metabolite. Validity of method A was proven by a combination of HPLC with TLC prior to GC-tandem MS analysis. A correlation was observed between the urinary concentrations of 2,3-dinor-5,6-dihydro-8-iso-PGF2alpha measured by GC-MS and GC-tandem MS (r=0.84).     

Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection

Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilayer perceptron). Two technical approaches are proposed for the transformation of the belief network into an informative prior. The first one consists in generating samples according to the most probable parameterization of the Bayesian belief network and using them as virtual data together with the real data in the Bayesian learning of a multilayer perceptron. The second approach consists in transforming probability distributions over belief network parameters into distributions over multilayer perceptron parameters. The essential attribute of the hybrid methodology is that it combines prior knowledge and statistical data efficiently when prior knowledge is available and the sample is of small or medium size. Additionally, we describe how the Bayesian approach can provide uncertainty information about the predictions (e.g. for classification with rejection). We demonstrate these techniques on the medical task of predicting the malignancy of ovarian masses and summarize the practical advantages of the Bayesian approach. We compare the learning curves for the hybrid methodology with those of several belief networks and multilayer perceptrons. Furthermore, we report the performance of Bayesian belief networks when they are allowed to exclude hard cases based on various measures of prediction uncertainty.     

一种基于多层感知器的房颤心电图检测方法

目的:提出一种基于多层感知器（MLP）的新型房颤识别算法。方法:首先设计一种新型自适应的R波阈值检测算法,然后以R波位置和幅度为特征,MLP为分类器进行正常/房颤心电图识别。MLP的网络参数采用深层置信网络预训练算法进行初始化,最后用误差反向传播算法对MLP网络权重进行调整。结果:在单通道心电图数据集上对正常、房颤心电信号进行分类,本研究方法的灵敏度达96.00%,特异性为84.18%,平均识别率为90.09%。结论:这种基于MLP的心电识别算法准确率高、计算复杂度较低,可为房颤的智能诊断提供一种新方法。     

Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test–retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.     

Densitometry test of bone tissue: validation of computer simulation studies

Bone densitometry measurements are performed to predict the fracture risk in bones. However, the sensitivity of these predictions are not satisfactory. One of the explanations is that densitometry ignores the (architectural) structural aspects of the bone. The effects of varying architectural parameters on the densitometry parameters can be effectively assessed by considering a 3-D image of a bone and vary the bone structure parameters in a controlled manner and determine the consequence of these changes on a simulated (virtual) densitometry analysis. In this paper we present such a computer simulation of densitometry analysis of bone. The simulation allows quantification of densitometry parameters, such as BMD and BMC, on the basis of computed tomography bone scans. The aim of the presented study is the evaluation of our method by comparing its results to the results from real densitometry (DEXA) tests. For the evaluation we selected three femoral bones. These items were CT scanned and individual computer models were created. In addition, the densitometry parameters of these items were assessed by a clinical DEXA scanner. The densitometry parameters obtained from the simulations were very close to the results from the DEXA densitometry measurements. We therefore conclude that our method can be employed in the research on the influence of changes in bone structure on densitometry test results.     

Qualitative and quantitative determination of sesquiterpenoids in Achillea species by reversed-phase high-performance liquid chromatography, mass-spectrometry and thin-layer chromatography.

A reversed-phase high-performance liquid chromatographic method was developed as a universal analysis system in order to determine and quantify antiphlogistic sesquiterpenoids in different Achillea species. Identification was performed by HPLC and diode array detection as well as by monitoring the HPLC fractions by TLC and MS. Using santonin as internal standard, HPLC separations were achieved with a methanol-water gradient system using RP 8 LiChrospher 100 (5 microm) as stationary phase. For validation, sample analyses were performed, using the two tetraploid species A. collina and A. pratensis. The method allows the identification and quantification of the main compounds achillicin, 8alpha-tigloxy-artabsin, 8alpha-angeloxy-artabsin, arglanin and santamarin with variation coefficients between 3.4 and 4.7% (total content) using santonin as internal standard. For the different compounds recovery was found between 81 and 107% performing multiple analyses of A. collina and A. pratensis.     

Evaluation of Three Pneumothorax Size Quantification Methods on Digitized Chest X-ray Films Using Medical-Grade Grayscale and Consumer-Grade Color Displays

This study focused on the effects of pneumothorax size quantification in digital radiology environments when a quantification method is selected according to the radiologist’s criteria. The objective of this study was to assess the effects of factors, including the radiologist (with different experience), displays (medical-grade and consumer-grade displays), or display calibration, on the Rhea, Collins, and Light quantification methods. This study used a factorial design with 76 cases, including 16 pneumothorax cases observed by six radiologists on three displays with and without the DICOM standard calibration. The gold standard was established by two radiologists by using computed tomography. Analysis of variance (ANOVA) was performed on the pneumothorax sizes. For the three quantifications methods, none of the evaluated factors were significant. We conclude that radiologists, displays, and calibration do not significantly affect the quantification of pneumothorax size in different digital radiology environments.     

Haemodialysis patients' beliefs about renal failure and its treatment

Patients' beliefs about illness are important because they influence adherence and adjustment, but they are often surprising and idiosyncratic. Qualitative research can identify them in ways that are not shaped by psychological theory, but quantification is necessary if clinicians are to be informed about the beliefs that are likely to be prevalent in their patients. Qualitative analysis of interviews with 16 haemodialysis (HD) patients identified beliefs about end-stage renal failure (ESRF) and its treatment that were formed into a questionnaire, completed by 156 similar patients. Patients attributed ESRF to diverse factors including lack of self-care and inadequate medical care. Patients lacked a clear belief in the mechanism of action of dietary control, and its necessity was not readily acknowledged. The common view of haemodialysis as 'cleansing' extended to the reassuring belief that it would purge the body of disallowed food or drink. Many patients regarded haemodialysis and dietary control as externally imposed challenges that dominated life. The findings identify potential targets for educational intervention to improve adherence and adjustment and predictions about effects of patients' beliefs that can be tested in future prospective studies.     

Better quantification of neonatal respiratory sinus arrhythmia—progress by modelling and model-related physiological examinations

A large interindividual variability of parameters quantifying respiratory sinus arrhythmia were found in a well defined group of healthy neonates during constant conditions of examination. Reasons for this can be found by means of a mathematical model which is based on physiological data. The results indicate sharp inconsistencies in the transfer function between respiratory movements and resulting respiratory sinus arrhythmia and are dependent on a relative frequency unit f_N (ratio of mean respiration rate to mean heart rate). The values of the coherence (as a function of this relative frequency unit) between respiratory movements and heart rate are also distinguished by a systematic decrease from lower to higher f_N values (f_N=0·1–0·5) and inconsistencies in modelling as well as in physiological examinations. The reasons for both effects can be demonstrated. The study is the basis of a new qualitative step of quantification of respiratory sinus arrhythmia in neonates by power spectral and coherence analyses.     

A sensitivity analysis of a personalized pulse wave propagation model for arteriovenous fistula surgery. Part A: Identification of most influential model parameters

Previously, a pulse wave propagation model was developed that has potential in supporting decision-making in arteriovenous fistula (AVF) surgery for hemodialysis. To adapt the wave propagation model to personalized conditions, patient-specific input parameters should be available. In clinics, the number of measurable input parameters is limited which results in sparse datasets. In addition, patient data are compromised with uncertainty. These uncertain and incomplete input datasets will result in model output uncertainties. By means of a sensitivity analysis the propagation of input uncertainties into output uncertainty can be studied which can give directions for input measurement improvement. In this study, a computational framework has been developed to perform such a sensitivity analysis with a variance-based method and Monte Carlo simulations. The framework was used to determine the influential parameters of our pulse wave propagation model applied to AVF surgery, with respect to parameter prioritization and parameter fixing. With this we were able to determine the model parameters that have the largest influence on the predicted mean brachial flow and systolic radial artery pressure after AVF surgery. Of all 73 parameters 51 could be fixed within their measurement uncertainty interval without significantly influencing the output, while 16 parameters importantly influence the output uncertainty. Measurement accuracy improvement should thus focus on these 16 influential parameters. The most rewarding are measurement improvements of the following parameters: the mean aortic flow, the aortic windkessel resistance, the parameters associated with the smallest arterial or venous diameters of the AVF in- and outflow tract and the radial artery windkessel compliance.     

Towards optimum chest compression performance during constant peak displacement cardiopulmonary resuscitation

The aim of this study is to determine the conditions necessary to achieve optimum chest compression (CC) performance during constant peak displacement cardiopulmonary resuscitation (CPR). This was accomplished by first performing a sensitivity analysis on a theoretical constant peak displacement CPR CC model to identify the parameters with the highest sensitivity. Next, the most sensitive parameters were then optimized for net sternum-to-spine compression depth, using a two-variable non-linear least squares method. The theoretical CC model was found to be most sensitive to: thoracic stiffness, maximum sternal displacement, CC rate, and back support stiffness. Based on a two-variable, non-linear least squares analysis to optimize the model for the net sternum-to-spine compression depth during constant peak displacement CPR, it was found that the optimum ranges for the CC rate and back support stiffness are between 40–120 cpm and 241.0–1198.5 Ncm⁻¹, respectively. Clinically, this suggests that current ERC guidelines for the CC rate during peak displacement CPR are appropriate; however, practitioners should be aware that the stiffness of the back support surfaces found in many hospitals may be sub-optimal and should consider using a backboard or a concrete floor to enhance CPR effectiveness.