Liver fat quantification by dual-echo MR imaging outperforms traditional histopathological analysis

The aim of this study was to evaluate the accuracy of dual-echo (DE) magnetic resonance imaging (MRI) with and without fat and water separation for the quantification of liver fat content (LFC) in vitro and in patients undergoing liver surgery, with comparison to histopathologic analysis.

Computerized tomographic morphometric analysis of the cervical spine

Detailed knowledge of cervical canal and transverse foramens' morphometry is critical for understanding the pathology of certain diseases and for proper preoperative planning. Lateral x-rays do not provide the necessary accuracy. A retrospective morphometric study of the cervical canal was performed at the authors' institution to measure mean dimensions of sagittal canal diameter (SCD), right and left transverse foramens' sagittal (SFD) and transverse (TFD) diameters and minimum distance between spinal canal and transverse foramens (dSC-TF) for each level of the cervical spine from C1-C7, using computerized tomographic scans, in 100 patients from the archives of the Emergency Room.

Structural Analysis of alpha-Fetoprotein (AFP)-like Peptides with Anti-Breast-Cancer Properties

The abundance of alpha-fetoprotein (AFP), a natural protein produced by the fetal yolk sac during pregnancy, correlates with lower incidence of estrogen receptor positive (ER+) breast cancer. The pharmacophore region of AFP has been narrowed down to a four amino acid (AA) region in the third domain of the 591 AA peptide. Our computational study focuses on a 4-mer segment consisting of the amino acids threonine-proline-valine-asparagine (TPVN). We have run replica exchange molecular dynamics (REMD) simulations and used 120 configurational snapshots from the total trajectory as starting configurations for quantum chemical calculations. We optimized structures using semiempirical (PM3, PM6, PM6-D2, PM6-H2, PM6-DH+, PM6-DH2) and density functional methods (TPSS, PBE0, M06-2X). By comparing the accuracy of these methods against RI-MP2 benchmarks, we devised a protocol for calculating the lowest energy conformers of these peptides accurately and efficiently. This protocol screens out high-energy conformers using lower levels of theory and outlines a general method for predicting small peptide structures.

The Modified Direct Analysis Method: an Extension of the Direct Analysis Method

The purpose of this research project is to study an innovative method for the stability assessment of structural steel systems, namely the Modified Direct Analysis Method (MDM). This method is intended to simplify an existing design method, the Direct Analysis Method (DM), by assuming a sophisticated second-order elastic structural analysis will be employed that can account for member and system instability, and thereby allow the design process to be reduced to confirming the capacity of member cross-sections. This last check can be easily completed by substituting an effective length of KL = 0 into existing member design equations. This simplification will be particularly useful for structural systems in which it is not clear how to define the member slenderness L/r when the laterally unbraced length L is not apparent, such as arches and the compression chord of an unbraced truss. To study the feasibility and accuracy of this new method, a set of 12 benchmark steel structural systems previously designed and analyzed by former Bucknell graduate student Jose Martinez-Garcia and a single column were modeled and analyzed using the nonlinear structural analysis software MASTAN2. A series of Matlab-based programs were prepared by the author to provide the code checking requirements for investigating the MDM. By comparing MDM and DM results against the more advanced distributed plasticity analysis results, it is concluded that the stability of structural systems can be adequately assessed in most cases using MDM, and that MDM often appears to be a more accurate but less conservative method in assessing stability.

Accuracy of Recognition and Diagnosis of Comorbid Depression in the Nursing Home

A variety of research has documented high levels of depression among older adults in the health care setting. Additional research has shown that care providers in health care settings are not very effective at diagnosing comorbid depression.This is a troublesome finding since comorbid depression has been linked to a number of negative outcomes in older adults. Early results have indicated that comorbid depression may be associated with a number of unfavorable consequences ranging from impairments in physical functioning to increased mortality.The health care setting with arguably the highest rate of physical impairment is the nursing home and it is the nursing home where the effects of comorbid depression may be most costly. Therefore, the current analysis uses data from the Institutional Population Component of the NationalMedical Expenditure Survey (US Department of Health and Human Services, 1990) to explore rates of both recognized and unrecognized comorbid depression in the nursing home setting. Using a constructed proxy variable representative of the DSM-III-R diagnosis of depression, results indicate that approximately 8.1% of nursing home residents have an unrecognized potential comorbid depression.

Periodic breathing during ascent to extreme altitude quantified by spectral analysis of the respiratory volume signal

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1(st) and 2(nd) ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO(2) and periodic breathing cycles significantly increased with acclimatization (p-value < 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO(2), through a significant negative correlation (p-value < 0.01). Higher Pm is observed at climbing periods visually labeled as PB with > 5 periodic breathing cycles through a significant positive correlation (p-value < 0.01). Our data demonstrate that quantification of the respiratory volume signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

Accuracy of B-type natriuretic peptide tests to exclude congestive heart failure: systematic review of test accuracy studies

BACKGROUND: Congestive heart failure (CHF) is a major public health problem. The use of B-type natriuretic peptide (BNP) tests shows promising diagnostic accuracy. Herein, we summarize the evidence on the accuracy of BNP tests in the diagnosis of CHF and compare the performance of rapid enzyme-linked immunosorbent assay (ELISA) and standard radioimmunosorbent assay (RIA) tests. METHODS: We searched electronic databases and the reference lists of included studies, and we contacted experts. Data were extracted on the study population, the type of test used, and methods. Receiver operating characteristic (ROC) plots and summary ROC curves were produced and negative likelihood ratios pooled. Random-effect meta-analysis and metaregression were used to combine data and explore sources of between-study heterogeneity. RESULTS: Nineteen studies describing 22 patient populations (9 ELISA and 13 RIA) and 9093 patients were included. The diagnosis of CHF was verified by echocardiography, radionuclide scan, or echocardiography combined with clinical criteria. The pooled negative likelihood ratio overall from random-effect meta-analysis was 0.18 (95% confidence interval [CI], 0.13-0.23). It was lower for the ELISA test (0.12; 95% CI, 0.09-0.16) than for the RIA test (0.23; 95% CI, 0.16-0.32). For a pretest probability of 20%, which is typical for patients with suspected CHF in primary care, a negative result of the ELISA test would produce a posttest probability of 2.9%; a negative RIA test, a posttest probability of 5.4%. CONCLUSIONS: The use of BNP tests to rule out CHF in primary care settings could reduce demand for echocardiography. The advantages of rapid ELISA tests need to be balanced against their higher cost.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.

Relative sensitivity factors of inorganic cations in frozen-hydrated standards in secondary ion MS analysis

We describe the measurement, at 100 K, of the SIMS relative sensitivity factors (RSFs) of the main physiological cations Na+, K+, Mg2+, and Ca2+ in frozen-hydrated (F-H) ionic solutions. Freezing was performed by either plunge freezing or high-pressure freezing. We also report the measurement of the RSFs in flax fibers, which are a model for ions in the plant cell wall, and in F-H ionic samples, which are a model for ions in the vacuole. RSFs were determined under bombardment with neutral oxygen (FAB) for both the fibers and the F-H samples. We show that referencing to ice-characteristic secondary ions is of little value in determining RSFs and that referencing to K is preferable. The RSFs of Na relative to K and of Ca relative to Mg in F-H samples are similar to their respective values in fiber samples, whereas the RSFs of both Ca and Mg relative to K are lower in fibers than in F-H samples. Our data show that the physical factors important for the determination of the RSFs are not the same in F-H samples and in homogeneous matrixes. Our data show that it is possible to perform a SIMS relative quantification of the cations in frozen-hydrated samples with an accuracy on the order of 15%. Referencing to K permits the quantification of the ionic ratios, even when the absolute concentration of the referencing ion is unknown. This is essential for physiological studies of F-H biological samples.

Electron energy-loss spectroscopy as a tool for elemental analysis in biological speciments.

A transmission electron microscope (TEM) accessory, the energy filter, enables the establishment of a method for elemental microanalysis, the electron energy-loss spectroscopy (EELS). In conventional TEM, unscattered, elastic, and inelastic scattered electrons contribute to image information. Energy-filtering TEM (EFTEM) allows elemental analysis at the ultrastructural level by using selected inelastic scattered electrons. EELS is an excellent method for elemental microanalysis and nanoanalysis with good sensitivity and accuracy. However, it is a complex method whose potential is seldom completely exploited, especially for biological specimens. In addition to spectral analysis, parallel-EELS, we present two different imaging techniques in this chapter, namely electron spectroscopic imaging (ESI) and image-EELS. We aim to introduce these techniques in this chapter with the elemental microanalysis of titanium. Ultrafine, 22-nm titanium dioxide particles are used in an inhalation study in rats to investigate the distribution of nanoparticles in lung tissue.

Accuracy of Preoperative Endoscopic Ultrasound (EUS) to Distinguish Between Early and Locally Advanced Esophageal Cancer in Daily Clinical Practice

Introduction: Preoperative chemoradiotherapy is generally recommended for locally advanced esophageal cancer (clinical stage T3 or T4 or nodal positive disease) but not for early cancer (clinical stage T0 to T2, N0). EUS has been described as the most accurate method to distinguish between early and locally advanced stage in several studies. Recently however, the high accuracy of EUS (90% or higher) was questioned by some investigators. This raises the issue whether the results of studies focused on EUS accuracy may be directly translated into daily clinical practice. Aim & Methods: The aim of this retrospective analysis was to assess the accuracy of preoperative EUS to distinguish between early and locally advanced esophageal cancer in daily clinical practice outside a study setting. EUS was performed by several investigators, including trainees in one university hospital. For this purpose, EUS reports and patient files (medical and surgical) including histological reports of 300 consecutive pts with esophageal tumors were reviewed. In pts with adenocarcinoma or squamous cell cancer and surgical resection without previous radio-/chemotherapy, EUS tumor staging was compared with histological diagnosis. Results: Out of the 300 consecutive pts with esophageal tumor and EUS 102 pts had esophageal surgery after EUS-staging without any radio-/chemotherapy. In 93 pts oesophageal cancer was confirmed, whereas 9 had other tumors. The mean age was 65 years (range 27-89), sex ratio female:male was 1:3.2. To distinguish between early and late tumor stage, the accuracy was 85%. The sensitivity and specificity for early cancer was 59%, and 93%, respectively. The diagnostic accuracy for local tumor spread was 90%, 90%, 68%, 69%, 89% for pT0, pT1, pT2, pT3 and pT4 lesions, respectively. The overall accuracy for T-stage was 74%. For pN-positive staging the accuracy of EUS was 73%. Conclusion: In daily clinical practice, the accuracy of EUS in assessing esophageal tumor staging is lower than in specific studies focusing on EUS accuracy. Mainly early esophageal cancer stages were overstaged. Thus, the implementation of recommendations for diagnostic work-up of esophageal cancer patients resulting from highly specific studies should consider the appropriate clinical setting.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.

Model Checking for ROC Regression Analysis

The Receiver Operating Characteristic (ROC) curve is a prominent tool for characterizing the accuracy of continuous diagnostic test. To account for factors that might invluence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date practical model checking techniques suitable for validating existing ROC regression models are not yet available. In this paper, we develop cumulative residual based procedures to graphically and numerically assess the goodness-of-fit for some commonly used ROC regression models, and show how specific components of these models can be examined within this framework. We derive asymptotic null distributions for the residual process and discuss resampling procedures to approximate these distributions in practice. We illustrate our methods with a dataset from the Cystic Fibrosis registry.

Survival Analysis with Large Dimensional Covariates: An Application in Microarray Studies

Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time (AFT) model. Assessment of the two methods is conducted through simulation studies and through analysis of microarray data obtained from a set of patients with diffuse large B-cell lymphoma where time to survival is of interest. The approaches are shown to match or exceed the predictive performance of a Cox-based and an AFT-based variable selection method. The methods are moreover shown to be much more computationally efficient than their respective Cox- and AFT- based counterparts.