Statistical analysis of personal radiofrequency electromagnetic field measurements with nondetects

Exposimeters are increasingly applied in bioelectromagnetic research to determine personal radiofrequency electromagnetic field (RF-EMF) exposure. The main advantages of exposimeter measurements are their convenient handling for study participants and the large amount of personal exposure data, which can be obtained for several RF-EMF sources. However, the large proportion of measurements below the detection limit is a challenge for data analysis. With the robust ROS (regression on order statistics) method, summary statistics can be calculated by fitting an assumed distribution to the observed data. We used a preliminary sample of 109 weekly exposimeter measurements from the QUALIFEX study to compare summary statistics computed by robust ROS with a naïve approach, where values below the detection limit were replaced by the value of the detection limit. For the total RF-EMF exposure, differences between the naïve approach and the robust ROS were moderate for the 90th percentile and the arithmetic mean. However, exposure contributions from minor RF-EMF sources were considerably overestimated with the naïve approach. This results in an underestimation of the exposure range in the population, which may bias the evaluation of potential exposure-response associations. We conclude from our analyses that summary statistics of exposimeter data calculated by robust ROS are more reliable and more informative than estimates based on a naïve approach. Nevertheless, estimates of source-specific medians or even lower percentiles depend on the assumed data distribution and should be considered with caution.

Microcystic macular edema: retrograde maculopathy caused by optic neuropathy

PURPOSE To investigate retrograde axonal degeneration for its potential to cause microcystic macular edema (MME), a maculopathy that has been previously described in patients with demyelinating disease. To identify risk factors for MME and to expand the anatomic knowledge on MME. DESIGN Retrospective case series. PARTICIPANTS We included 117 consecutive patients and 180 eyes with confirmed optic neuropathy of variable etiology. Patients with glaucoma were excluded. METHODS We determined age, sex, visual acuity, etiology of optic neuropathy, and the temporal and spatial characteristics of MME. Eyes with MME were compared with eyes with optic neuropathy alone and to healthy fellow eyes. With retinal layer segmentation we quantitatively measured the intraretinal anatomy. MAIN OUTCOME MEASURES Demographic data, distribution of MME in the retina, and thickness of retinal layers were analyzed. RESULTS We found MME in 16 eyes (8.8%) from 9 patients, none of whom had multiple sclerosis or neuromyelitis optica. The MME was restricted to the inner nuclear layer (INL) and had a characteristic perifoveal circular distribution. Compared with healthy controls, MME was associated with significant thinning of the ganglion cell layer and nerve fiber layer, as well as a thickening of the INL and the deeper retinal layers. Youth is a significant risk factor for MME. CONCLUSIONS Microcystic macular edema is not specific for demyelinating disease. It is a sign of optic neuropathy irrespective of its etiology. The distinctive intraretinal anatomy suggests that MME is caused by retrograde degeneration of the inner retinal layers, resulting in impaired fluid resorption in the macula.

dGEMRIC and subsequent T1 mapping of the hip at 1.5 Tesla: normative data on zonal and radial distribution in asymptomatic volunteers

To characterize the zonal distribution of three-dimensional (3D) T1 mapping in the hip joint of asymptomatic adult volunteers.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Cardiogoniometric parameters for detection of coronary artery disease at rest as a function of stenosis localization and distribution

Cardiogoniometry (CGM), a spatiotemporal electrocardiologic 5-lead method with automated analysis, may be useful in primary healthcare for detecting coronary artery disease (CAD) at rest. Our aim was to systematically develop a stenosis-specific parameter set for global CAD detection. In 793 consecutively admitted patients with presumed non-acute CAD, CGM data were collected prior to elective coronary angiography and analyzed retrospectively. 658 patients fulfilled the inclusion criteria, 405 had CAD verified by coronary angiography; the 253 patients with normal coronary angiograms served as the non-CAD controls. Study patients--matched for age, BMI, and gender--were angiographically assigned to 8 stenosis-specific CAD categories or to the controls. One CGM parameter possessing significance (P < .05) and the best diagnostic accuracy was matched to one CAD category. The area under the ROC curve was .80 (global CAD versus controls). A set containing 8 stenosis-specific CGM parameters described variability of R vectors and R-T angles, spatial position and potential distribution of R/T vectors, and ST/T segment alterations. Our parameter set systematically combines CAD categories into an algorithm that detects CAD globally. Prospective validation in clinical studies is ongoing.