Are component positioning and prosthesis size associated with hip resurfacing failure?

BACKGROUND: Recent studies suggest that there is a learning curve for metal-on-metal hip resurfacing. The purpose of this study was to assess whether implant positioning changed with surgeon experience and whether positioning and component sizing were associated with implant longevity. METHODS: We evaluated the first 361 consecutive hip resurfacings performed by a single surgeon, which had a mean follow-up of 59 months (range, 28 to 87 months). Pre and post-operative radiographs were assessed to determine the inclination of the acetabular component, as well as the sagittal and coronal femoral stem-neck angles. Changes in the precision of component placement were determined by assessing changes in the standard deviation of each measurement using variance ratio and linear regression analysis. Additionally, the cup and stem-shaft angles as well as component sizes were compared between the 31 hips that failed over the follow-up period and the surviving components to assess for any differences that might have been associated with an increased risk for failure. RESULTS: Surgeon experience was correlated with improved precision of the antero-posterior and lateral positioning of the femoral component. However, femoral and acetabular radiographic implant positioning angles were not different between the surviving hips and failures. The failures had smaller mean femoral component diameters as compared to the non-failure group (44 versus 47 millimeters). CONCLUSIONS: These results suggest that there may be differences in implant positioning in early versus late learning curve procedures, but that in the absence of recognized risk factors such as intra-operative notching of the femoral neck and cup inclination in excess of 50 degrees, component positioning does not appear to be associated with failure. Nevertheless, surgeons should exercise caution in operating patients with small femoral necks, especially when they are early in the learning curve.

Evaluation of the Does Reduction Potential Using a Breast Positioning Technique for Organ-based Tube Current Modulated CT Examinations

Purpose: The purpose of this work was to investigate the breast dose saving potential of a breast positioning technique (BP) for thoracic CT examinations with organ-based tube current modulation (OTCM).

Methods: The study included 13 female patient models (XCAT, age range: 27-65 y.o., weight range: 52 to 105.8 kg). Each model was modified to simulate three breast sizes in standard supine geometry. The modeled breasts were further deformed, emulating a BP that would constrain the breasts within 120° anterior tube current (mA) reduction zone. The tube current value of the CT examination was modeled using an attenuation-based program, which reduces the radiation dose to 20% in the anterior region with a corresponding increase to the posterior region. A validated Monte Carlo program was used to estimate organ doses with a typical clinical system (SOMATOM Definition Flash, Siemens Healthcare). The simulated organ doses and organ doses normalized by CTDIvol were compared between attenuation-based tube current modulation (ATCM), OTCM, and OTCM with BP (OTCMBP).

Results: On average, compared to ATCM, OTCM reduced the breast dose by 19.3±4.5%, whereas OTCMBP reduced breast dose by 36.6±6.9% (an additional 21.3±7.3%). The dose saving of OTCMBP was more significant for larger breasts (on average 32, 38, and 44% reduction for 0.5, 1.5, and 2.5 kg breasts, respectively). Compared to ATCM, OTCMBP also reduced thymus and heart dose by 12.1 ± 6.3% and 13.1 ± 5.4%, respectively.

Conclusions: In thoracic CT examinations, OTCM with a breast positioning technique can markedly reduce unnecessary exposure to the radiosensitive organs in the anterior chest wall, specifically breast tissue. The breast dose reduction is more notable for women with larger breasts.

Variability of particulate matter concentrations along roads and motorways determined by a moving measurement unit

The spatial variability of aerosol number and mass along roads was determined in different regions (urban, rural and coastal-marine) of the Netherlands. A condensation particle counter (CPC) and an optical aerosol spectrometer (LAS-X) were installed in a van along with a global positioning system (GPS). Concentrations were measured with high-time resolutions while driving allowing investigations not possible with stationary equipment. In particular, this approach proves to be useful to identify those locations where numbers and mass attain high levels ('hot spots'). In general, concentrations of number and mass of particulate matter increase along with the degree of urbanisation, with number concentration being the more sensitive indicator. The lowest particle numbers and PM1-concentrations are encountered in a coastal and rural area: <5000cm-3 and 6μgm-3, respectively. The presence of sea-salt material along the North-Sea coast enhances PM>1-concentrations compared to inland levels. High-particle numbers are encountered on motorways correlating with traffic intensity; the largest average number concentration is measured on the ring motorway around Amsterdam: about 160000cm-3 (traffic intensity 100000vehday-1). Peak values occur in tunnels where numbers exceed 106cm-3. Enhanced PM1 levels (i.e. larger than 9μgm-3) exist on motorways, major traffic roads and in tunnels. The concentrations of PM>1 appear rather uniformly distributed (below 6μgm-3 for most observations). On the urban scale, (large) spatial variations in concentration can be explained by varying intensities of traffic and driving patterns. The highest particle numbers are measured while being in traffic congestions or when behind a heavy diesel-driven vehicle (up to 600×103cm-3). Relatively high numbers are observed during the passages of crossings and, at a decreasing rate, on main roads with much traffic, quiet streets and residential areas with limited traffic. The number concentration exhibits a larger variability than mass: the mass concentration on city roads with much traffic is 12% higher than in a residential area at the edge of the same city while the number of particles changes by a factor of two (due to the presence of the ultrafine particles (aerodynamic diameter <100nm). It is further indicated that people residing at some 100m downwind a major traffic source are exposed to (still) 40% more particles than those living in the urban background areas. © 2004 Elsevier Ltd. All rights reserved.

"Thinking too much": A systematic review of a common idiom of distress.

Idioms of distress communicate suffering via reference to shared ethnopsychologies, and better understanding of idioms of distress can contribute to effective clinical and public health communication. This systematic review is a qualitative synthesis of "thinking too much" idioms globally, to determine their applicability and variability across cultures. We searched eight databases and retained publications if they included empirical quantitative, qualitative, or mixed-methods research regarding a "thinking too much" idiom and were in English. In total, 138 publications from 1979 to 2014 met inclusion criteria. We examined the descriptive epidemiology, phenomenology, etiology, and course of "thinking too much" idioms and compared them to psychiatric constructs. "Thinking too much" idioms typically reference ruminative, intrusive, and anxious thoughts and result in a range of perceived complications, physical and mental illnesses, or even death. These idioms appear to have variable overlap with common psychiatric constructs, including depression, anxiety, and PTSD. However, "thinking too much" idioms reflect aspects of experience, distress, and social positioning not captured by psychiatric diagnoses and often show wide within-cultural variation, in addition to between-cultural differences. Taken together, these findings suggest that "thinking too much" should not be interpreted as a gloss for psychiatric disorder nor assumed to be a unitary symptom or syndrome within a culture. We suggest five key ways in which engagement with "thinking too much" idioms can improve global mental health research and interventions: it (1) incorporates a key idiom of distress into measurement and screening to improve validity of efforts at identifying those in need of services and tracking treatment outcomes; (2) facilitates exploration of ethnopsychology in order to bolster cultural appropriateness of interventions; (3) strengthens public health communication to encourage engagement in treatment; (4) reduces stigma by enhancing understanding, promoting treatment-seeking, and avoiding unintentionally contributing to stigmatization; and (5) identifies a key locally salient treatment target.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.