Feasibility of diffusion tensor imaging (DTI) with fibre tractography of the normal female pelvic floor

To prospectively determine the feasibility of diffusion tensor imaging (DTI) with fibre tractography as a tool for the three-dimensional (3D) visualisation of normal pelvic floor anatomy. Five young female nulliparous subjects (mean age 28 ± 3 years) underwent DTI at 3.0T. Two-dimensional diffusion-weighted axial spin-echo echo-planar (SP-EPI) pulse sequence of the pelvic floor was performed, with additional T2-TSE multiplanar sequences for anatomical reference. Fibre tractography for visualisation of predefined pelvic floor and pelvic wall muscles was performed offline by two observers, applying a consensus method. Three eigenvalues (λ1, λ2, λ3), fractional anisotropy (FA) and mean diffusivity (MD) were calculated from the fibre trajectories. In all subjects fibre tractography resulted in a satisfactory anatomical representation of the pubovisceral muscle, perineal body, anal - and urethral sphincter complex and internal obturator muscle. Mean FA values ranged from 0.23 ± 0.02 to 0.30 ± 0.04, MD values from 1.30 ± 0.08 to 1.73 ± 0.12 × 10(-)³ mm²/s. Muscular structures in the superficial layer of the pelvic floor could not be satisfactorily identified. This study demonstrates the feasibility of visualising the complex three-dimensional pelvic floor architecture using 3T-DTI with fibre tractography. DTI of the deep female pelvic floor may provide new insights into pelvic floor disorder

A comparative analysis of Patient-Reported Expanded Disability Status Scale tools.

BACKGROUND: Patient-Reported Expanded Disability Status Scale (PREDSS) tools are an attractive alternative to the Expanded Disability Status Scale (EDSS) during long term or geographically challenging studies, or in pressured clinical service environments. OBJECTIVES: Because the studies reporting these tools have used different metrics to compare the PREDSS and EDSS, we undertook an individual patient data level analysis of all available tools. METHODS: Spearman's rho and the Bland-Altman method were used to assess correlation and agreement respectively. RESULTS: A systematic search for validated PREDSS tools covering the full EDSS range identified eight such tools. Individual patient data were available for five PREDSS tools. Excellent correlation was observed between EDSS and PREDSS with all tools. A higher level of agreement was observed with increasing levels of disability. In all tools, the 95% limits of agreement were greater than the minimum EDSS difference considered to be clinically significant. However, the intra-class coefficient was greater than that reported for EDSS raters of mixed seniority. The visual functional system was identified as the most significant predictor of the PREDSS-EDSS difference. CONCLUSION: This analysis will (1) enable researchers and service providers to make an informed choice of PREDSS tool, depending on their individual requirements, and (2) facilitate improvement of current PREDSS tools.University of Southampton and National Institute of Health Research (NIHR)

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone

CHEMICAL CHANGES IN BEAN GRAINS DURING STORAGE IN CONTROLLED CONDITIONS

Banjarmasin, Borneo (Kalimantan, Indonesia) 195-GrayscaleForman Safety Negatives, Box

Multiple Deprivation, Severity and Latent Sub-Groups:Advantages of Factor Mixture Modelling for Analysing Material Deprivation

Material deprivation is represented in different forms and manifestations. Two individuals with the same deprivation score (i.e. number of deprivations), for instance, are likely to be unable to afford or access entirely or partially different sets of goods and services, while one individual may fail to purchase clothes and consumer durables and another one may lack access to healthcare and be deprived of adequate housing . As such, the number of possible patterns or combinations of multiple deprivation become increasingly complex for a higher number of indicators. Given this difficulty, there is interest in poverty research in understanding multiple deprivation, as this analysis might lead to the identification of meaningful population sub-groups that could be the subjects of specific policies. This article applies a factor mixture model (FMM) to a real dataset and discusses its conceptual and empirical advantages and disadvantages with respect to other methods that have been used in poverty research . The exercise suggests that FMM is based on more sensible assumptions (i.e. deprivation covary within each class), provides valuable information with which to understand multiple deprivation and is useful to understand severity of deprivation and the additive properties of deprivation indicators

Spider Silk Constructs Enhance Axonal Regeneration and Remyelination in Long Nerve Defects in Sheep

BACKGROUND: Surgical reapposition of peripheral nerve results in some axonal regeneration and functional recovery, but the clinical outcome in long distance nerve defects is disappointing and research continues to utilize further interventional approaches to optimize functional recovery. We describe the use of nerve constructs consisting of decellularized vein grafts filled with spider silk fibers as a guiding material to bridge a 6.0 cm tibial nerve defect in adult sheep. METHODOLOGY/PRINCIPAL FINDINGS: The nerve constructs were compared to autologous nerve grafts. Regeneration was evaluated for clinical, electrophysiological and histological outcome. Electrophysiological recordings were obtained at 6 months and 10 months post surgery in each group. Ten months later, the nerves were removed and prepared for immunostaining, electrophysiological and electron microscopy. Immunostaining for sodium channel (NaV 1.6) was used to define nodes of Ranvier on regenerated axons in combination with anti-S100 and neurofilament. Anti-S100 was used to identify Schwann cells. Axons regenerated through the constructs and were myelinated indicating migration of Schwann cells into the constructs. Nodes of Ranvier between myelin segments were observed and identified by intense sodium channel (NaV 1.6) staining on the regenerated axons. There was no significant difference in electrophysiological results between control autologous experimental and construct implantation indicating that our construct are an effective alternative to autologous nerve transplantation. CONCLUSIONS/SIGNIFICANCE: This study demonstrates that spider silk enhances Schwann cell migration, axonal regrowth and remyelination including electrophysiological recovery in a long-distance peripheral nerve gap model resulting in functional recovery. This improvement in nerve regeneration could have significant clinical implications for reconstructive nerve surgery

Visual inspection with acetic acid as a cervical cancer test: accuracy validated using latent class analysis

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to validate the accuracy of an alternative cervical cancer test – visual inspection with acetic acid (VIA) – by addressing possible imperfections in the gold standard through latent class analysis (LCA). The data were originally collected at peri-urban health clinics in Zimbabwe.</p> <p>Methods</p> <p>Conventional accuracy (sensitivity/specificity) estimates for VIA and two other screening tests using colposcopy/biopsy as the reference standard were compared to LCA estimates based on results from all four tests. For conventional analysis, negative colposcopy was accepted as a negative outcome when biopsy was not available as the reference standard. With LCA, local dependencies between tests were handled through adding direct effect parameters or additional latent classes to the model.</p> <p>Results</p> <p>Two models yielded good fit to the data, a 2-class model with two adjustments and a 3-class model with one adjustment. The definition of latent disease associated with the latter was more stringent, backed by three of the four tests. Under that model, sensitivity for VIA (abnormal+) was 0.74 compared to 0.78 with conventional analyses. Specificity was 0.639 versus 0.568, respectively. By contrast, the LCA-derived sensitivity for colposcopy/biopsy was 0.63.</p> <p>Conclusion</p> <p>VIA sensitivity and specificity with the 3-class LCA model were within the range of published data and relatively consistent with conventional analyses, thus validating the original assessment of test accuracy. LCA probably yielded more likely estimates of the true accuracy than did conventional analysis with in-country colposcopy/biopsy as the reference standard. Colpscopy with biopsy can be problematic as a study reference standard and LCA offers the possibility of obtaining estimates adjusted for referent imperfections.</p

Research methods for subgrouping low back pain

<p>Abstract</p> <p>Background</p> <p>There is considerable clinician and researcher interest in whether the outcomes for patients with low back pain, and the efficiency of the health systems that treat them, can be improved by 'subgrouping research'. Subgrouping research seeks to identify subgroups of people who have clinically important distinctions in their treatment needs or prognoses. Due to a proliferation of research methods and variability in how subgrouping results are interpreted, it is timely to open discussion regarding a conceptual framework for the research designs and statistical methods available for subgrouping studies (a method framework). The aims of this debate article are: (1) to present a method framework to inform the design and evaluation of subgrouping research in low back pain, (2) to describe method options when investigating prognostic effects or subgroup treatment effects, and (3) to discuss the strengths and limitations of research methods suitable for the hypothesis-setting phase of subgroup studies.</p> <p>Discussion</p> <p>The proposed method framework proposes six phases for studies of subgroups: studies of assessment methods, hypothesis-setting studies, hypothesis-testing studies, narrow validation studies, broad validation studies, and impact analysis studies. This framework extends and relabels a classification system previously proposed by McGinn et al (2000) as suitable for studies of clinical prediction rules. This extended classification, and its descriptive terms, explicitly anchor research findings to the type of evidence each provides. The inclusive nature of the framework invites appropriate consideration of the results of diverse research designs. Method pathways are described for studies designed to test and quantify prognostic effects or subgroup treatment effects, and examples are discussed. The proposed method framework is presented as a roadmap for conversation amongst researchers and clinicians who plan, stage and perform subgrouping research.</p> <p>Summary</p> <p>This article proposes a research method framework for studies of subgroups in low back pain. Research designs and statistical methods appropriate for sequential phases in this research are discussed, with an emphasis on those suitable for hypothesis-setting studies of subgroups of people seeking care.</p