Are osteoporotic fractures being adequately investigated?: A questionnaire of GP & orthopaedic surgeons

BACKGROUND: To investigate the current practice of Orthopaedic Surgeons & General Practitioners (GP) when presented with patients who have a fracture, with possible underlying Osteoporosis. METHODS: Questionnaires were sent to 140 GPs and 140 Orthopaedic Surgeons. The participants were asked their routine clinical practice with regard to investigation of underlying osteoporosis in 3 clinical scenarios. 55 year old lady with a low trauma Colles fracture 60 year old lady with a vertebral wedge fracture 70 year old lady with a low trauma neck of femur fracture. RESULTS: Most doctors agreed that patients over 50 years old with low trauma fractures required investigation for osteoporosis, however, most surgeons (56%, n = 66) would discharge patients with low trauma Colles fracture without requesting or initiating investigation for osteoporosis. Most GPs (67%, n = 76) would not investigate a similar patient for osteoporosis, unless prompted by the Orthopaedic Surgeon or patient. More surgeons (71%, n= 83) and GPs (64%, n = 72) would initiate investigations for osteoporosis in a vertebral wedge fracture, but few surgeons (35%, n = 23) would investigate a neck of femur fracture patient after orthopaedic treatment. CONCLUSION: Most doctors know that fragility fractures in patients over 50 years old require investigation for Osteoporosis; however, a large population of patients with osteoporotic fractures are not being given the advantages of secondary prevention

Recognition in Ants: Social Origin Matters

The ability of group members to discriminate against foreigners is a keystone in the evolution of sociality. In social insects, colony social structure (number of queens) is generally thought to influence abilities of resident workers to discriminate between nestmates and non-nestmates. However, whether social origin of introduced individuals has an effect on their acceptance in conspecific colonies remains poorly explored. Using egg-acceptance bioassays, we tested the influence of social origin of queen-laid eggs on their acceptance by foreign workers in the ant Formica selysi. We showed that workers from both single- and multiple-queen colonies discriminated against foreign eggs from single-queen colonies, whereas they surprisingly accepted foreign eggs from multiple-queen colonies. Chemical analyses then demonstrated that social origins of eggs and workers could be discriminated on the basis of their chemical profiles, a signal generally involved in nestmate discrimination. These findings provide the first evidence in social insects that social origins of eggs interfere with nestmate discrimination and are encoded by chemical signatures

Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images

We introduce a method for large scale reconstruction of complex bundles of neural processes from fluorescent image stacks. We imaged yellow fluorescent protein labeled axons that innervated a whole muscle, as well as dendrites in cerebral cortex, in transgenic mice, at the diffraction limit with a confocal microscope. Each image stack was digitally re-sampled along an orientation such that the majority of axons appeared in cross-section. A region growing algorithm was implemented in the open-source Reconstruct software and applied to the semi-automatic tracing of individual axons in three dimensions. The progression of region growing is constrained by user-specified criteria based on pixel values and object sizes, and the user has full control over the segmentation process. A full montage of reconstructed axons was assembled from the ∼200 individually reconstructed stacks. Average reconstruction speed is ∼0.5 mm per hour. We found an error rate in the automatic tracing mode of ∼1 error per 250 um of axonal length. We demonstrated the capacity of the program by reconstructing the connectome of motor axons in a small mouse muscle

Mesoscopic organization reveals the constraints governing C. elegans nervous system

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations, such as optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis, we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.Comment: Published version, Minor modifications, 16 pages, 9 figure

Detection and Verification of Mammalian Mirtrons by Northern Blotting

microRNAs (miRNAs) have vital roles in regulating gene expression—contributing to major diseases like cancer and heart disease. Over the last decade, thousands of miRNAs have been discovered through high throughput sequencing-based annotation. Different classes have been described, as well as a great dynamic range of expression levels. While sequencing approaches provide insight into biogenesis and allow confident identification, there is a need for additional methods for validation and characterization. Northern blotting was one of the first techniques used for studying miRNAs, and remains one of the most valuable as it avoids enzymatic manipulation of miRNA transcripts. Blotting can also provide insight into biogenesis by revealing RNA processing intermediates. Compared to sequencing, however, northern blotting is a relatively insensitive technology. This creates a challenge for detecting low expressed miRNAs, particularly those produced by inefficient, non-canonical pathways. In this chapter, we describe a strategy to study such miRNAs by northern blotting that involves ectopic expression of both miRNAs and miRNA-binding Argonaute (Ago) proteins. Through use of epitope tags, this strategy also provides a convenient method for verification of small RNA competency to be loaded into regulatory complexes

Brain Performance versus Phase Transitions

We here illustrate how a well-founded study of the brain may originate in assuming analogies with phase-transition phenomena. Analyzing to what extent a weak signal endures in noisy environments, we identify the underlying mechanisms, and it results a description of how the excitability associated to (non-equilibrium) phase changes and criticality optimizes the processing of the signal. Our setting is a network of integrate-and-fire nodes in which connections are heterogeneous with rapid time-varying intensities mimicking fatigue and potentiation. Emergence then becomes quite robust against wiring topology modification—in fact, we considered from a fully connected network to the Homo sapiens connectome—showing the essential role of synaptic flickering on computations. We also suggest how to experimentally disclose significant changes during actual brain operation.The authors acknowledge support from the Spanish Ministry of Economy and Competitiveness under the project FIS2013-43201-P

c-Met overexpression in inflammatory breast carcinomas: automated quantification on tissue microarrays

Inflammatory breast carcinoma (IBC) is a rare but aggressive tumour associated with poor outcome owing to early metastases. Increased expression of c-Met protein correlates with reduced survival and high metastatic risk in human cancers including breast carcinomas and is targetable by specific drugs, that could potentially improve the prognosis. In the present study, we compared c-Met expression in IBC (n=41) and non-IBC (n=480) immunohistochemically (Ventana Benchmark autostainer) in two tissue microarrays (TMA) along with PI3K and E-cadherin. The results were quantified through an automated image analysis device (SAMBA Technologies). We observed that (i) c-Met was significantly overexpressed in IBC as compared with non-IBC (P<0.001), (ii) PI3K was overexpressed (P<0.001) in IBC, suggesting that the overexpressed c-Met is functionally active at least through the PI3K signal transduction pathway; and (iii) E-cadherin was paradoxically also overexpressed in IBC. We concluded that overexpressed c-Met in IBC constitutes a potential target for specific therapy for the management of patients with poor-outcome tumours such as IBC. Automated image analysis of TMA proved to be a valuable tool for high-throughput immunohistochemical quantification of the expression of intratumorous protein markers

Trabecular Reorganization in Consecutive Iliac Crest Biopsies when Switching from Bisphosphonate to Strontium Ranelate Treatment

BACKGROUND: Several agents are available to treat osteoporosis while addressing patient-specific medical needs. Individuals' residual risk to severe fracture may require changes in treatment strategy. Data at osseous cellular and microstructural levels due to a therapy switch between agents with different modes of action are rare. Our study on a series of five consecutively taken bone biopsies from an osteoporotic individual over a six-year period analyzes changes in cellular characteristics, bone microstructure and mineralization caused by a therapy switch from an antiresorptive (bisphosphonate) to a dual action bone agent (strontium ranelate). METHODOLOGY/PRINCIPAL FINDINGS: Biopsies were progressively taken from the iliac crest of a female patient. Four biopsies were taken during bisphosphonate therapy and one biopsy was taken after one year of strontium ranelate (SR) treatment. Furthermore, serum bone markers and dual x-ray absorptiometry measurements were acquired. Undecalcified histology was used to assess osteoid parameters and bone turnover. Structural indices and degree of mineralization were determined using microcomputed tomography, quantitative backscattered electron imaging, and combined energy dispersive x-ray/µ-x-ray-fluorescence microanalysis. CONCLUSIONS/SIGNIFICANCE: Microstructural data revealed a notable increase in bone volume fraction after one year of SR treatment compared to the bisphosphonate treatment period. Indices of connectivity density, structure model index and trabecular bone pattern factor were predominantly enhanced indicating that the architectural transformation from trabecular rods to plates was responsible for the bone volume increase and less due to changes in trabecular thickness and number. Administration of SR following bisphosphonates led to a maintained mineralization profile with an uptake of strontium on the bone surface level. Reactivated osteoclasts designed tunneling, hook-like intratrabecular resorption sites. The appearance of tunneling resorption lacunae and the formation of both mini-modeling units and osteon-like structures within increased plate-like cancellous bone mass provides additional information on the mechanisms of strontium ranelate following bisphosphonate treatment, which may deserve special attention when monitoring a treatment switch

An iconic language for the graphical representation of medical concepts

<p>Abstract</p> <p>Background</p> <p>Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.</p> <p>Methods</p> <p>The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.</p> <p>Results</p> <p>VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, <it>p </it>= 0.003) and 1.8 times faster (<it>p </it>< 0.001).</p> <p>Conclusion</p> <p>VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.</p

A New Measure of Centrality for Brain Networks

Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network