Missed orthopaedic injuries in adult trauma patients at a major trauma centre

Abstract Introduction: Despite advances in trauma care, missed injury remains a significant cause of morbidity and mortality in trauma worldwide. In England, few have published their missed injury rates and there are no recent data for London. In 2010 London trauma networks were restructured and the impact on missed injury rates is not known. This study aimed to determine the incidence of missed orthopaedic injury for adult trauma patients at St George's Hospital, London, and to analyse missed injuries and comment on risk factors. Method: Trauma patients were recorded prospectively at the daily trauma meeting from July to September 2012. The researcher attended clinical activities and reviewed the patient notes and radiology reports daily whilst each patient was an inpatient until discharge. Missed injuries were defined as fractures or dislocations discovered more than 12 h after arrival in the emergency department. The notes for missed injury patients were reviewed again at six months. Missed injury details were recorded/analysed. Results: Three hundred and forty three adult trauma patients were referred to trauma and orthopaedics in the threemonth study period; 5 (1.5%) had a missed injury and 148 (43.1%) had an ISS>15. All missed injuries occurred in these major trauma patients, giving an incidence of 5/148 (3.4%). Four were extremity injuries and one was cervical. All missed injury patients had a GCS of 15/15, were admitted outside normal working hours, were direct admissions and had wholebody CT. Conclusions: At 3.4% our missed injury incidence is comparable to those published from similar major trauma centres. This provides recent London data following the restructuring of trauma networks

Using Empirical Phase Diagrams to Understand the Role of Intramolecular Dynamics in Immunoglobulin G Stability

Understanding the relationship between protein dynamics and stability is of paramount importance to the fields of biology and pharmaceutics. Clarifying this relationship is complicated by the large amount of experimental data that must be generated and analyzed if motions that exist over the wide range of timescales are to be included. To address this issue, we propose an approach that utilizes a multidimensional vector-based empirical phase diagram (EPD) to analyze a set of dynamic results acquired across a temperature-pH perturbation plane. This approach is applied to a humanized immunoglobulin G1 (IgG1), a protein of major biological and pharmaceutical importance whose dynamic nature is linked to its multiple biological roles. Static and dynamic measurements are used to characterize the IgG and to construct both static and dynamic empirical phase diagrams. Between pH 5 and 8, a single, pH-dependent transition is observed that corresponds to thermal unfolding of the IgG. Under more acidic conditions, evidence exists for the formation of a more compact, aggregation resistant state of the immunoglobulin, known as A-form. The dynamics-based EPD presents a considerably more detailed pattern of apparent phase transitions over the temperature-pH plane. The utility and potential applications of this approach are discussed

Adaptation-Based Programming in Haskell

We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.Comment: In Proceedings DSL 2011, arXiv:1109.032

People of the British Isles: preliminary analysis of genotypes and surnames in a UK control population

There is a great deal of interest in fine scale population structure in the UK, both as a signature of historical immigration events and because of the effect population structure may have on disease association studies. Although population structure appears to have a minor impact on the current generation of genome-wide association studies, it is likely to play a significant part in the next generation of studies designed to search for rare variants. A powerful way of detecting such structure is to control and document carefully the provenance of the samples involved. Here we describe the collection of a cohort of rural UK samples (The People of the British Isles), aimed at providing a well-characterised UK control population that can be used as a resource by the research community as well as providing fine scale genetic information on the British population. So far, some 4,000 samples have been collected, the majority of which fit the criteria of coming from a rural area and having all four grandparents from approximately the same area. Analysis of the first 3,865 samples that have been geocoded indicates that 75% have a mean distance between grandparental places of birth of 37.3km, and that about 70% of grandparental places of birth can be classed as rural. Preliminary genotyping of 1,057 samples demonstrates the value of these samples for investigating fine scale population structure within the UK, and shows how this can be enhanced by the use of surnames

Deep Learning for Single-Molecule Science

Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in Machine Learning (ML), so-called Deep Learning (DL) offers an interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional Machine Learning strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the 'internal workings' of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a Convolutional Neural Network, may be used for base calling in DNA sequencing applications. We compare it with a Support Vector Machine as a more conventional ML method, and and discuss some of the strengths and weaknesses of the approach. In particular, a 'deep' neural network has many features of a 'black box', which has important implications on how we look at and interpret data

PTPN11 mosaicism causes a spectrum of pigmentary and vascular neurocutaneous disorders and predisposes to melanoma

Phakomatosis pigmentovascularis (PPV) is a diagnosis which denotes the coexistence of pigmentary and vascular birthmarks of specific types, accompanied by variable multisystem involvement including central nervous system disease, asymmetrical growth and a predisposition to malignancy. Using a tightly phenotyped group and high depth next generation sequencing of affected tissues we discover here clonal mosaic variants in gene PTPN11 encoding SHP2 phosphatase as a cause of PPV type III or spilorosea. Within an individual the same variant is found in distinct pigmentary and vascular birthmarks and is undetectable in blood. We go on to demonstrate that the same variants can cause either the specific pigmentary or vascular phenotypes alone, as well as driving melanoma development within the pigmentary lesion. Protein conformational modelling highlights that while variants lead to loss of function at the level of the phosphatase domain, resultant conformational changes promote longer ligand binding. In vitro modelling of the missense variants confirms downstream MAPK pathway overactivation, and widespread disruption of human endothelial cell angiogenesis. Importantly, PTPN11-mosaic patients theoretically risk passing on the variant to their children as the germline RASopathy Noonan syndrome with lentigines. These findings improve our understanding of the pathogenesis and biology of naevus spilus and capillary malformation syndromes, paving the way for better clinical management

Software platform virtualization in chemistry research and university teaching

<p>Abstract</p> <p>Background</p> <p>Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories.</p> <p>Results</p> <p>Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs.</p> <p>Conclusion</p> <p>Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide.</p

Loss-of-Function Mutations in the Cell-Cycle Control Gene CDKN2A Impact on Glucose Homeostasis in Humans.

At the CDKN2A/B locus, three independent signals for type 2 diabetes risk are located in a non-coding region near CDKN2A. The disease-associated alleles have been implicated in reduced β-cell function, but the underlying mechanism remains elusive. In mice, β-cell specific loss of Cdkn2a causes hyperplasia whilst overexpression leads to diabetes, highlighting CDKN2A as a candidate effector transcript. Rare CDKN2A loss-of-function mutations are a cause of familial melanoma and offer the opportunity to determine the impact of CDKN2A haploinsufficiency on glucose homeostasis in humans. To test the hypothesis that such individuals have improved β-cell function, we performed oral and intravenous glucose tolerance tests on mutation carriers and matched controls. Compared with controls, carriers displayed increased insulin secretion, impaired insulin sensitivity and reduced hepatic insulin clearance. These results are consistent with a model whereby CDKN2A-loss affects a range of different tissues, including pancreatic β-cells and liver. To test for direct effects of CDKN2A-loss on β-cell function, we performed knockdown in a human β-cell line, EndoC-bH1. This revealed increased insulin secretion independent of proliferation. Overall, we demonstrate that CDKN2A is an important regulator of glucose homeostasis in humans, thus supporting its candidacy as an effector transcript for type 2 diabetes-associated alleles in the region

Loss-of-Function Mutations in the Cell-Cycle Control Gene CDKN2A Impact on Glucose Homeostasis in Humans.

At the CDKN2A/B locus, three independent signals for type 2 diabetes risk are located in a non-coding region near CDKN2A. The disease-associated alleles have been implicated in reduced β-cell function, but the underlying mechanism remains elusive. In mice, β-cell specific loss of Cdkn2a causes hyperplasia whilst overexpression leads to diabetes, highlighting CDKN2A as a candidate effector transcript. Rare CDKN2A loss-of-function mutations are a cause of familial melanoma and offer the opportunity to determine the impact of CDKN2A haploinsufficiency on glucose homeostasis in humans. To test the hypothesis that such individuals have improved β-cell function, we performed oral and intravenous glucose tolerance tests on mutation carriers and matched controls. Compared with controls, carriers displayed increased insulin secretion, impaired insulin sensitivity and reduced hepatic insulin clearance. These results are consistent with a model whereby CDKN2A-loss affects a range of different tissues, including pancreatic β-cells and liver. To test for direct effects of CDKN2A-loss on β-cell function, we performed knockdown in a human β-cell line, EndoC-bH1. This revealed increased insulin secretion independent of proliferation. Overall, we demonstrate that CDKN2A is an important regulator of glucose homeostasis in humans, thus supporting its candidacy as an effector transcript for type 2 diabetes-associated alleles in the region

Phosphorylation of Syntaxin‐1a by casein kinase 2α (CK2α) regulates presynaptic vesicle exocytosis from the reserve pool

The t-soluble NSF-attachment protein receptor protein Syntaxin-1a (Stx-1a) is abundantly expressed at pre-synaptic terminals where it plays a critical role in the exocytosis of neurotransmitter-containing synaptic vesicles. Stx-1a is phosphorylated by Casein kinase 2α (CK2α) at Ser14, which has been proposed to regulate the interaction of Stx-1a and Munc-18 to control of synaptic vesicle priming. However, the role of CK2α in synaptic vesicle dynamics remains unclear. Here, we show that CK2α over-expression reduces evoked synaptic vesicle release. Furthermore, shRNA-mediated knockdown of CK2α in primary hippocampal neurons strongly enhanced vesicle exocytosis from the reserve pool, with no effect on the readily releasable pool of primed vesicles. In neurons in which endogenous Stx-1a was knocked down and replaced with a CK2α phosphorylation-deficient mutant, Stx-1a(D17A), vesicle exocytosis was also increased. These results reveal a previously unsuspected role of CK2α phosphorylation in specifically regulating the reserve synaptic vesicle pool, without changing the kinetics of release from the readily releasable pool