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The UK Clinical Research Collaboration (UKCRC) Tissue Directory and Coordination Centre: the UK’s centre for facilitating the usage of human samples for medical research
The UKCRC Tissue Directory and Coordination Centre was established to improve access to and utilisation of UK human tissue samples for medical research. The key output of the Centre is the creation of the UK’s first pan-disease Tissue Directory (https://directory.biobankinguk.org/). Any researcher can search the Directory based on a series of simple key words including disease classification, age, sex, sample type, preservation details, quality indicators and datasets available. The Directory as of April 2017 contains 100 Bioresources. Researchers seeking fresh samples can also search for facilities that offer bespoke collection services. Future work of the Centre will be to explore greater standardisation of biobanking activities across the UK and to facilitate an inter-connected research infrastructure related to the use of human biosamples
Study protocol for a multicentre longitudinal mixed methods study to explore the Outcomes of ChildrEn and fAmilies in the first year after paediatric Intensive Care: the OCEANIC study.
INTRODUCTION: Annually in the UK, 20 000 children become very ill or injured and need specialist care within a paediatric intensive care unit (PICU). Most children survive. However, some children and their families may experience problems after they have left the PICU including physical, functional and/or emotional problems. It is unknown which children and families experience such problems, when these occur or what causes them. The aim of this mixed-method longitudinal cohort study is to understand the physical, functional, emotional and social impact of children surviving PICU (aged: 1 month-17 years), their parents and siblings, during the first year after a PICU admission. METHODS AND ANALYSIS: A quantitative study involving 300 child survivors of PICU; 300 parents; and 150-300 siblings will collect data (using self-completion questionnaires) at baseline, PICU discharge, 1, 3, 6 and 12 months post-PICU discharge. Questionnaires will comprise validated and reliable instruments. Demographic data, PICU admission and treatment data, health-related quality of life, functional status, strengths and difficulties behaviour and post-traumatic stress symptoms will be collected from the child. Parent and sibling data will be collected on the impact of paediatric health conditions on the family's functioning capabilities, levels of anxiety and social impact of the child's PICU admission. Data will be analysed using descriptive and inferential statistics. Concurrently, an embedded qualitative study involving semistructured interviews with 24 enrolled families at 3 months and 9 months post-PICU discharge will be undertaken. Framework analysis will be used to analyse the qualitative data. ETHICS AND DISSEMINATION: The study has received ethical approval from the National Health Services Research Ethics Committee (Ref: 19/WM/0290) and full governance clearance. This will be the first UK study to comprehensively investigate physical, functional, emotional and social consequences of PICU survival in the first-year postdischarge.Clinical Trials Registration Number: ISRCTN28072812 [Pre-results]
Insights into enterotoxigenic Escherichia coli diversity in Bangladesh utilizing genomic epidemiology
Poloxomer 188 Has a Deleterious Effect on Dystrophic Skeletal Muscle Function
Duchenne muscular dystrophy (DMD) is an X-linked, fatal muscle wasting disease for which there is currently no cure and limited palliative treatments. Poloxomer 188 (P188) is a tri-block copolymer that has been proposed as a potential treatment for cardiomyopathy in DMD patients. Despite the reported beneficial effects of P188 on dystrophic cardiac muscle function, the effects of P188 on dystrophic skeletal muscle function are relatively unknown. Mdx mice were injected intraperitoneally with 460 mg/kg or 30 mg/kg P188 dissolved in saline, or saline alone (control). The effect of single-dose and 2-week daily treatment was assessed using a muscle function test on the Tibialis Anterior (TA) muscle in situ in anaesthetised mice. The test comprises a warm up, measurement of the force-frequency relationship and a series of eccentric contractions with a 10% stretch that have previously been shown to cause a drop in maximum force in mdx mice. After 2 weeks of P188 treatment at either 30 or 460 mg/kg/day the drop in maximum force produced following eccentric contractions was significantly greater than that seen in saline treated control mice (P = 0.0001). Two week P188 treatment at either dose did not significantly change the force-frequency relationship or maximum isometric specific force produced by the TA muscle. In conclusion P188 treatment increases susceptibility to contraction-induced injury following eccentric contractions in dystrophic skeletal muscle and hence its suitability as a potential therapeutic for DMD should be reconsidered
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have been
proposed in the literature, the question of how to aggregate the predictions of
the individual members of the ensemble has received little attention so far. In
this paper, we introduce a formal framework of ensemble multilabel
classification, in which we distinguish two principal approaches: "predict then
combine" (PTC), where the ensemble members first make loss minimizing
predictions which are subsequently combined, and "combine then predict" (CTP),
which first aggregates information such as marginal label probabilities from
the individual ensemble members, and then derives a prediction from this
aggregation. While both approaches generalize voting techniques commonly used
for multilabel ensembles, they allow to explicitly take the target performance
measure into account. Therefore, concrete instantiations of CTP and PTC can be
tailored to concrete loss functions. Experimentally, we show that standard
voting techniques are indeed outperformed by suitable instantiations of CTP and
PTC, and provide some evidence that CTP performs well for decomposable loss
functions, whereas PTC is the better choice for non-decomposable losses.Comment: 14 pages, 2 figure
A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries
<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p
Cancer cells exploit an orphan RNA to drive metastatic progression.
Here we performed a systematic search to identify breast-cancer-specific small noncoding RNAs, which we have collectively termed orphan noncoding RNAs (oncRNAs). We subsequently discovered that one of these oncRNAs, which originates from the 3' end of TERC, acts as a regulator of gene expression and is a robust promoter of breast cancer metastasis. This oncRNA, which we have named T3p, exerts its prometastatic effects by acting as an inhibitor of RISC complex activity and increasing the expression of the prometastatic genes NUPR1 and PANX2. Furthermore, we have shown that oncRNAs are present in cancer-cell-derived extracellular vesicles, raising the possibility that these circulating oncRNAs may also have a role in non-cell autonomous disease pathogenesis. Additionally, these circulating oncRNAs present a novel avenue for cancer fingerprinting using liquid biopsies
Implications for Cardiac Function Following Rescue of the Dystrophic Diaphragm in a Mouse Model of Duchenne Muscular Dystrophy
Containment studies of transgenic mosquitoes in disease endemic countries: the broad concept of facilities readiness
Genetic strategies for large scale pest or vector control using modified insects are not yet operational in Africa, and currently rely on import of the modified strains to begin preliminary, contained studies. Early involvement of research teams from participating countries is crucial to evaluate candidate field interventions. Following the recommended phased approach for novel strategies, evaluation should begin with studies in containment facilities. Experiences to prepare facilities and build international teams for research on transgenic mosquitoes revealed some important organizing themes underlying the concept of “facilities readiness,” or the point at which studies in containment may proceed, in sub-Saharan African settings. First, “compliance” for research with novel or non-native living organisms was defined as the fulfillment of all legislative and regulatory requirements. This is not limited to regulations regarding use of transgenic organisms. Second, the concept of “colony utility” was related to the characteristics of laboratory colonies being produced so that results of studies may be validated across time, sites, and strains or technologies; so that the appropriate candidate strains are moved forward toward field studies. Third, the importance of achieving “defensible science” was recognized, including that study conclusions can be traced back to evidence, covering the concerns of various stakeholders over the long term. This, combined with good stewardship of resources and appropriate funding, covers a diverse set of criteria for declaring when “facilities readiness” has been attained. It is proposed that, despite the additional demands on time and resources, only with the balance of and rigorous achievement of each of these organizing themes can collaborative research into novel strategies in vector or pest control reliably progress past initial containment studies
Genomics of Divergence along a Continuum of Parapatric Population Differentiation
MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1)
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