Nano-biolistics: a method of biolistic transfection of cells and tissues using a gene gun with novel nanometer-sized projectiles.

BACKGROUND: Biolistic transfection is proving an increasingly popular method of incorporating DNA or RNA into cells that are difficult to transfect using traditional methods. The technique routinely uses 'microparticles', which are ~1 μm diameter projectiles, fired into tissues using pressurised gas. These microparticles are efficient at delivering DNA into cells, but cannot efficiently transfect small cells and may cause significant tissue damage, thus limiting their potential usefulness. Here we describe the use of 40 nm diameter projectiles--nanoparticles--in biolistic transfections to determine if they are a suitable alternative to microparticles. RESULTS: Examination of transfection efficiencies in HEK293 cells, using a range of conditions including different DNA concentrations and different preparation procedures, reveals similar behaviour of microparticles and nanoparticles. The use of nanoparticles, however, resulted in ~30% fewer damaged HEK293 cells following transfection. Biolistic transfection of mouse ear tissue revealed similar depth penetration for the two types of particles, and also showed that 20% in microparticle-transfected samples. Visualising details of small cellular structures was also considerably enhanced when using nanoparticles. CONCLUSIONS: We conclude that nanoparticles are as efficient for biolistic transfection as microparticles, and are more appropriate for use in small cells, when examining cellular structures and/or where tissue damage is a problem.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact

Trends in the availability and usage of electrophysical agents in physiotherapy practices from 1990 to 2010: A review

This is the Pre-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 Maney PublishingBackground: The use of electrophysical agents has a historically important role in physiotherapy practice. There are anecdotal reports that the availability and usage of electrotherapy modalities are declining, which may have implications for physiotherapy practice. The aim of this literature review was to provide scientific evidence on electrotherapy usage in the last 20 years by identifying trends in availability, use, and non-use of nine electrotherapeutic modalities in physiotherapy practices during 1990s and 2000s. Methods: Review of empirical studies published in the English language from 1990 to 2010 and identified through searching online bibliographic databases, which included: Medline/OvidSP, PubMed Central, CINAHL/EBSCOhost, ScienceDirect, Scopus, ISI Web of Science, and Google Scholar. Findings: In the last 20 years, ultrasound availability and usage show increasing trends in several countries. The availability and use of pulsed shortwave diathermy and laser have shown steady trends. Transcutaneous electrical nerve stimulation, interferential, and biofeedback availability and usage have shown increasing trends in the UK and decreasing trends in Australia and the Republic of Ireland. Trends of continuous shortwave diathermy availability and use are declining irrespective of the country of the study. The availability and usage of microwave diathermy and H-wave show steeply declining trends, while there is a sharp rise in their non-availability over the last several years. Conclusions: The availability and use of electrophysical agents have greatly changed in the last 20 years. Declining trends in the availability and usage along with increasing trend of non-availability of electrotherapy modalities may have implications for electrotherapy education, training, and practice in the coming years.This study was funded by Health & Safety Executive, UK (grant no. 4371/R47.022)

Characterization of Shewanella oneidensis MtrC: a cell-surface decaheme cytochrome involved in respiratory electron transport to extracellular electron acceptors

MtrC is a decaheme c-type cytochrome associated with the outer cell membrane of Fe(III)-respiring species of the Shewanella genus. It is proposed to play a role in anaerobic respiration by mediating electron transfer to extracellular mineral oxides that can serve as terminal electron acceptors. The present work presents the first spectropotentiometric and voltammetric characterization of MtrC, using protein purified from Shewanella oneidensis MR-1. Potentiometric titrations, monitored by UV–vis absorption and electron paramagnetic resonance (EPR) spectroscopy, reveal that the hemes within MtrC titrate over a broad potential range spanning between approximately +100 and approximately -500 mV (vs. the standard hydrogen electrode). Across this potential window the UV–vis absorption spectra are characteristic of low-spin c-type hemes and the EPR spectra reveal broad, complex features that suggest the presence of magnetically spin-coupled low-spin c-hemes. Non-catalytic protein film voltammetry of MtrC demonstrates reversible electrochemistry over a potential window similar to that disclosed spectroscopically. The voltammetry also allows definition of kinetic properties of MtrC in direct electron exchange with a solid electrode surface and during reduction of a model Fe(III) substrate. Taken together, the data provide quantitative information on the potential domain in which MtrC can operate

M3C: Monte Carlo reference-based consensus clustering.

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters

Low-grade pelvic masses with spindle cell and fibroblastic proliferation: a case report

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Effects of study design and allocation on participant behaviour-ESDA: study protocol for a randomized controlled trial

Background: What study participants think about the nature of a study has been hypothesised to affect subsequent behaviour and to potentially bias study findings. In this trial we examine the impact of awareness of study design and allocation on participant drinking behaviour. Methods/Design: A three-arm parallel group randomised controlled trial design will be used. All recruitment, screening, randomisation, and follow-up will be conducted on-line among university students. Participants who indicate a hazardous level of alcohol consumption will be randomly assigned to one of three groups. Group A will be informed their drinking will be assessed at baseline and again in one month (as in a cohort study design). Group B will be told the study is an intervention trial and they are in the control group. Group C will be told the study is an intervention trial and they are in the intervention group. All will receive exactly the same brief educational material to read. After one month, alcohol intake for the past 4 weeks will be assessed. Discussion: The experimental manipulations address subtle and previously unexplored ways in which participant behaviour may be unwittingly influenced by standard practice in trials. Given the necessity of relying on self-reported outcome, it will not be possible to distinguish true behaviour change from reporting artefact. This does not matter in the present study, as any effects of awareness of study design or allocation involve bias that is not well understood. There has been little research on awareness effects, and our outcomes will provide an indication of the possible value of further studies of this type and inform hypothesis generation

Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays

Background Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells

Relationship of cognitive function in patients with schizophrenia in remission to disability: a cross-sectional study in an Indian sample

Background: Cognitive deficits in various domains have been consistently replicated in patients with schizophrenia. Most studies looking at the relationship between cognitive dysfunction and functional disability are from developed countries. Studies from developing countries are few. The purpose of the present study was to compare the neurocognitive function in patients with schizophrenia who were in remission with that of normal controls and to determine if there is a relationship between measures of cognition and functional disability. <p/>Methods: This study was conducted in the Psychiatric Unit of a General Hospital in Mumbai, India. Cognitive function in 25 patients with schizophrenia in remission was compared to 25 normal controls. Remission was confirmed using the brief psychiatric rating scale (BPRS) and scale for the assessment of negative symptoms (SANS). Subjects were administered a battery of cognitive tests covering aspects of memory, executive function and attention. The results obtained were compared between the groups. Correlation analysis was used to look for relationship between illness factors, cognitive function and disability measured using the Indian disability evaluation and assessment scale. <p/>Results: Patients with schizophrenia showed significant deficits on tests of attention, concentration, verbal and visual memory and tests of frontal lobe/executive function. They fared worse on almost all the tests administered compared to normal controls. No relationship was found between age, duration of illness, number of years of education and cognitive function. In addition, we did not find a statistically significant relationship between cognitive function and scores on the disability scale. <p/>Conclusion: The data suggests that persistent cognitive deficits are seen in patients with schizophrenia under remission. The cognitive deficits were not associated with symptomatology and functional disability. It is possible that various factors such as employment and family support reduce disability due to schizophrenia in developing countries like India. Further studies from developing countries are required to explore the relationship between cognitive deficits, functional outcome and the role of socio-cultural variables as protective factors

Extracellular traps and PAD4 released by macrophages induce citrullination and auto-antibody production in autoimmune arthritis

Barts Charity grant number MRC0177