Bioengineering the ameloblastoma tumour to study its effect on bone nodule formation

Ameloblastoma is a benign, epithelial cancer of the jawbone, which causes bone resorption and disfigurement to patients affected. The interaction of ameloblastoma with its tumour stroma drives invasion and progression. We used stiff collagen matrices to engineer active bone forming stroma, to probe the interaction of ameloblastoma with its native tumour bone microenvironment. This bone-stroma was assessed by nano-CT, transmission electron microscopy (TEM), Raman spectroscopy and gene analysis. Furthermore, we investigated gene correlation between bone forming 3D bone stroma and ameloblastoma introduced 3D bone stroma. Ameloblastoma cells increased expression of MMP-2 and -9 and RANK temporally in 3D compared to 2D. Our 3D biomimetic model formed bone nodules of an average surface area of 0.1 mm2 and average height of 92.37 ± 7.96 μm over 21 days. We demonstrate a woven bone phenotype with distinct mineral and matrix components and increased expression of bone formation genes in our engineered bone. Introducing ameloblastoma to the bone stroma, completely inhibited bone formation, in a spatially specific manner. Multivariate gene analysis showed that ameloblastoma cells downregulate bone formation genes such as RUNX2. Through the development of a comprehensive bone stroma, we show that an ameloblastoma tumour mass prevents osteoblasts from forming new bone nodules and severely restricted the growth of existing bone nodules. We have identified potential pathways for this inhibition. More critically, we present novel findings on the interaction of stromal osteoblasts with ameloblastoma

Multi-drug resistant Acinetobacter infections in critically injured Canadian forces soldiers

<p>Abstract</p> <p>Background</p> <p>Military members, injured in Afghanistan or Iraq, have returned home with multi-drug resistant <it>Acinetobacter baumannii </it>infections. The source of these infections is unknown.</p> <p>Methods</p> <p>Retrospective study of all Canadian soldiers who were injured in Afghanistan and who required mechanical ventilation from January 1 2006 to September 1 2006. Patients who developed <it>A. baumannii </it>ventilator associated pneumonia (VAP) were identified. All <it>A. baumannii </it>isolates were retrieved for study patients and compared with <it>A. baumannii </it>isolates from environmental sources from the Kandahar military hospital using pulsed-field gel electrophoresis (PFGE).</p> <p>Results</p> <p>During the study period, six Canadian Forces (CF) soldiers were injured in Afghanistan, required mechanical ventilation and were repatriated to Canadian hospitals. Four of these patients developed <it>A. baumannii </it>VAP. <it>A. baumannii </it>was also isolated from one environmental source in Kandahar – a ventilator air intake filter. Patient isolates were genetically indistinguishable from each other and from the isolates cultured from the ventilator filter. These isolates were resistant to numerous classes of antimicrobials including the carbapenems.</p> <p>Conclusion</p> <p>These results suggest that the source of <it>A. baumannii </it>infection for these four patients was an environmental source in the military field hospital in Kandahar. A causal linkage, however, was not established with the ventilator. This study suggests that infection control efforts and further research should be focused on the military field hospital environment to prevent further multi-drug resistant <it>A. baumannii </it>infections in injured soldiers.</p

Do Electronic Health Records Help or Hinder Medical Education?

Many countries worldwide are digitizing patients' medical records. What impact will these electronic health records have upon medical education? This debate examines the threats and opportunities

Protection of early phase hepatic ischemia-reperfusion injury by cholinergic agonists

BACKGROUND: Cytokine production is critical in ischemia/reperfusion (IR) injury. Acetylcholine binds to macrophages and inhibits cytokine synthesis, through the cholinergic anti-inflammatory pathway. This study examined the role of the cholinergic pathway in cytokine production and hepatic IR- injury. METHODS: Adult male mice underwent 90-min of partial liver ischemia followed by reperfusion. The AChR agonists (1,1-dimethyl-4-phenyl-L-pioperazinium-iodide [DMPP], and nicotine) or saline-vehicle were administered i.p. before ischemia. Plasma cytokine tumor necrosis factor (TNF)-α, macrophage inflammatory protein-2, and Interleukin-6 were measured. Liver injury was assessed by plasma alanine transaminase (ALT) and liver histopathology. RESULTS: A reperfusion time-dependent hepatocellular injury occurred as was indicated by increased plasma-ALT and histopathology. The injury was associated with marked elevation of plasma cytokines/chemokines. Pre-ischemic treatment of mice with DMPP or nicotine significantly decreased plasma-ALT and cytokines after 3 h of reperfusion. After 6 h of reperfusion, the protective effect of DMPP decreased and reached a negligible level by 24 h of reperfusion, despite significantly low levels of plasma cytokines. Histopathology showed markedly diminished hepatocellular injury in DMPP- and nicotine-pretreated mice during the early-phase of hepatic-IR, which reached a level comparable to saline-treated mice at late-phase of IR. CONCLUSION: Pharmacological modulation of the cholinergic pathway provides a means to modulate cytokine production and to delay IR-induced heaptocellular injury

BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

Background: The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems' level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log(2)- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. Results: The only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques. Conclusions: BABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets

Dynamic metabolic patterns tracking neurodegeneration and gliosis following 26S proteasome dysfunction in mouse forebrain neurons

Metabolite profling is an important tool that may better capture the multiple features of neurodegeneration. With the considerable parallels between mouse and human metabolism, the use of metabolomics in mouse models with neurodegenerative pathology provides mechanistic insight and ready translation into aspects of human disease. Using 400MHz nuclear magnetic resonance spectroscopy we have carried out a temporal region-specifc investigation of the metabolome of neuron-specifc 26S proteasome knockout mice characterised by progressive neurodegeneration and Lewy-like inclusion formation in the forebrain. An early signifcant decrease in N-acetyl aspartate revealed evidence of neuronal dysfunction before cell death that may be associated with changes in brain neuroenergetics, underpinning the use of this metabolite to track neuronal health. Importantly, we show early and extensive activation of astrocytes and microglia in response to targeted neuronal dysfunction in this context, but only late changes in myo-inositol; the best established glial cell marker in magnetic resonance spectroscopy studies, supporting recent evidence that additional early neuroinfammatory markers are needed. Our results extend the limited understanding of metabolite changes associated with gliosis and provide evidence that changes in glutamate homeostasis and lactate may correlate with astrocyte activation and have biomarker potential for tracking neuroinfammation

Assessment of Metabolic Phenotypes in Patients with Non-ischemic Dilated Cardiomyopathy Undergoing Cardiac Resynchronization Therapy

Studies of myocardial metabolism have reported that contractile performance at a given myocardial oxygen consumption (MVO2) can be lower when the heart is oxidizing fatty acids rather than glucose or lactate. The objective of this study is to assess the prognostic value of myocardial metabolic phenotypes in identifying non-responders among non-ischemic dilated cardiomyopathy (NIDCM) patients undergoing cardiac resynchronization therapy (CRT). Arterial and coronary sinus plasma concentrations of oxygen, glucose, lactate, pyruvate, free fatty acids (FFA), and 22 amino acids were obtained from 19 male and 2 female patients (mean age 56 ± 16) with NIDCM undergoing CRT. Metabolite fluxes/MVO2 and extraction fractions were calculated. Flux balance analysis (FBA) was performed with MetaFluxNet 1.8 on a metabolic network of the cardiac mitochondria (189 reactions, 230 metabolites) reconstructed from mitochondrial proteomic data (615 proteins) from human heart tissue. Non-responders based on left ventricular ejection fraction (LVEF) demonstrated a greater mean FFA extraction fraction (35% ± 17%) than responders [18 ± 10%, p = 0.0098, area under the estimated ROC curve (AUC) was 0.8238, S.E. 0.1115]. Calculated adenosine triphosphate (ATP)/MVO2 using FBA correlated with change in New York Heart Association (NYHA) class (rho = 0.63, p = 0.0298; AUC = 0.8381, S.E. 0.1316). Non-responders based on both LVEF and NYHA demonstrated a greater mean FFA uptake/MVO2 (0.115 ± 0.112) than responders (0.034 ± 0.030, p = 0.0171; AUC = 0.8593, S.E. 0.0965). Myocardial FFA flux and calculated maximal ATP synthesis flux using FBA may be helpful as biomarkers in identifying non-responders among NIDCM patients undergoing CRT

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Background: Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.Results: We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.Conclusions: A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes