Checks and cheques : implementing a population health and recall system to improve coverage of patients with diabetes in a rural general practice

Identification of all diabetic patients in the population is essential if diabetic care is to be effective in achieving the targets of the St Vincent Declaration.1 The challenge therefore is to establish population based monitoring and control systems by means of state of the art technology in order to achieve quality assurance in the provision of care for patients with diabetes. 2,3 Disease management receives extensive international support as the most appropriate approach to organising and delivering healthcare for chronic conditions like diabetes.4 This approach is achieved through a combination of guidelines for practice, patient education, consultations and follow up using a planned team approach and a strong focus on continuous quality improvement using information technology. 5,6 The current software (Medical Director) could not easily meet these requirements which led us to adopt a trial of Ferret. In designing this project we used change management7 and the plan, do, study, act cycle8 illustrated in Diagram 1.<br /

Plasma CXCL10, sCD163 and sCD14 Levels Have Distinct Associations with Antiretroviral Treatment and Cardiovascular Disease Risk Factors

We investigate the associations of three established plasma biomarkers in the context of HIV and treatment-related variables including a comprehensive cardiovascular disease risk assessment, within a large ambulatory HIV cohort. Patients were recruited in 2010 to form the Royal Perth Hospital HIV/CVD risk cohort. Plasma sCD14, sCD163 and CXCL10 levels were measured in 475 consecutive patients with documented CVD risk (age, ethnicity, gender, smoking, blood pressure, BMI, fasting metabolic profile) and HIV treatment history including immunological/virological outcomes. The biomarkers assessed showed distinct associations with virological response: CXCL10 strongly correlated with HIV-1 RNA (p0.2). Associations between higher sCD163 and protease inhibitor therapy (p = 0.05) and lower sCD14 with integrase inhibitor therapy (p = 0.02) were observed. Levels of sCD163 were also associated with CVD risk factors (age, ethnicity, HDL, BMI), with a favourable influence of Framingham score <10% (p = 0.04). Soluble CD14 levels were higher among smokers (p = 0.002), with no effect of other CVD risk factors, except age (p = 0.045). Our findings confirm CXCL10, sCD163 and sCD14 have distinct associations with different aspects of HIV infection and treatment. Levels of CXCL10 correlated with routinely monitored variables, sCD163 levels reflect a deeper level of virological suppression and influence of CVD risk factors, while sCD14 levels were not associated with routinely monitored variables, with evidence of specific effects of smoking and integrase inhibitor therapy warranting further investigation

The development and evaluation of mini-GEMs: a short, focused, online e-learning videos in geriatric medicine

Mini Geriatric E-Learning Modules (Mini-GEMs) are short, focused, e-learning videos on geriatric medicine topics, hosted on YouTube, which are targeted at junior doctors working with older people. This study aimed to explore how these resources are accessed and used. The authors analyzed the viewing data from 22 videos published over the first 18 months of the Mini-GEM project. We conducted a focus group of U.K. junior doctors considering their experiences with Mini-GEMS. The Mini-GEMs were viewed 10,291 times over 18 months, equating to 38,435 minutes of total viewing time. The average viewing time for each video was 3.85 minutes. Learners valued the brevity and focused nature of the Mini-GEMs and reported that they watched them in a variety of settings to supplement clinical experiences and consolidate learning. Watching the videos led to an increase in self-reported confidence in managing older patients. Mini-GEMs can effectively disseminate clinical teaching material to a wide audience. The videos are valued by junior doctors due to their accessibility and ease of use

Measurement of Hydrogen Peroxide Influx Into Cells: Preparation For Measurement Using On-Chip Microelectrode Array

Hydrogen peroxide (H2O2) is commonly known as a toxic reactive oxidative species (ROS) for cells. Recent studies have found evidence that H2O2 is also an important cellular signalling molecule. Quantifying cellular influx of H2O2 will contribute to researchers’ understanding of the role H2O2 plays in healthy cells and cells involved in the progression of cancers and degenerative diseases. This work utilizes an assay kit and fluorescence techniques to evaluate cell lines and conditions to create a model biological system for measuring cellular H2O2 consumption. Pancreatic beta cells (MIN6), astrocytes, and glioblastoma cells (GBM43 and GBAM1) were placed in 10 μM and 20 μM H2O2 solutions for up to 5 hours. The consumption of H2O2 was measured using an Amplex Red Hydrogen Peroxide/Peroxidase Assay Kit (Molecular Probes/Invitrogen). GBAM1 cells exposed to 20 μM H2O2 displayed the fastest rate of H2O2 consumption (4.8 ± 1.2 nmol H2O2/min/106 cells), followed by GBM43 cells (1.5±0.46), astrocytes (1.1±0.24), and MIN6 cells (0.29±0.075). Additionally, the rate of consumption increased with increases in H2O2 concentration. In the future, an on-chip micro-electrode array (MEA) will be used for real-time electrochemical experiments to measure influx of H2O2 by astrocytes and GBAM1 cells with spatio-temporal resolution that the current techniques lack. The results from the electrochemical experiments will be compared to results from the assay kit to determine the ability of the MEA to accurately measure H2O2 concentration and flux. The MEA can be extended to a wide variety of cellular environments for analysis of additional real-time biological events

Cellular Model of Hydrogen Peroxide Release: In Preparation for On-Chip Sensor Measurements

Hydrogen peroxide is traditionally associated with cellular damage; however, recent studies show that low levels of H2O2 are released by cells as part of normal intercellular communication. The mechanisms of hydrogen peroxide transport, uptake and release, and biological effects are not yet well known but have important implications for cancer, stem cells, and aging. Standard H2O2 assays cannot make spatially or temporally resolved quantitative measurements at a cellular scale. Previously we developed a microelectrode array (MEA) and calibration methods for quantifying H2O2 gradients in space and time. The sensor was validated using artificial H2O2 gradients at subsecond and micrometer scale resolutions. The present study begins cellular work on H2O2 release to identify a cellular model system for MEA sensor testing. The morphology and H2O2 release from U937 human monocytes were analyzed after stimulation with ionomycin (1.2 ug/mL) and/or phorbol 12-myristate 13-acetate (PMA). Monocytes were stimulated with PMA (10 ng/mL to 150 ng/mL) for six hours. Hydrogen peroxide release was quantified over time using a traditional amplex red flurometric assay method. Mouse pancreatic beta (MIN6) cells were also tested as a negative control. Monocytes stimulated with PMA alone produced, on average, three times more H2O2 than those stimulated with ionomycin or a combination. Monocytes without ionomycin released H2O2 at 18.34 pmol/min/106 cells at 25 ng/mL of PMA. Ten, 25, and 100 ng/mL of PMA produced H2O2 significantly faster than the non-stimulated control. No significant difference was seen between PMA concentrations when ionomycin was added. These results indicate that PMA stimulated human monocytes may serve as a good model system for cellular validation of the H2O2 MEAs. In the future, biofunctionalization of the electrodes for additional molecular specificity will allow for the expansion of the method to other analytes, giving the sensor potential use in non-traditional lab environments with the ability to perform multiple assays autonomously

Neuroanatomical Domain of the Foundational Model of Anatomy Ontology

Background: The diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging. Description: The Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications. Conclusions: A requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain. We highlighted several use cases of these extensions, including ontology reuse, neuroimaging data annotation, and organizing 3D brain models

Identifying Patient-Specific Epstein-Barr Nuclear Antigen-1 Genetic Variation and Potential Autoreactive Targets Relevant to Multiple Sclerosis Pathogenesis

Background: Epstein-Barr virus (EBV) infection represents a major environmental risk factor for multiple sclerosis (MS), with evidence of selective expansion of Epstein-Barr Nuclear Antigen-1 (EBNA1)-specific CD4+ T cells that cross-recognize MS-associated myelin antigens in MS patients. HLA-DRB1*15-restricted antigen presentation also appears to determine susceptibility given its role as a dominant risk allele. In this study, we have utilised standard and next-generation sequencing techniques to investigate EBNA-1 sequence variation and its relationship to HLA-DR15 binding affinity, as well as examining potential cross-reactive immune targets within the central nervous system proteome. Methods: Sanger sequencing was performed on DNA isolated from peripheral blood samples from 73 Western Australian MS cases, without requirement for primary culture, with additional FLX 454 Roche sequencing in 23 samples to identify low-frequency variants. Patient-derived viral sequences were used to predict HLA-DRB1*1501 epitopes (NetMHCII, NetMHCIIpan) and candidates were evaluated for cross recognition with human brain proteins. Results: EBNA-1 sequence variation was limited, with no evidence of multiple viral strains and only low levels of variation identified by FLX technology (8.3% nucleotide positions at a 1% cutoff). In silico epitope mapping revealed two known HLA-DRB1*1501-restricted epitopes (‘AEG’: aa 481–496 and ‘MVF’: aa 562–577), and two putative epitopes between positions 502–543. We identified potential cross-reactive targets involving a number of major myelin antigens including experimentally confirmed HLA-DRB1*15-restricted epitopes as well as novel candidate antigens within myelin and paranodal assembly proteins that may be relevant to MS pathogenesis. Conclusions: This study demonstrates the feasibility of obtaining autologous EBNA-1 sequences directly from buffy coat samples, and confirms divergence of these sequences from standard laboratory strains. This approach has identified a number of immunogenic regions of EBNA-1 as well as known and novel targets for autoreactive HLA-DRB1*15-restricted T cells within the central nervous system that could arise as a result of cross-reactivity with EBNA-1-specific immune responses

Ensuring phenotyping algorithms using national electronic health records are FAIR:Meeting the needs of the cardiometabolic research community

Phenotyping algorithms enable the extraction of clinically-relevant information (such as diagnoses, prescription information, or a blood pressure measurement) from electronic health records for use in research. They have enormous potential and wide-ranging utility in research to improve disease understanding, health, and healthcare provision. While great progress has been achieved over the past years in standardising how genomic data are represented and curated (e.g. VCF files for variants), phenotypic data are significantly more fragmented and lack a common representation approach. This lack of standards creates challenges, including a lack of comparability, transparency and reproducibility, and limiting the subsequent use of phenotyping algorithms in other research studies. The FAIR guiding principles for scientific data management and stewardship state that digital assets should be findable, accessible, interoperable and reusable, yet the current lack of phenotyping algorithm standards means that phenotyping algorithms are not FAIR. We have therefore engaged with the community to address these challenges, including defining standards for the reporting and sharing of phenotyping algorithms. Here we present the results of our engagement with the community to identify and explore their requirements and outline our recommendations to ensure FAIR phenotyping algorithms are available to meet the needs of the cardiometabolic research community

Ensuring phenotyping algorithms using national electronic health records are FAIR:Meeting the needs of the cardiometabolic research community

Phenotyping algorithms enable the extraction of clinically-relevant information (such as diagnoses, prescription information, or a blood pressure measurement) from electronic health records for use in research. They have enormous potential and wide-ranging utility in research to improve disease understanding, health, and healthcare provision. While great progress has been achieved over the past years in standardising how genomic data are represented and curated (e.g. VCF files for variants), phenotypic data are significantly more fragmented and lack a common representation approach. This lack of standards creates challenges, including a lack of comparability, transparency and reproducibility, and limiting the subsequent use of phenotyping algorithms in other research studies. The FAIR guiding principles for scientific data management and stewardship state that digital assets should be findable, accessible, interoperable and reusable, yet the current lack of phenotyping algorithm standards means that phenotyping algorithms are not FAIR. We have therefore engaged with the community to address these challenges, including defining standards for the reporting and sharing of phenotyping algorithms. Here we present the results of our engagement with the community to identify and explore their requirements and outline our recommendations to ensure FAIR phenotyping algorithms are available to meet the needs of the cardiometabolic research community