Identification of key residues that confer Rhodobacter sphaeroides LPS activity at horse TLR4/MD-2.

The molecular determinants underpinning how hexaacylated lipid A and tetraacylated precursor lipid IVa activate Toll-like receptor 4 (TLR4) are well understood, but how activation is induced by other lipid A species is less clear. Species specificity studies have clarified how TLR4/MD-2 recognises different lipid A structures, for example tetraacylated lipid IVa requires direct electrostatic interactions for agonism. In this study, we examine how pentaacylated lipopolysaccharide from Rhodobacter sphaeroides (RSLPS) antagonises human TLR4/MD-2 and activates the horse receptor complex using a computational approach and cross-species mutagenesis. At a functional level, we show that RSLPS is a partial agonist at horse TLR4/MD-2 with greater efficacy than lipid IVa. These data suggest the importance of the additional acyl chain in RSLPS signalling. Based on docking analysis, we propose a model for positioning of the RSLPS lipid A moiety (RSLA) within the MD-2 cavity at the TLR4 dimer interface, which allows activity at the horse receptor complex. As for lipid IVa, RSLPS agonism requires species-specific contacts with MD-2 and TLR4, but the R2 chain of RSLA protrudes from the MD-2 pocket to contact the TLR4 dimer in the vicinity of proline 442. Our model explains why RSLPS is only partially dependent on horse TLR4 residue R385, unlike lipid IVa. Mutagenesis of proline 442 into a serine residue, as found in human TLR4, uncovers the importance of this site in RSLPS signalling; horse TLR4 R385G/P442S double mutation completely abolishes RSLPS activity without its counterpart, human TLR4 G384R/S441P, being able to restore it. Our data highlight the importance of subtle changes in ligand positioning, and suggest that TLR4 and MD-2 residues that may not participate directly in ligand binding can determine the signalling outcome of a given ligand. This indicates a cooperative binding mechanism within the receptor complex, which is becoming increasingly important in TLR signalling.This work was supported by a project grant from the Horserace Betting Levy Board to CEB and a Horserace Betting Levy Board Veterinary Research Training Scholarship to KLI. This work was also supported by a Wellcome Trust program grant to NJG and CEB. CEB is a BBSRC Research Development Fellow.This is the final version of the article. It first appeared from PLOS at http://dx.doi.org/10.1371/journal.pone.0098776

A systematic review of the health and well-being benefits of biodiverse environments

This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of Toxicology and Environmental Health, Part B: Critical Reviews on 05 Mar 2014, available online: http://www.tandfonline.com/doi/pdf/10.1080/10937404.2013.856361Recent ecosystem service models have placed biodiversity as a central factor in the processes that link the natural environment to health. While it is recognized that disturbed ecosystems might negatively affect human well-being, it is not clear whether biodiversity is related to or can promote "good" human health and well-being. The aim of this study was to systematically identify, summarize, and synthesize research that had examined whether biodiverse environments are health promoting. The objectives were twofold: (1) to map the interdisciplinary field of enquiry and (2) to assess whether current evidence enables us to characterize the relationship. Due to the heterogeneity of available evidence a narrative synthesis approach was used, which is textual rather than statistical. Extensive searches identified 17 papers that met the inclusion criteria: 15 quantitative and 2 qualitative. The evidence was varied in disciplinary origin, with authors approaching the question using different study designs and methods, and conceptualizations of biodiversity, health, and well-being. There is some evidence to suggest that biodiverse natural environments promote better health through exposure to pleasant environments or the encouragement of health-promoting behaviors. There was also evidence of inverse relationships, particularly at a larger scale (global analyses). However, overall the evidence is inconclusive and fails to identify a specific role for biodiversity in the promotion of better health. High-quality interdisciplinary research is needed to produce a more reliable evidence base. Of particular importance is identifying the specific ecosystem services, goods, and processes through which biodiversity may generate good health and well-being.European Regional Development Fund Programme 2007 to 2013European Social Fund Convergence Programme for Cornwall and the Isles of Scilly

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Risk-taking in disorders of natural and drug rewards: neural correlates and effects of probability, valence, and magnitude.

Pathological behaviors toward drugs and food rewards have underlying commonalities. Risk-taking has a fourfold pattern varying as a function of probability and valence leading to the nonlinearity of probability weighting with overweighting of small probabilities and underweighting of large probabilities. Here we assess these influences on risk-taking in patients with pathological behaviors toward drug and food rewards and examine structural neural correlates of nonlinearity of probability weighting in healthy volunteers. In the anticipation of rewards, subjects with binge eating disorder show greater risk-taking, similar to substance-use disorders. Methamphetamine-dependent subjects had greater nonlinearity of probability weighting along with impaired subjective discrimination of probability and reward magnitude. Ex-smokers also had lower risk-taking to rewards compared with non-smokers. In the anticipation of losses, obesity without binge eating had a similar pattern to other substance-use disorders. Obese subjects with binge eating also have impaired discrimination of subjective value similar to that of the methamphetamine-dependent subjects. Nonlinearity of probability weighting was associated with lower gray matter volume in dorsolateral and ventromedial prefrontal cortex and orbitofrontal cortex in healthy volunteers. Our findings support a distinct subtype of binge eating disorder in obesity with similarities in risk-taking in the reward domain to substance use disorders. The results dovetail with the current approach of defining mechanistically based dimensional approaches rather than categorical approaches to psychiatric disorders. The relationship to risk probability and valence may underlie the propensity toward pathological behaviors toward different types of rewards.This is the final version. It was first published by NPG at http://www.nature.com/npp/journal/v40/n4/full/npp2014242a.htm

Experiences of patients with chronic gastrointestinal conditions: in their own words

<p>Abstract</p> <p>Background</p> <p>Irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) are chronic conditions affecting millions of individuals in the United States. The symptoms are well-documented and can be debilitating. How these chronic gastrointestinal (GI) conditions impact the daily lives of those afflicted is not well documented, especially from a patient's perspective.</p> <p>Methods</p> <p>Here we describe data from a series of 22 focus groups held at three different academic medical centers with individuals suffering from chronic GI conditions. All focus groups were audio recorded and transcribed. Two research team members independently analyzed transcripts from each focus group following an agreed upon coding scheme.</p> <p>Results</p> <p>One-hundred-thirty-six individuals participated in our study, all with a chronic GI related condition. They candidly discussed three broad themes that characterize their daily lives: identification of disease and personal identity, medications and therapeutics, and daily adaptations. These all tie to our participants trying to deal with symptoms on a daily basis. We find that a recurrent topic underlying these themes is the dichotomy of experiencing uncertainty and striving for control.</p> <p>Conclusions</p> <p>Study participants' open dialogue and exchange of experiences living with a chronic GI condition provide insight into how these conditions shape day-to-day activities. Our findings provide fertile ground for discussions about how clinicians might best facilitate, acknowledge, and elicit patients' stories in routine care to better address their experience of illness.</p

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

A Subset of Latency-Reversing Agents Expose HIV-Infected Resting CD4⁺ T-Cells to Recognition by Cytotoxic T-Lymphocytes

Resting CD4⁺ T-cells harboring inducible HIV proviruses are a critical reservoir in antiretroviral therapy (ART)-treated subjects. These cells express little to no viral protein, and thus neither die by viral cytopathic effects, nor are efficiently cleared by immune effectors. Elimination of this reservoir is theoretically possible by combining latency-reversing agents (LRAs) with immune effectors, such as CD8⁺ T-cells. However, the relative efficacy of different LRAs in sensitizing latently-infected cells for recognition by HIV-specific CD8⁺ T-cells has not been determined. To address this, we developed an assay that utilizes HIV-specific CD8⁺ T-cell clones as biosensors for HIV antigen expression. By testing multiple CD8⁺ T-cell clones against a primary cell model of HIV latency, we identified several single agents that primed latently-infected cells for CD8⁺ T-cell recognition, including IL-2, IL-15, two IL-15 superagonists (IL-15SA and ALT-803), prostratin, and the TLR-2 ligand Pam₃CSK₄. In contrast, we did not observe CD8⁺ T-cell recognition of target cells following treatment with histone deacetylase inhibitors or with hexamethylene bisacetamide (HMBA). In further experiments we demonstrate that a clinically achievable concentration of the IL-15 superagonist ‘ALT-803’, an agent presently in clinical trials for solid and hematological tumors, primes the natural ex vivo reservoir for CD8⁺ T-cell recognition. Thus, our results establish a novel experimental approach for comparative evaluation of LRAs, and highlight ALT-803 as an LRA with the potential to synergize with CD8⁺ T-cells in HIV eradication strategies.United States. National Institutes of Health (AI111860

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020