2,098 research outputs found

    Timescales of influenza A/H3N2 antibody dynamics

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    Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants’ histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses

    Ebola virus glycoprotein stimulates IL-18 dependent natural killer cell responses

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    BACKGROUND: NK cells are activated by innate cytokines and viral ligands to kill virus-infected cells; these functions are enhanced during secondary immune responses and after vaccination by synergy with effector T cells and virus-specific antibodies. In human Ebola virus infection, clinical outcome is strongly associated with the initial innate cytokine response, but the role of NK cells has not been thoroughly examined. METHODS: The novel 2-dose heterologous Adenovirus type 26.ZEBOV (Ad26.ZEBOV) and modified vaccinia Ankara-BN-Filo (MVA-BN-Filo) vaccine regimen is safe and provides specific immunity against Ebola glycoprotein, and is currently in phase 2 and 3 studies. Here, we analysed NK cell phenotype and function in response to Ad26.ZEBOV, MVA-BN-Filo vaccination regimen, and in response to in vitro Ebola glycoprotein stimulation of PBMC isolated before and after vaccination. RESULTS: We show enhanced NK cell proliferation and activation after vaccination compared with baseline. Ebola glycoprotein-induced activation of NK cells was dependent on accessory cells and TLR-4-dependent innate cytokine secretion (predominantly from CD14+ monocytes) and enriched within less differentiated NK cell subsets. Optimal NK cell responses were dependent on IL-18 and IL-12, whilst IFN-γ secretion was restricted by high concentrations of IL-10. CONCLUSION: This study demonstrates the induction of NK cell effector functions early after Ad26.ZEBOV, MVA-BN-Filo vaccination and provides a mechanism for the activation and regulation of NK cells by Ebola GP. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02313077. FUNDING: U.K. Medical Research Council Studentship in Vaccine Research, Innovative Medicines Initiative 2 Joint Undertaking, EBOVAC (Grant 115861) and Crucell Holland (now Janssen Vaccines & Prevention B.V.), European Union’s Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations (EFPIA)

    Tendinopathy—from basic science to treatment

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    Chronic tendon pathology (tendinopathy), although common, is difficult to treat. Tendons possess a highly organized fibrillar matrix, consisting of type I collagen and various 'minor' collagens, proteoglycans and glycoproteins. The tendon matrix is maintained by the resident tenocytes, and there is evidence of a continuous process of matrix remodeling, although the rate of turnover varies at different sites. A change in remodeling activity is associated with the onset of tendinopathy. Major molecular changes include increased expression of type III collagen, fibronectin, tenascin C, aggrecan and biglycan. These changes are consistent with repair, but they might also be an adaptive response to changes in mechanical loading. Repeated minor strain is thought to be the major precipitating factor in tendinopathy, although further work is required to determine whether it is mechanical overstimulation or understimulation that leads to the change in tenocyte activity. Metalloproteinase enzymes have an important role in the tendon matrix, being responsible for the degradation of collagen and proteoglycan in both healthy patients and those with disease. Metalloproteinases that show increased expression in painful tendinopathy include ADAM (a disintegrin and metalloproteinase)-12 and MMP (matrix metalloproteinase)-23. The role of these enzymes in tendon pathology is unknown, and further work is required to identify novel and specific molecular targets for therapy

    Minimum sample size for external validation of a clinical prediction model with a binary outcome

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    In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p

    Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

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    Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide

    Cytomolecular identification of individual wheat-wheat chromosome arm associations in wheat-rye hybrids

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    Chromosome pairing in the meiotic metaphase I of wheatrye hybrids has been characterized by sequential genomic and fluorescent in situ hybridization allowing not only the discrimination of wheat and rye chromosomes, but also the identification of the individual wheat and rye chromosome arms involved in the chromosome associations. The majority of associations (93.8%) were observed between the wheat chromosomes. The largest number of wheat-wheat chromosome associations (53%) was detected between the A and D genomes, while the frequency of B-D and A-B associations was significantly lower (32 and 8%, respectively). Among the A-D chromosome associations, pairing between the 3AL and 3DL arms was observed with the highest frequency, while the most frequent of all the chromosome associations (0.113/ cell) was found to be the 3DS-3BS. Differences in the pairing frequency of the individual chromosome arms of wheat-rye hybrids have been discussed in relation to the homoeologous relationships between the constituent genomes of hexaploid wheat

    Prognostic markers in cancer: the evolution of evidence from single studies to meta-analysis, and beyond

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    In oncology, prognostic markers are clinical measures used to help elicit an individual patient's risk of a future outcome, such as recurrence of disease after primary treatment. They thus facilitate individual treatment choice and aid in patient counselling. Evidence-based results regarding prognostic markers are therefore very important to both clinicians and their patients. However, there is increasing awareness that prognostic marker studies have been neglected in the drive to improve medical research. Large protocol-driven, prospective studies are the ideal, with appropriate statistical analysis and clear, unbiased reporting of the methods used and the results obtained. Unfortunately, published prognostic studies rarely meet such standards, and systematic reviews and meta-analyses are often only able to draw attention to the paucity of good-quality evidence. We discuss how better-quality prognostic marker evidence can evolve over time from initial exploratory studies, to large protocol-driven primary studies, and then to meta-analysis or even beyond, to large prospectively planned pooled analyses and to the initiation of tumour banks. We highlight articles that facilitate each stage of this process, and that promote current guidelines aimed at improving the design, analysis, and reporting of prognostic marker research. We also outline why collaborative, multi-centre, and multi-disciplinary teams should be an essential part of future studies
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