183 research outputs found
Recovering the chiral critical end-point via delocalization of quark interactions
We show that for the lower branch of the quark condensate and values higher
than approximately the chiral critical end-point in
the Nambu--Jona-Lasinio model does not occur in the phase diagram. By using
lattice motivated non-local quark interactions, we demonstrate that the
critical end-point can be recovered. We study this behavior for a range of
condensate values and find that the variation in the position of the critical
end-point is more pronounced as the condensate is increased.Comment: title changed, minor changes in text, version to match the one
published in PR
Biological age estimation using circulating blood biomarkers
Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population
Serum metabolomic profiles associated with subclinical and clinical cardiovascular phenotypes in people with type 2 diabetes
BACKGROUND: Atherosclerotic cardiovascular diseases (CVD) is the leading cause of death in diabetes, but the full range of biomarkers reflecting atherosclerotic burden and CVD risk in people with diabetes is unknown. Metabolomics may help identify novel biomarkers potentially involved in development of atherosclerosis. We investigated the serum metabolomic profile of subclinical atherosclerosis, measured using ankle brachial index (ABI), in people with type 2 diabetes, compared with the profile for symptomatic CVD in the same population. METHODS: The Edinburgh Type 2 Diabetes Study is a cohort of 1,066 individuals with type 2 diabetes. ABI was measured at baseline, years 4 and 10, with cardiovascular events assessed at baseline and during 10 years of follow-up. A panel of 228 metabolites was measured at baseline using nuclear magnetic resonance spectrometry, and their association with both ABI and prevalent CVD was explored using univariate regression models and least absolute shrinkage and selection operator (LASSO). Metabolites associated with baseline ABI were further explored for association with follow-up ABI and incident CVD. RESULTS: Mean (standard deviation, SD) ABI at baseline was 0.97 (0.18, N = 1025), and prevalence of CVD was 35.0%. During 10-year follow-up, mean (SD) change in ABI was + 0.006 (0.178, n = 436), and 257 CVD events occurred. Lactate, glycerol, creatinine and glycoprotein acetyls levels were associated with baseline ABI in both univariate regression [βs (95% confidence interval, CI) ranged from − 0.025 (− 0.036, − 0.015) to − 0.023 (− 0.034, − 0.013), all p < 0.0002] and LASSO analysis. The associations remained nominally significant after adjustment for major vascular risk factors. In prospective analyses, lactate was nominally associated with ABI measured at years 4 and 10 after adjustment for baseline ABI. The four ABI-associated metabolites were all positively associated with prevalent CVD [odds ratios (ORs) ranged from 1.29 (1.13, 1.47) to 1.49 (1.29, 1.74), all p < 0.0002], and they were also positively associated with incident CVD [ORs (95% CI) ranged from 1.19 (1.02, 1.39) to 1.35 (1.17, 1.56), all p < 0.05]. CONCLUSIONS: Serum metabolites relating to glycolysis, fluid balance and inflammation were independently associated with both a marker of subclinical atherosclerosis and with symptomatic CVD in people with type 2 diabetes. Additional investigation is warranted to determine their roles as possible etiological and/or predictive biomarkers for atherosclerotic CVD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-022-01493-w
Combining serum metabolomic profiles with traditional risk factors improves 10-year cardiovascular risk prediction in people with type 2 diabetes
Integrating omics datasets with the OmicsPLS package
Background: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. Results: We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. Conclusions: We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLSand can be installed in R via install.packages("OmicsPLS")
Resonant enhancement in leptogenesis
Vanilla leptogenesis within the type I seesaw framework requires the mass scale of the right-handed neutrinos to be above 109 GeV. This lower bound can be avoided if at least two of the sterile states are almost mass degenerate, which leads to an enhancement of the decay asymmetry. Leptogenesis models that can be tested in current and upcoming experiments often rely on this resonant enhancement, and a systematic and consistent description is therefore necessary for phenomenological applications. In this review article, we give an overview of different methods that have been used to study the saturation of the resonant enhancement when the mass difference becomes comparable to the characteristic width of the Majorana neutrinos. In this limit, coherent flavor transitions start to play a decisive role, and off-diagonal correlations in flavor space have to be taken into account. We compare various formalisms that have been used to describe the resonant regime and discuss under which circumstances the resonant enhancement can be captured by simplified expressions for the CP asymmetry. Finally, we briefly review some of the phenomenological aspects of resonant leptogenesis
Spray-driedMicrospheres Based on Chitosan and LecithinCyclosporin A Delivery System
Conventional and composed cyclosporin A (CsA)-loaded polymeric microspheres (MS) were prepared by spray-drying of CsA/chitosan one-phase system (solutions) and CsA/lecithin/chitosan two-phase system (suspensions). Microspheres were characterised in terms of production yield, entrapment efficiency, size distribution, zeta-potential, thermal properties, swelling ability and drug release profile. Conventional MS were characterised by mean diameter ranging from 1.15 ± 0.91 to 1.27 ± 0.84 m and CsA entrapment efficiency varying from 72.6 to 87.3%. Composed MS were characterised by larger mean diameter (1.32 ± 1.08 to 1.53 ± 1.15 m) and higher CsA entrapment efficiency (86.6–94.3%) compared to the corresponding conventional MS. Only composed MS showed swelling ability, which was proportional to chitosan base content in the preparation. In vitro CsA release profile depended on both,
the type of the spray-dried system and the chitosan used, as these factors were crucial in determining CsA entrapment pattern and swelling/dissolution ability of MS
Executive function predicts school readiness in autistic and typical preschool children
Children’s emerging executive functions (EF) have been shown to be critical for a whole range of other functions, including school readiness and later academic success. Here we examine for the first time whether individual differences in EF are uniquely associated with autistic children’s readiness to learn in school, beyond general and developmental influences in age and ability. Thirty autistic and 30 typical preschool children, matched on age and ability, were assessed on EF (working memory, inhibition, set-shifting) and school readiness measures. Autistic children performed significantly worse on school readiness measures and EF measures relative to typical children. Furthermore, individual differences in children’s EF skills, especially in inhibitory control and working memory, were uniquely related to variation in their school readiness for both autistic and non-autistic children. The findings from this cross-sectional study provide further support for the potential role of EF in explaining the variability in autistic children’s functional outcomes
Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene
K.A.K. acknowledges funding from the MRC Doctoral Training Programme in Precision Medicine (MR/N013166/1). L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). Z.K. was supported by the Swiss National Science Foundation (310030-189147). J.F.W. acknowledges funding from the MRC Human Genetics Unit programme grant Quantitative Traits in Health and Disease (U. MC_UU_00007/10). N.M.M. was supported by a Wellcome Trust New Investigator Award (100981/Z/13/Z). We kindly thank Alain Colige and colleagues at the University of Liege for the provision of Adamts14+/– mouse sperm. We would also like to thank the researchers, funders and participants of all the contributing cohorts. Specifically, we thank the UK Biobank Resource, approved under application 19655. ORCADES was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276, CZB/4/710), the Royal Society, the MRC Human Genetics Unit, Arthritis Research UK and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA extractions were performed at the Clinical Research Facility in Edinburgh. We would like to acknowledge the invaluable contributions of the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). The genetics work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. The Fenland Study (10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1). We are grateful to all the volunteers and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. We further acknowledge support for genomics from the Medical Research Council (MC_PC_13046).Peer reviewedPublisher PD
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