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  • Journal article
    Shah M, Inacio M, Lu C, Schiratti P-R, Zheng S, Clement A, Simoes Monteiro de Marvao A, Bai W, King A, Ware J, Wilkins M, Mielke J, Elci E, Kryukov I, McGurk K, Bender C, Freitag D, O'Regan Det al., 2023,

    Environmental and genetic predictors of human cardiovascular ageing

    , Nature Communications, Vol: 14, Pages: 1-15, ISSN: 2041-1723

    Cardiovascular ageing is a process that begins early in life and leads to a progressive change instructure and decline in function due to accumulated damage across diverse cell types, tissues andorgans contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence andend-organ damage, however the genetic architecture of cardiovascular ageing is not known. Herewe use machine learning approaches to quantify cardiovascular age from image-derived traits ofvascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated bycardiometabolic risk factors and we also identify prescribed medications that are potential modifiersof ageing. Through large-scale modelling of ageing across multiple traits our results reveal insightsinto the mechanisms driving premature cardiovascular ageing and reveal potential molecular targetsto attenuate age-related processes.

  • Journal article
    Thanaj M, Mielke J, McGurk K, Bai W, Savioli N, Simoes Monteiro de Marvao A, Meyer H, Zeng L, Sohler F, Lumbers T, Wilkins M, Ware J, Bender C, Rueckert D, MacNamara A, Freitag D, O'Regan Det al., 2022,

    Genetic and environmental determinants of diastolic heart function

    , Nature Cardiovascular Research, Vol: 1, Pages: 361-371, ISSN: 2731-0590

    Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends onmyocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processesand is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiacmotion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wideassociation study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomericfunction under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes wereindependent predictors of diastolic function and we found a causal relationship between genetically-determined ventricularstiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolicfunction that are relevant for identifying causal relationships and potential tractable targets.

  • Journal article
    Beaney T, Clarke J, Woodcock T, McCarthy R, Saravanakumar K, Barahona M, Blair M, Hargreaves Det al., 2021,

    Patterns of healthcare utilisation in children and young people: a retrospective cohort study using routinely collected healthcare data in Northwest London

    , BMJ Open, Vol: 11, Pages: 1-14, ISSN: 2044-6055

    ObjectivesWith a growing role for health services in managing population health, there is a need for early identification of populations with high need. Segmentation approaches partition the population based on demographics, long-term conditions (LTCs) or healthcare utilisation but have mostly been applied to adults. Our study uses segmentation methods to distinguish patterns of healthcare utilisation in children and young people (CYP) and to explore predictors of segment membership.DesignRetrospective cohort study.SettingRoutinely collected primary and secondary healthcare data in Northwest London from the Discover database.Participants378,309 CYP aged 0-15 years registered to a general practice in Northwest London with one full year of follow-up.Primary and secondary outcome measuresAssignment of each participant to a segment defined by seven healthcare variables representing primary and secondary care attendances, and description of utilisation patterns by segment. Predictors of segment membership described by age, sex, ethnicity, deprivation and LTCs.ResultsParticipants were grouped into six segments based on healthcare utilisation. Three segments predominantly used primary care; two moderate utilisation segments differed in use of emergency or elective care, and a high utilisation segment, representing 16,632 (4.4%) children accounted for the highest mean presentations across all service types. The two smallest segments, representing 13.3% of the population, accounted for 62.5% of total costs. Younger age, residence in areas of higher deprivation, and presence of one or more LTCs were associated with membership of higher utilisation segments, but 75.0% of those in the highest utilisation segment had no LTC.ConclusionsThis article identifies six segments of healthcare utilisation in CYP and predictors of segment membership. Demographics and LTCs may not explain utilisation patterns as strongly as in adults which may limit the use of routine data in predicting ut

  • Journal article
    Liu Z, Peach R, Lawrance E, Noble A, Ungless M, Barahona Met al., 2021,

    Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service

    , Frontiers in Digital Health, Vol: 3, Pages: 1-14, ISSN: 2673-253X

    The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.

  • Journal article
    Ming DK, Myall AC, Hernandez B, Wei├če AY, Peach RL, Barahona M, Rawson TM, Holmes AHet al., 2021,

    Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin

    , BMC Infectious Diseases, Vol: 21

    Background: To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making. Methods: Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital. Results: CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant. Conclusions: Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies.

  • Conference paper
    Liu Z, Barahona M, 2021,

    Similarity measure for sparse time course data based on Gaussian processes

    , Uncertainty in Artificial Intelligence 2021, Publisher: PMLR, Pages: 1332-1341

    We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.

  • Journal article
    Simoes Monteiro de Marvao A, McGurk K, Zheng S, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes T, Savioli N, Halliday B, Xu X, Buchan R, Baksi A, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis A, Zhang X, Jang M, Berry A, Pantazis A, Barton P, Rueckert D, Prasad S, Walsh R, Ho C, Cook S, Ware J, O'Regan Det al., 2021,

    Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy

    , Journal of the American College of Cardiology, Vol: 78, Pages: 1097-1110, ISSN: 0735-1097

    Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in thegeneral population.Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to thepresence of rare variants in sarcomere-encoding genes amongst middle-aged adults.Methods: We analysed whole exome sequencing and cardiac magnetic resonance (CMR)imaging in UK Biobank participants stratified by sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associatedsarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and theprevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk ofdeath or major adverse cardiac events compared to controls (HR 1.69, 95% CI 1.38 to 2.07,p<0.001), mainly due to heart failure endpoints (HR 4.23, 95% CI 3.07 to 5.83, p<0.001). In21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increasein left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) buthypertrophy (≥13mm) was only present in 18.4% (n=9/49, 95% CI 9 to 32%). SARC-HCMP/LP were still associated with heart failure after adjustment for wall thickness (HR 6.74,95% CI 2.43 to 18.7, p<0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have lowaggregate penetrance for overt HCM but are associated with increased risk of adversecardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absoluteevent rates are low, identification of these variants may enhance risk stratification beyondfamilial disease.

  • Journal article
    Mersmann S, Stromich L, Song F, Wu N, Vianello F, Barahona M, Yaliraki Set al., 2021,

    ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules

    , Nucleic Acids Research, Vol: 49, Pages: W551-W558, ISSN: 0305-1048

    The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at:

  • Journal article
    Chrysostomou S, Roy R, Prischi F, Thamlikitkul L, Chapman KL, Mufti U, Peach R, Ding L, Hancock D, Moore C, Molina-Arcas M, Mauri F, Pinato DJ, Abrahams JM, Ottaviani S, Castellano L, Giamas G, Pascoe J, Moonamale D, Pirrie S, Gaunt C, Billingham L, Steven NM, Cullen M, Hrouda D, Winkler M, Post J, Cohen P, Salpeter SJ, Bar V, Zundelevich A, Golan S, Leibovici D, Lara R, Klug DR, Yaliraki SN, Barahona M, Wang Y, Downward J, Skehel JM, Ali MMU, Seckl MJ, Pardo OEet al., 2021,

    Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer.

    , Science translational medicine, Vol: 13, ISSN: 1946-6234

    Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells. Silencing this kinase, through either RNA interference or CRISPR, sensitized tumor cells to chemotherapy and hindered metastasis in vitro and in vivo in a tail vein injection model. Drug screening revealed several floxacin antibiotics as potent RSK4 activation inhibitors, and trovafloxacin reproduced all effects of RSK4 silencing in vitro and in/ex vivo using lung cancer xenograft and genetically engineered mouse models and bladder tumor explants. Through x-ray structure determination and Markov transient and Deuterium exchange analyses, we identified the allosteric binding site and revealed how this compound blocks RSK4 kinase activation through binding to an allosteric site and mimicking a kinase autoinhibitory mechanism involving the RSK4's hydrophobic motif. Last, we show that patients undergoing chemotherapy and adhering to prophylactic levofloxacin in the large placebo-controlled randomized phase 3 SIGNIFICANT trial had significantly increased (<i>P</i> = 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.

  • Journal article
    Godoy-Lorite A, Jones N, 2021,

    Inference and influence of network structure using snapshot social behavior without network data

    , Science Advances, Vol: 7, ISSN: 2375-2548

    Population behavior, like voting and vaccination, depends on the structure of social networks. This structure can differ depending on behavior type and is typically hidden. However, we do often have behavioral data, albeit only snapshots taken at one time point. We present a method jointly inferring a model for both network structure and human behavior using only snapshot population-level behavioral data. This exploits the simplicity of a few parameter model, geometric sociodemographic network model, and a spin-based model of behavior. We illustrate, for the European Union referendum and two London mayoral elections, how the model offers both prediction and the interpretation of the homophilic inclinations of the population. Beyond extracting behavior-specific network structure from behavioral datasets, our approach yields a framework linking inequalities and social preferences to behavioral outcomes. We illustrate potential network-sensitive policies: How changes to income inequality, social temperature, and homophilic preferences might have reduced polarization in a recent election.

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