Predictors of myocardial fibrosis and response to anti-fibrotic therapy in heart failure with preserved ejection fraction

AbstractMyocardial fibrosis, measured using magnetic resonance extracellular volume (ECV), associates with adverse outcome in heart failure with preserved ejection fraction (HFpEF). In the PIROUETTE (The Pirfenidone in Patients with Heart Failure and Preserved Left Ventricular Ejection Fraction) trial, the novel anti-fibrotic agent pirfenidone reduced myocardial fibrosis. We sought to identify baseline characteristics that associate with myocardial fibrotic burden, the change in myocardial fibrosis over a year, and predict response to pirfenidone in patients with HFpEF. Amongst patients enrolled in the PIROUETTE trial (n = 107), linear regression models were used to assess the relationship between baseline variables and baseline myocardial ECV, with change in myocardial ECV adjusting for treatment allocation, and to identify variables that modified the pirfenidone treatment effect. Body mass index, left atrial reservoir strain, haemoglobin and aortic distensibility were associated with baseline ECV in stepwise modelling, and systolic blood pressure, and log N-terminal pro B-type natriuretic peptide were associated with baseline ECV in clinically-guided modelling. QRS duration, left ventricular mass and presence of an infarct at baseline were associated with an increase in ECV from baseline to week 52. Whilst QRS duration, presence of an infarct, global longitudinal strain and left atrial strain modified the treatment effect of pirfenidone when considered individually, no variable modified treatment effect on multivariable modelling. Baseline characteristics were identified that associate with myocardial fibrosis and predict change in myocardial fibrosis. No variables that independently modify the treatment effect of pirfenidone were identified (PIROUETTE, NCT02932566).</jats:p

Cybersecurity in motion: A survey of challenges and requirements for future test facilities of CAVs

The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs

Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs

The Impact of Delayed Treatment of Uncomplicated \u3ci\u3eP. falciparum\u3c/i\u3e Malaria on Progression to Severe Malaria: A Systematic Review and a Pooled Multicentre Individual-Patient Meta-Analysis

BACKGROUND: Delay in receiving treatment for uncomplicated malaria (UM) is often reported to increase the risk of developing severe malaria (SM), but access to treatment remains low in most high-burden areas. Understanding the contribution of treatment delay on progression to severe disease is critical to determine how quickly patients need to receive treatment and to quantify the impact of widely implemented treatment interventions, such as \u27test-and-treat\u27 policies administered by community health workers (CHWs). We conducted a pooled individual-participant meta-analysis to estimate the association between treatment delay and presenting with SM. METHODS AND FINDINGS: A search using Ovid MEDLINE and Embase was initially conducted to identify studies on severe Plasmodium falciparum malaria that included information on treatment delay, such as fever duration (inception to 22nd September 2017). Studies identified included 5 case-control and 8 other observational clinical studies of SM and UM cases. Risk of bias was assessed using the Newcastle-Ottawa scale, and all studies were ranked as \u27Good\u27, scoring ≥7/10. Individual-patient data (IPD) were pooled from 13 studies of 3,989 (94.1% aged \u3c15 years) SM patients and 5,780 (79.6% aged \u3c15 years) UM cases in Benin, Malaysia, Mozambique, Tanzania, The Gambia, Uganda, Yemen, and Zambia. Definitions of SM were standardised across studies to compare treatment delay in patients with UM and different SM phenotypes using age-adjusted mixed-effects regression. The odds of any SM phenotype were significantly higher in children with longer delays between initial symptoms and arrival at the health facility (odds ratio [OR] = 1.33, 95% CI: 1.07-1.64 for a delay of \u3e24 hours versus ≤24 hours; p = 0.009). Reported illness duration was a strong predictor of presenting with severe malarial anaemia (SMA) in children, with an OR of 2.79 (95% CI:1.92-4.06; p \u3c 0.001) for a delay of 2-3 days and 5.46 (95% CI: 3.49-8.53; p \u3c 0.001) for a delay of \u3e7 days, compared with receiving treatment within 24 hours from symptom onset. We estimate that 42.8% of childhood SMA cases and 48.5% of adult SMA cases in the study areas would have been averted if all individuals were able to access treatment within the first day of symptom onset, if the association is fully causal. In studies specifically recording onset of nonsevere symptoms, long treatment delay was moderately associated with other SM phenotypes (OR [95% CI] \u3e3 to ≤4 days versus ≤24 hours: cerebral malaria [CM] = 2.42 [1.24-4.72], p = 0.01; respiratory distress syndrome [RDS] = 4.09 [1.70-9.82], p = 0.002). In addition to unmeasured confounding, which is commonly present in observational studies, a key limitation is that many severe cases and deaths occur outside healthcare facilities in endemic countries, where the effect of delayed or no treatment is difficult to quantify. CONCLUSIONS: Our results quantify the relationship between rapid access to treatment and reduced risk of severe disease, which was particularly strong for SMA. There was some evidence to suggest that progression to other severe phenotypes may also be prevented by prompt treatment, though the association was not as strong, which may be explained by potential selection bias, sample size issues, or a difference in underlying pathology. These findings may help assess the impact of interventions that improve access to treatment

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study.

BACKGROUND Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. INTERPRETATION This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. FUNDING European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation

A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies

Copyright © 2021 Nijman, Oostenbrink, Moll, Casals-Pascual, von Both, Cunnington, De, Eleftheriou, Emonts, Fink, van der Flier, de Groot, Kaforou, Kohlmaier, Kuijpers, Lim, Maconochie, Paulus, Martinon-Torres, Pokorn, Romaine, Calle, Schlapbach, Smit, Tsolia, Usuf, Wright, Yeung, Zavadska, Zenz, Levin, Herberg, Carrol and the PERFORM consortium (Personalized Risk assessment in febrile children to optimize Real-life Management across the European Union).Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI. Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0– <16 years: the Alder Hey emergency department (n = 1,120), Alder Hey pediatric intensive care unit (n = 355), Erasmus emergency department (n = 1,993), Maasstad emergency department (n = 714) and St. Mary's hospital (n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination. Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis (“definite bacterial” category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis (“definite viral” category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of “definite bacterial” vs. “definite viral” following the PERFORM algorithm than using the “SBI” vs. “non-SBI” classification; summary AUC for PCT was 0.77 (95% CI 0.72–0.82) vs. 0.70 (95% CI 0.65–0.75); for NGAL this was 0.80 (95% CI 0.69–0.91) vs. 0.70 (95% CI 0.58–0.81); for resistin this was 0.68 (95% CI 0.61–0.75) vs. 0.64 (0.58–0.69) The three biomarkers combined had summary AUC of 0.83 (0.77–0.89) for “definite bacterial” vs. “definite viral” infections and 0.71 (0.67–0.74) for “SBI” vs. “non-SBI.” Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies.publishersversionPeer reviewe

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature.

BackgroundAppropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.MethodsA multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.FindingsWe identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.ConclusionsOur data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.FundingEuropean Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC