Evolutionary Pathways of the Pandemic Influenza A (H1N1) 2009 in the UK

The emergence of the influenza (H1N1) 2009 virus provided a unique opportunity to study the evolution of a pandemic virus following its introduction into the human population. Virological and clinical surveillance in the UK were comprehensive during the first and second waves of the pandemic in 2009, with extensive laboratory confirmation of infection allowing a detailed sampling of representative circulating viruses. We sequenced the complete coding region of the haemagglutinin (HA) segment of 685 H1N1 pandemic viruses selected without bias during two waves of pandemic in the UK (April-December 2009). Phylogenetic analysis showed that although temporal accumulation of amino acid changes was observed in the HA sequences, the overall diversity was less than that typically seen for seasonal influenza A H1N1 or H3N2. There was co-circulation of multiple variants as characterised by signature amino acid changes in the HA. A specific substitution (S203T) became predominant both in UK and global isolates. No antigenic drift occurred during 2009 as viruses with greater than four-fold reduction in their haemagglutination inhibition (HI) titre (“low reactors”) were detected in a low proportion (3%) and occurred sporadically. Although some limited antigenic divergence in viruses with four-fold reduction in HI titre might be related to the presence of 203T, additional studies are needed to test this hypothesis

Osteoarticular Infections in Pediatric Hospitals in Europe: A Prospective Cohort Study From the EUCLIDS Consortium

BACKGROUND: Pediatric osteoarticular infections (POAIs) are serious diseases requiring early diagnosis and treatment. METHODS: In this prospective multicenter cohort study, children with POAIs were selected from the European Union Childhood Life-threatening Infectious Diseases Study (EUCLIDS) database to analyze their demographic, clinical, and microbiological data. RESULTS: A cohort of 380 patients with POAIs, 203 with osteomyelitis (OM), 158 with septic arthritis (SA), and 19 with both OM and SA, was analyzed. Thirty-five patients were admitted to the Pediatric Intensive Care Unit; out of these, six suffered from shock, one needed an amputation of the right foot and of four left toes, and two had skin transplantation. According to the Pediatric Overall Performance Score, 36 (10.5%) showed a mild overall disability, 3 (0.8%) a moderate, and 1 (0.2%) a severe overall disability at discharge. A causative organism was detected in 65% (247/380) of patients. Staphylococcus aureus (S. aureus) was identified in 57.1% (141/247) of microbiological confirmed cases, including 1 (0.7%) methicillin-resistant S. aureus (MRSA) and 6 (4.2%) Panton-Valentine leukocidin (PVL)-producing S. aureus, followed by Group A Streptococcus (18.2%) and Kingella kingae (8.9%). K. kingae and PVL production in S. aureus were less frequently reported than expected from the literature. CONCLUSION: POAIs are associated with a substantial morbidity in European children, with S. aureus being the major detected pathogen. In one-third of patients, no causative organism is identified. Our observations show an urgent need for the development of a vaccine against S. aureus and for the development of new microbiologic diagnostic guidelines for POAIs in European pediatric hospitals

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Hemostasis Proteins in Invasive Meningococcal and Nonmeningococcal Infections: A Prospective Multicenter Study

OBJECTIVES: We aimed to describe the variation of hemostasis proteins in children with bacterial infections due to different pathogens (Neisseria meningitidis, Streptococcus pneumoniae, Staphylococcus aureus, and group A streptococcus [GAS]) and to study hemostasis proteins in relation to mortality. DESIGN: Preplanned analysis in prospective cohort study. SETTING: Hospitals in five European countries (Austria, The Netherlands, Spain, Switzerland, and the United Kingdom). PATIENTS: Admitted children (2012-2016) with community-acquired infections due to meningococci (n = 83), pneumococci (n = 64), S. aureus (n = 50), and GAS (n = 44) with available serum samples collected less than 48 hours after admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fibronectin, plasminogen activator inhibitor type 1 (PAI-1), thrombomodulin, and a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS-13) were measured in serum in 2019-2020. Additionally, von Willebrand factor, protein C, protein S, and factor IX were measured in citrate plasma available from a subset of patients. Outcome measures included in-hospital mortality and disease severity (need for ventilation/inotropes, Pediatric Index of Mortality score). Of 241 children, 21 (8.7%) died and 177 (73.5%) were admitted to PICU. Mortality rate was similar for the pathogen groups. Levels of fibronectin and thrombomodulin differed for the different pathogens (p < 0.05). Fibronectin levels were lower in GAS infections than in S. pneumoniae and S. aureus infections but did not differ from meningococcal infections. Thrombomodulin levels in meningococcal infections were higher than in S. aureus and pneumococcal infections. Overall, the area under the curve for mortality was 0.81 (95% CI, 0.70-0.92) for thrombomodulin and 0.78 (95% CI, 0.69-0.88) for ADAMTS-13. The association of each hemostasis protein did not vary across pathogens for any of the outcome measures. CONCLUSIONS: Hemostatic disturbances in childhood bacterial infections are not limited to meningococcal sepsis but occur with a comparable severity across nonmeningococcal infections. High thrombomodulin and high ADAMTS-13 had good discriminative ability for mortality. Our results emphasize the importance of hemostatic disturbances in meningococcal and nonmeningococcal pediatric bacterial infections

Hemostasis Proteins in Invasive Meningococcal and Nonmeningococcal Infections: A Prospective Multicenter Study.

ObjectivesWe aimed to describe the variation of hemostasis proteins in children with bacterial infections due to different pathogens (Neisseria meningitidis, Streptococcus pneumoniae, Staphylococcus aureus, and group A streptococcus [GAS]) and to study hemostasis proteins in relation to mortality.DesignPreplanned analysis in prospective cohort study.SettingHospitals in five European countries (Austria, The Netherlands, Spain, Switzerland, and the United Kingdom).PatientsAdmitted children (2012-2016) with community-acquired infections due to meningococci (n = 83), pneumococci (n = 64), S. aureus (n = 50), and GAS (n = 44) with available serum samples collected less than 48 hours after admission.InterventionsNone.Measurements and main resultsFibronectin, plasminogen activator inhibitor type 1 (PAI-1), thrombomodulin, and a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS-13) were measured in serum in 2019-2020. Additionally, von Willebrand factor, protein C, protein S, and factor IX were measured in citrate plasma available from a subset of patients. Outcome measures included in-hospital mortality and disease severity (need for ventilation/inotropes, Pediatric Index of Mortality score). Of 241 children, 21 (8.7%) died and 177 (73.5%) were admitted to PICU. Mortality rate was similar for the pathogen groups. Levels of fibronectin and thrombomodulin differed for the different pathogens (p ConclusionsHemostatic disturbances in childhood bacterial infections are not limited to meningococcal sepsis but occur with a comparable severity across nonmeningococcal infections. High thrombomodulin and high ADAMTS-13 had good discriminative ability for mortality. Our results emphasize the importance of hemostatic disturbances in meningococcal and nonmeningococcal pediatric bacterial infections

Hemostasis Proteins in Invasive Meningococcal and Nonmeningococcal Infections: A Prospective Multicenter Study

OBJECTIVES: We aimed to describe the variation of hemostasis proteins in children with bacterial infections due to different pathogens (Neisseria meningitidis, Streptococcus pneumoniae, Staphylococcus aureus, and group A streptococcus [GAS]) and to study hemostasis proteins in relation to mortality. DESIGN: Preplanned analysis in prospective cohort study. SETTING: Hospitals in five European countries (Austria, The Netherlands, Spain, Switzerland, and the United Kingdom). PATIENTS: Admitted children (2012-2016) with community-acquired infections due to meningococci (n = 83), pneumococci (n = 64), S. aureus (n = 50), and GAS (n = 44) with available serum samples collected less than 48 hours after admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fibronectin, plasminogen activator inhibitor type 1 (PAI-1), thrombomodulin, and a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS-13) were measured in serum in 2019-2020. Additionally, von Willebrand factor, protein C, protein S, and factor IX were measured in citrate plasma available from a subset of patients. Outcome measures included in-hospital mortality and disease severity (need for ventilation/inotropes, Pediatric Index of Mortality score). Of 241 children, 21 (8.7%) died and 177 (73.5%) were admitted to PICU. Mortality rate was similar for the pathogen groups. Levels of fibronectin and thrombomodulin differed for the different pathogens (p < 0.05). Fibronectin levels were lower in GAS infections than in S. pneumoniae and S. aureus infections but did not differ from meningococcal infections. Thrombomodulin levels in meningococcal infections were higher than in S. aureus and pneumococcal infections. Overall, the area under the curve for mortality was 0.81 (95% CI, 0.70-0.92) for thrombomodulin and 0.78 (95% CI, 0.69-0.88) for ADAMTS-13. The association of each hemostasis protein did not vary across pathogens for any of the outcome measures. CONCLUSIONS: Hemostatic disturbances in childhood bacterial infections are not limited to meningococcal sepsis but occur with a comparable severity across nonmeningococcal infections. High thrombomodulin and high ADAMTS-13 had good discriminative ability for mortality. Our results emphasize the importance of hemostatic disturbances in meningococcal and nonmeningococcal pediatric bacterial infections

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine