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Background: The human parainfluenza virus 3 (HPIV-3) outbreak at the haemato-oncology ward of the Maastricht University Medical Centre in the summer of 2016. Aim: To describe an effective strategy to control the largest reported HPIV-3 outbreak at an adult haematology–oncology ward in the Netherlands by implementing infection control measures and molecular epidemiology investigation. Methods: Clinical, patient and diagnostic data were both pro- and retrospectively collected. HPIV-3 real-time polymerase chain reaction (HPIV-3 RT-PCR) was validated using oropharyngeal rinse samples. Screening of all new and admitted patients was implemented to identify asymptomatic infection or prolonged shedding of HPIV-3 allowing cohort isolation. Findings: The HPIV-3 outbreak occurred between 9 July and 28 September 2016 and affected 53 patients. HPIV-3 RT-PCR on oropharyngeal rinse samples demonstrated an up to 10-fold higher sensitivity compared with pharyngeal swabs. Monitoring showed that at first positive PCR, 20 patients (38%) were asymptomatic (of which 11 remained asymptomatic) and the average duration of shedding was 14 days (range 1–58). Asymptomatic patients had lower viral load, shorter period of viral shedding (≤14 days) and were mostly immune-competent oncology patients. The outbreak was under control five weeks after implementation of screening of asymptomatic patients. Conclusion: Implementation of a sensitive screening method identified both symptomatic and asymptomatic patients which had lower viral loads and allowed early cohort isolation. This is especially important in a ward that combines patients with varying immune status, because both immunocompromised and immune-competent patients are likely to spread the HPIV-3 virus, either through prolonged shedding or through asymptomatic course of disease.

BACKGROUND. Respiratory syncytial virus (RSV) is an important cause of acute pulmonary disease and one of the last remaining major infections of childhood for which there is no vaccine. CD4+ T cells play a key role in antiviral immunity, but they have been little studied in the human lung.METHODS. Healthy adult volunteers were inoculated i.n. with RSV A Memphis 37. CD4+ T cells in blood and the lower airway were analyzed by flow cytometry and immunohistochemistry. Bronchial soluble mediators were measured using quantitative PCR and MesoScale Discovery. Epitope mapping was performed by IFN-γ ELISpot screening, confirmed by in vitro MHC binding.RESULTS. Activated CD4+ T cell frequencies in bronchoalveolar lavage correlated strongly with local C-X-C motif chemokine 10 levels. Thirty-nine epitopes were identified, predominantly toward the 3′ end of the viral genome. Five novel MHC II tetramers were made using an immunodominant EFYQSTCSAVSKGYL (F-EFY) epitope restricted to HLA-DR4, -DR9, and -DR11 (combined allelic frequency: 15% in Europeans) and G-DDF restricted to HLA-DPA1*01:03/DPB1*02:01 and -DPA1*01:03/DPB1*04:01 (allelic frequency: 55%). Tetramer labeling revealed enrichment of resident memory CD4+ T (Trm) cells in the lower airway; these Trm cells displayed progressive differentiation, downregulation of costimulatory molecules, and elevated CXCR3 expression as infection evolved.CONCLUSIONS. Human infection challenge provides a unique opportunity to study the breadth of specificity and dynamics of RSV-specific T-cell responses in the target organ, allowing the precise investigation of Trm recognizing novel viral antigens over time. The new tools that we describe enable precise tracking of RSV-specific CD4+ cells, potentially accelerating the development of effective vaccines.TRIAL REGISTRATION. ClinicalTrials.gov NCT02755948.FUNDING. Medical Research Council, Wellcome Trust, National Institute for Health Research.

Respiratory syncytial virus (RSV) is the leading cause of severe acute lower respiratory tract infections in infants worldwide. Although several pattern recognition receptors (PRRs) can sense RSV-derived pathogen-associated molecular patterns (PAMPs), infection with RSV is typically associated with low to undetectable levels of type I interferons (IFNs). Multiple RSV proteins can hinder the host’s innate immune response. The main players are NS1 and NS2 which suppress type I IFN production and signalling in multiple ways. The recruitment of innate immune cells and the production of several cytokines are reduced by RSV G. Next, RSV N can sequester immunostimulatory proteins to inclusion bodies (IBs). N might also facilitate the assembly of a multiprotein complex that is responsible for the negative regulation of innate immune pathways. Furthermore, RSV M modulates the host’s innate immune response. The nuclear accumulation of RSV M has been linked to an impaired host gene transcription, in particular for nuclear-encoded mitochondrial proteins. In addition, RSV M might also directly target mitochondrial proteins which results in a reduced mitochondrion-mediated innate immune recognition of RSV. Lastly, RSV SH might prolong the viral replication in infected cells and influence cytokine production.

The effect of viral infection on nasopharyngeal carriage of Streptococcus pneumoniae during childhood is not well known. We studied dynamics of pneumococcal colonization by quantitative PCR during the natural course of viral bronchiolitis. At time of admission, 47(47%) of 100 patients with bronchiolitis carried pneumococci. In patients with viral bronchiolitis who did not receive antibiotics, pneumococcal load decreased from time of admission to discharge (n = 35, cycle threshold 23 vs. 25, P = 0.0017) and from discharge to follow-up (n = 22, cycle threshold 25 vs. 40, P = 0.003). We conclude that viral respiratory infection is negatively associated with pneumococcal colonization of the upper airways. (C) 2016 Wiley Periodicals, Inc.

Characterization of virosomes, in late stage preclinical development as vaccines for Respiratory Syncytial Virus (RSV), with a membrane-incorporated synthetic monophosphoryl lipid A, 3D-PHADA (R) adjuvant.Virosomes were initially formed by contacting a lipid film containing 3D-PHADA (R) with viral membranes solubilized with the short chain phospholipid DCPC, followed by dialysis, later by adding solubilized 3D-PHAD to viral membranes, or to preformed virosomes from DMSO.Virosomes formed from lipid films contained the membrane glycoproteins G and F, at similar F to G ratios but lower concentrations than in virus, and the added lipids, but only a fraction of the 3D-PHADA (R). By single particle tracking (SPT), the virosome size distribution resembled that seen by cryo-electron microscopy, but dynamic light scattering showed much larger particles. These differences were caused by small virosome aggregates. Measured by SPT, virosomes were stable for 300 days. 3DPHAD A (R) incorporation in virosomes could be enhanced by providing the adjuvant from DCPC solubilized stock, but also by adding DMSO dissolved adjuvant to pre-formed virosomes. Virosomes with 0.1 mg/mg of 3D-PHADA (R)/viral protein from DMSO induced antibody titers similar to those by virosomes containing 0.2 mg/mg of DCPC-solubilized 3D-PHADA (R).Stable 3D-PHADA (R) adjuvanted RSV virosomes can be formulated.

The completion of the human genome and the advancement of high-throughput technologies have enable the quantification of thousands of genes for precision medicine. The problem with gene expression data is that the number of genes (as variables) greatly supersedes the number of samples thereby rendering regular statistical models that often require large number of samples than variables, unsatisfactory. As such, several machine learning algorithms (functions) have been proposed in the literature to tackle this problem of small sample size relative to the number of variables, commonly referred to as the “curse of dimensionality”. Nevertheless, there is no universal algorithm that performs best on all gene expression data. The varying performance of these algorithms across datasets is a clear indication that there are data characteristics associated with the performance of the algorithms. In order to determine an optimal function for a given gene expression data, several algorithms are often compared and the one with the smallest cross-validated error is chosen. This approach leads to selection bias because an algorithm might have the smallest cross-validated error by chance. To combat this, a number of selection bias correction methods have been proposed but no such method is guaranteed to be effective when several least optimal functions are compared on a dataset with a small sample size. Alternative approaches utilize combined input of several algorithms (super-learners) with the goal of improving predictions. Such approaches are hardly accepted in medical applications because they are often considered black boxes whose models are hard to interpret and they utilize the entire genome instead of a selected profile, making practical application time consuming and costly. Hence traditional algorithms that can perform variable selection to yield a gene signature and interpretable models are often preferred over super-learners but the question which of these algorithms is optimal for a given data remains unanswered. In this thesis, we have identified gene expression data characteristics that associate with the performance of often used traditional machine learning algorithms, using publicly available microarray gene expression data. With the identified data characteristics we systematically varied the variables and assessed their associative effects to the performance of these functions using simulations. Additionally, we analyzed our simulated results to provide predictive models for selecting an optimal algorithm for diagnostic or prognostic analysis on any given data, with little or no bias. Application of our models on several real-life gene expression data showed high correlations between predicted and actually achieved performance of the functions. One of such models was used to select an optimal algorithm that was subsequently utilized to identify and validate prognostic biomarkers for disease severity in respiratory syncytial virus (RSV) infected infants. The identified 84 genes signature might serve as the basis for the management of RSV disease in pediatric wards.

The completion of the human genome and the advancement of high-throughput technologies have enable the quantification of thousands of genes for precision medicine. The problem with gene expression data is that the number of genes (as variables) greatly supersedes the number of samples thereby rendering regular statistical models that often require large number of samples than variables, unsatisfactory. As such, several machine learning algorithms (functions) have been proposed in the literature to tackle this problem of small sample size relative to the number of variables, commonly referred to as the “curse of dimensionality”. Nevertheless, there is no universal algorithm that performs best on all gene expression data. The varying performance of these algorithms across datasets is a clear indication that there are data characteristics associated with the performance of the algorithms. In order to determine an optimal function for a given gene expression data, several algorithms are often compared and the one with the smallest cross-validated error is chosen. This approach leads to selection bias because an algorithm might have the smallest cross-validated error by chance. To combat this, a number of selection bias correction methods have been proposed but no such method is guaranteed to be effective when several least optimal functions are compared on a dataset with a small sample size. Alternative approaches utilize combined input of several algorithms (super-learners) with the goal of improving predictions. Such approaches are hardly accepted in medical applications because they are often considered black boxes whose models are hard to interpret and they utilize the entire genome instead of a selected profile, making practical application time consuming and costly. Hence traditional algorithms that can perform variable selection to yield a gene signature and interpretable models are often preferred over super-learners but the question which of these algorithms is optimal for a given data remains unanswered. In this thesis, we have identified gene expression data characteristics that associate with the performance of often used traditional machine learning algorithms, using publicly available microarray gene expression data. With the identified data characteristics we systematically varied the variables and assessed their associative effects to the performance of these functions using simulations. Additionally, we analyzed our simulated results to provide predictive models for selecting an optimal algorithm for diagnostic or prognostic analysis on any given data, with little or no bias. Application of our models on several real-life gene expression data showed high correlations between predicted and actually achieved performance of the functions. One of such models was used to select an optimal algorithm that was subsequently utilized to identify and validate prognostic biomarkers for disease severity in respiratory syncytial virus (RSV) infected infants. The identified 84 genes signature might serve as the basis for the management of RSV disease in pediatric wards.

The effect of viral infection on nasopharyngeal carriage of Streptococcus pneumoniae during childhood is not well known. We studied dynamics of pneumococcal colonization by quantitative PCR during the natural course of viral bronchiolitis. At time of admission, 47 (47%) of 100 patients with bronchiolitis carried pneumococci. In patients with viral bronchiolitis who did not receive antibiotics, pneumococcal load decreased from time of admission to discharge (n = 35, cycle threshold 23 vs. 25, P = 0.0017) and from discharge to follow-up (n = 22, cycle threshold 25 vs. 40, P = 0.003). We conclude that viral respiratory infection is negatively associated with pneumococcal colonization of the upper airways. Pediatr Pulmonol. 2016;51:863–867.

Spanish 'Fondo de Investigacion Sanitaria' 'Secretaria de Estado para la Cooperacion Internacional' Regional Government of Murcia, Spain Foundation to Combat Asthma (Stichting Astmabestrijding), Amsterdam, The Netherlands Male gender, asthmatic heredity, perinatal tobacco smoke exposure and respiratory infections have been associated with wheeze in the first years of life, among other risk factors. However, information about what factors modify the time to the first episode of wheeze in infants is lacking. The present study analyses which factors are associated with shorter time to the first episode of wheeze in infants. Parents of 11- to 24-month-old children were surveyed when attending their health-care centres for a control visit. They answered a questionnaire including the age in months when a first wheeze episode (if any) had occurred (outcome variable). The study was performed in 14 centres in Latin America (LA) and in 8 centres in Europe (EU) (at least 1,000 infants per centre). Factors known to be associated with wheezing in the cohort were included in a survival analysis (Cox proportional hazards model). Summary hazard ratios adjusted for all risk factors (aHR) were calculated using the meta-analysis approach with random effects. A total of 15,067 infants had experienced wheezing at least once, out of 35,049 surveyed. Male gender in LA (aHR 1.05, 95% confidence interval (CI) 1.00-1.10, P = 0.047), parental asthma in LA and EU (aHR 1.05, 95% CI 1.00-1.11, P = 0.037), infant eczema in EU (aHR 1.25, 95% CI 1.12-1.39, P < 0.001) and having a cold during the first 3 months in LA and EU (aHR 1.97, 95% CI 1.90-2.04, P < 0.001), in LA (aHR 1.98, 95% CI 1.90-2.06, P < 0.001) and in EU (aHR 1.91, 95% CI 1.75-2.09, P < 0.001) were associated with a shorter period of time to the first episode. Breast feeding for at least 3 months was associated with a longer period, only in LA (aHR 0.91, 95% CI 0.86-0.96, P < 0.001). Cold symptoms during the first 3 months is the most consistent factor shortening the time to the first episode of wheezing; breast feeding for >= 3 months delays it only in LA, whereas eczema shortens it only in EU. Avoiding a common cold in the first months of life could be a good strategy to delay the first wheeze episode; however, cohort studies will help to elucidate this association. Univ Murcia, Virgen de la Arrixaca Univ Childrens Hosp, Pediat Resp & Allergy Unit, Murcia, Spain Univ Santiago Chile USACH, Dept Pediat Resp Med, Hosp El Pino, Santiago, Chile Fed Univ Sao Paulo UNIFESP, Dept Pediat, Div Allergy Clin Immunol & Rheumatol, Sao Paulo, Brazil Isala Klin, Princess Amalia Childrens Clin, Zwolle, Netherlands Univ Med Ctr Groningen, UMCG Postgrad Sch Med, NL-9713 AV Groningen, Netherlands Univ Murcia, Dept Pediat, Murcia, Spain IMIB Biores Inst, Murcia, Spain Fed Univ Sao Paulo UNIFESP, Dept Pediat, Div Allergy Clin Immunol & Rheumatol, Sao Paulo, Brazil Spanish 'Fondo de Investigacion Sanitaria': PI050480 'Secretaria de Estado para la Cooperacion Internacional': A/3069/05 'Secretaria de Estado para la Cooperacion Internacional': A/5189/06 Web of Science

Goritzka et al. describe a role for recruited inflammatory monocytes in antiviral immunity and protection from RSV infection in mice. The authors demonstrate that this is critically dependent on the production of type I IFNs by alveolar macrophages triggered via RIG-I–like receptors, thus highlighting an important cell-extrinsic mechanism of type I IFN–mediated antiviral activity., Type I interferons (IFNs) are important for host defense from viral infections, acting to restrict viral production in infected cells and to promote antiviral immune responses. However, the type I IFN system has also been associated with severe lung inflammatory disease in response to respiratory syncytial virus (RSV). Which cells produce type I IFNs upon RSV infection and how this directs immune responses to the virus, and potentially results in pathological inflammation, is unclear. Here, we show that alveolar macrophages (AMs) are the major source of type I IFNs upon RSV infection in mice. AMs detect RSV via mitochondrial antiviral signaling protein (MAVS)–coupled retinoic acid–inducible gene 1 (RIG-I)–like receptors (RLRs), and loss of MAVS greatly compromises innate immune restriction of RSV. This is largely attributable to loss of type I IFN–dependent induction of monocyte chemoattractants and subsequent reduced recruitment of inflammatory monocytes (infMo) to the lungs. Notably, the latter have potent antiviral activity and are essential to control infection and lessen disease severity. Thus, infMo recruitment constitutes an important and hitherto underappreciated, cell-extrinsic mechanism of type I IFN–mediated antiviral activity. Dysregulation of this system of host antiviral defense may underlie the development of RSV-induced severe lung inflammation.

Background: Inhaled nitric oxide (iNO) has been used to improve oxygenation in adults and children with acute respiratory distress syndrome (ARDS) since several years. Previous studies on the effect of iNO in patients with ARDS were usually lumped together with respect to causation. Methods: We describe the acute effect of iNO in 4 children (age 4-54 months) suffering from acute, virus-induced, hypoxemic respiratory failure. Inhaled NO was administered at a dose of 20 ppm during the first hour and subsequently decreased to a dose of 5 ppm. The effects on oxygenation were assessed by changes in arterial oxygen tension and in the oxygenation index at one and 4 hours after initiating iNO therapy. Results: Inhaled NO dramatically improved oxygenation in 3 of 4 children and reduced the oxygenation index by more than 30%. One patient showed no improvement in oxygenation, but a significant reduction in pulmonary vascular resistance. Discussion: Although previous studies have failed to show any benefit of iNO on the survival rate of children with ARDS, we believe that its use can be justified if the improvement in oxygenation helps to stabilise the patient during the acute stage of hypoxemic respiratory failure.

OBJECTIVES: Human rhinoviruses (HRVs) have been suggested to play a role in the development of childhood wheezing. However, whether HRV is causally related to the development of wheezing or HRV-associated wheeze is merely an indicator of disease susceptibility is unclear. Our aim was to study the role of HRV during infancy in the development of lower respiratory disease during infancy and childhood.METHODS: In a population-based birth cohort, during the 1st year of life, nose and throat swabs were collected on a monthly basis, regardless of any symptoms. Polymerase chain reaction was used to detect an extensive panel of respiratory pathogens. Lung function was measured before 2 months of age. Information on respiratory symptoms was collected by daily questionnaires and electronic patient files.RESULTS: 1425 samples were collected in 140 infants. Both the presence of (single or multiple) pathogens (HRV equal to other pathogens) and increased respiratory system resistance were significantly associated with lower respiratory symptoms during infancy. HRV presence during infancy was not associated with the risk of wheezing at age 4, but every HRV episode with wheezing increased the risk of wheezing at age 4 (odds ratio 1.9, 1.1-3.5). This association weakened after adjustment for lung function (odds ratio 1.4, 0.7-2.9).CONCLUSIONS: HRV and other viruses are associated with lower respiratory symptoms during infancy, as well as a high presymptomatic respiratory system resistance. HRV presence during infancy is not associated with childhood wheezing, but wheeze during a HRV episode is an indicator of children at high risk for childhood wheeze, partly because of a reduced neonatal lung function.

Item does not contain fulltext BACKGROUND: : Viral upper respiratory tract infections have been described as an important factor in the development of otitis media (OM), although it is unclear whether they facilitate bacterial OM or can directly cause OM. To clarify the role of viral infections in OM, we compared the relative contribution of viruses and bacteria with the induction of inflammatory cytokine responses in the middle ear of children suffering from OM. METHODS: : Children up to 5 years of age, with recurrent or chronic episodes of OM and scheduled for ventilation tube insertion were enrolled in a prospective study. Middle ear fluids (n = 116) were collected during surgery, and quantitative polymerase chain reaction was performed to detect bacterial and viral otopathogens, that is, Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis and 15 respiratory viruses. Finally, concentrations of the inflammatory mediators interleukin (IL)-1beta, IL-6, IL-8, IL-10, IL-17a and tumor necrosis factor-alpha were determined. RESULTS: : Middle ear fluids were clustered into 4 groups, based on the detection of viruses (28%), bacteria (27%), both bacteria and viruses (27%) or no otopathogens (19%). Bacterial detection was associated with significantly elevated concentrations of cytokines compared with middle ear fluids without bacteria (P < 0.001 for all cytokines tested) in a bacterial load-dependent and species-dependent manner. In contrast, the presence of viruses was not associated with changes in cytokine values, and no synergistic effect between viral-bacterial coinfections was observed. CONCLUSIONS: : The presence of bacteria, but not viruses, is associated with an increased inflammatory response in the middle ear of children with recurrent or chronic OM.