Bark anatomy, chemical composition and ethanol-water extract composition of Anadenanthera peregrina and Anadenanthera colubrina

The bark of Anadenanthera peregrina (L.) Speg and Anadenanthera colubrina (Vell.) Brenan were characterized in relation to anatomical and chemical features. The barks were similar and included a thin conducting phloem, a largely dilated and sclerified non-conducting phloem, and a rhyridome with periderms with thin phellem interspersed by cortical tissues. Only small differences between species were observed that cannot be used alone for taxonomic purposes. The summative chemical composition of A. peregrina and A. colubrina was respectively: 8.2% and 7.7% ash; 28.8% and 29.3% extractives; 2.4% and 2.6% suberin; and 18.9% lignin. The monosaccharide composition showed the predominance of glucose (on average 82% of total neutral sugars) and of xylose (9%). The ethanol-water extracts of A. peregrina and A. colubrina barks included a high content of phenolics, respectively: total phenolics 583 and 682 mg GAE/g extract; 148 and 445 mg CE/g extract; tannins 587 and 98 mg CE/g extract. The antioxidant activity was 238 and 269 mg Trolox/g extract. The barks of the Anadenanthera species are a potential source of polar extractives that will represent an important valorization and therefore contribute to improve the overall economic potential and sustainability of A. peregrina and A. colubrinainfo:eu-repo/semantics/publishedVersio

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

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

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

Life-threatening infections in children in Europe (the EUCLIDS Project): a prospective cohort study

Background: Sepsis and severe focal infections represent a substantial disease burden in children admitted to hospital. We aimed to understand the burden of disease and outcomes in children with life-threatening bacterial infections in Europe. Methods: The European Union Childhood Life-threatening Infectious Disease Study (EUCLIDS) was a prospective, multicentre, cohort study done in six countries in Europe. Patients aged 1 month to 18 years with sepsis (or suspected sepsis) or severe focal infections, admitted to 98 participating hospitals in the UK, Austria, Germany, Lithuania, Spain, and the Netherlands were prospectively recruited between July 1, 2012, and Dec 31, 2015. To assess disease burden and outcomes, we collected demographic and clinical data using a secured web-based platform and obtained microbiological data using locally available clinical diagnostic procedures. Findings: 2844 patients were recruited and included in the analysis. 1512 (53·2%) of 2841 patients were male and median age was 39·1 months (IQR 12·4–93·9). 1229 (43·2%) patients had sepsis and 1615 (56·8%) had severe focal infections. Patients diagnosed with sepsis had a median age of 27·6 months (IQR 9·0–80·2), whereas those diagnosed with severe focal infections had a median age of 46·5 months (15·8–100·4; p<0·0001). Of 2844 patients in the entire cohort, the main clinical syndromes were pneumonia (511 [18·0%] patients), CNS infection (469 [16·5%]), and skin and soft tissue infection (247 [8·7%]). The causal microorganism was identified in 1359 (47·8%) children, with the most prevalent ones being Neisseria meningitidis (in 259 [9·1%] patients), followed by Staphylococcus aureus (in 222 [7·8%]), Streptococcus pneumoniae (in 219 [7·7%]), and group A streptococcus (in 162 [5·7%]). 1070 (37·6%) patients required admission to a paediatric intensive care unit. Of 2469 patients with outcome data, 57 (2·2%) deaths occurred: seven were in patients with severe focal infections and 50 in those with sepsis. Interpretation: Mortality in children admitted to hospital for sepsis or severe focal infections is low in Europe. The disease burden is mainly in children younger than 5 years and is largely due to vaccine-preventable meningococcal and pneumococcal infections. Despite the availability and application of clinical procedures for microbiological diagnosis, the causative organism remained unidentified in approximately 50% of patients

Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process

© 2016, The International Biometric Society For the classical, homoscedastic measurement error model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to measurement error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects modeling process, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. This general model includes classical measurement errors, Berkson measurement errors, mixtures of Berkson and classical errors and problems that are not measurement error problems, but also cases where the data-generating process for true exposure is a complex, nonlinear random effects modeling process. The methods are illustrated using the National Institutes of Health–AARP Diet and Health Study where the latent variable is a dietary pattern score called the Healthy Eating Index-2005. We also show how our general model includes methods used in radiation epidemiology as a special case. Simulations are used to illustrate the methods