Nosocomial influenza in south-western Swiss hospitals during two seasonal epidemics: an observational study.

BACKGROUND: In Switzerland each year, influenza leads to between 112,000 and 275,000 medical consultations. Data on nosocomial influenza infection are limited. AIM: To describe nosocomial cases of seasonal influenza in south-western Switzerland. METHODS: This study was conducted during two seasonal influenza epidemics from 2016 to 2018 in 27 acute care public hospitals in south-western Switzerland. During these two time-periods, every patient hospitalized for >72 h who was positively screened by reverse transcription-polymerase chain reaction or antigen detection for influenza was included in the survey. Characteristics of patients included age, sex, and comorbidities. Included patients were followed up until discharge or death. Complications and administration of antineuraminidases and/or antibiotics were registered. FINDINGS: The median influenza vaccine coverage of healthcare workers was 40%. In all, 836 patients were included (98% with type A influenza virus in 2016-2017; 77% with type B virus in 2017-2018). Most patients (81%) had an unknown vaccine status. Overall, the incidence of nosocomial influenza was 0.5 per 100 admissions (0.35 per 1000 patient-days). The most frequent comorbidities were diabetes (20%), chronic respiratory diseases (19%), and malnutrition (17%). Fever (77%) and cough (66%) were the most frequent symptoms. Seventy-one percent of patients received antineuraminidases, 28% received antibiotics. Infectious complications such as pneumonia were reported in 9%. Overall, the all-cause mortality was 6%. CONCLUSION: The occurrence of nosocomial influenza underlines the importance of vaccinating patients and healthcare workers, rapidly recognizing community- or hospital-acquired cases, and applying adequate additional measures to prevent dissemination, including the timely administration of antineuraminidases to avoid antibiotic use (and misuse).

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA.

Social integration of robots into groups of cockroaches to control self-organized choices.

Collective behavior based on self-organization has been shown in group-living animals from insects to vertebrates. These findings have stimulated engineers to investigate approaches for the coordination of autonomous multirobot systems based on self-organization. In this experimental study, we show collective decision-making by mixed groups of cockroaches and socially integrated autonomous robots, leading to shared shelter selection. Individuals, natural or artificial, are perceived as equivalent, and the collective decision emerges from nonlinear feedbacks based on local interactions. Even when in the minority, robots can modulate the collective decision-making process and produce a global pattern not observed in their absence. These results demonstrate the possibility of using intelligent autonomous devices to study and control self-organized behavioral patterns in group-living animals.