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The influenza vaccination is recommended for all German pregnant women and health care personnel (HCP). We are the first to publish vaccination rates of mothers of hospitalized newborns and HCP in neonatal units. Between September 2016 and March 2017, data were collected in our level-III neonatology department in this descriptive multidisciplinary study, using an anonymous questionnaire. As a result, 513 persons were asked to participate, including 330 parents and 183 HCP. We received an 80.3% (412/513) response rate, 87.3% (288/330), and 67.8% (124/183) from parents and HCP, respectively. Ten percent (16/160) of mothers and 4.7% (6/127) of fathers had been vaccinated in 2016–2017 and 54.4% (87/160) mothers and 52.2% (66/127) fathers ever in their lifetime. In 2016–2017, 51.2% (21/41) of physicians had been vaccinated, 25.5% (14/55) of nurses, and 50.0% (14/28) of other staff members. When comparing those who had more than five influenza vaccinations in their life time, physicians were at 43.9% (18/41) versus nurses at 10.9% (6/55) (p < 0.01), and other HCP at 7.4% (2/27) (p < 0.01). The influenza vaccine uptake rate of 10% in mothers of hospitalized neonates is disappointingly low, resulting in 90% of hospitalized neonates being potentially vulnerable to influenza infection at a time where the risk for influenza-related complication can be severe.
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.