Therkildsenlykkegaard1412

Although influenza can lead to adverse outcomes during pregnancy, the level of influenza vaccine coverage among pregnant women remains very low. According to the literature, a high level of knowledge about influenza disease and the influenza vaccine is one of the main determinants of vaccination coverage. The objective of the present study was to describe pregnant women's level of knowledge of these topics and to identify any corresponding determinants.

A prospective, observational, hospital-based study of women having given birth in our university medical centre during the 2014-2015 influenza season. Data were collected through a self-questionnaire or extracted from medical records. Determinants of highest knowledge were identified using logistic regression.

Of the 2069 women included in the study, 827 (40%) did not know that influenza can lead to severe adverse outcomes for the mother, and 960 (46%) did not know about possible severe adverse outcomes for the baby. Two hundred and one women (9.8%) stated that the vaccine was "contraindicated" or "unnecessary" during pregnancy. Only 205 women (17%) had been vaccinated during a previous pregnancy. Determinants of the highest level of knowledge were age over 24, a high educational level, previous influenza vaccination, nulliparity, and the recommendation of vaccination by a healthcare professional.

Recommending vaccination during pregnancy appears to increase knowledge about influenza and its vaccine among pregnant women.

Recommending vaccination during pregnancy appears to increase knowledge about influenza and its vaccine among pregnant women.Argonaute (AGO) proteins are the key component of the RNA interference machinery that suppresses gene expression by forming an RNA-induced silencing complex (RISC) with microRNAs (miRNAs). Each miRNA is involved in various cellular processes, such as development, differentiation, tumorigenesis, and viral infection. Thus, molecules that regulate miRNA function are expected to have therapeutic potential. In addition, the biogenesis of miRNA is a multistep process involving various proteins, although the complete pathway remains to be elucidated. Therefore, identification of molecules that can specifically modulate each step will help understand the mechanism of gene suppression. To date, several AGO2 inhibitors have been identified. However, these molecules were identified through a single screening method, and no studies have specifically evaluated a combinatorial strategy. Here, we demonstrated a combinatorial screening (SCR) approach comprising an in silico molecular docking study, surface plasmon resonance (SPR) analysis, and nuclear magnetic resonance (NMR) analysis, focusing on the strong binding between the 5'-terminal phosphate of RNA and the AGO2 middle (MID) domain. By combining SPR and NMR, we identified binding modes of amino acid residues binding to AGO2. First, using a large chemical library (over 6,000,000 compounds), 171 compounds with acidic functional groups were screened using in silico SCR. Next, we constructed an SPR inhibition system that could analyze only the 5'-terminal binding site of RNA, and nine molecules that strongly bound to the AGO2 MID domain were selected. Finally, using NMR, three molecules that bound to the desired site were identified. The RISC inhibitory ability of the "hit" compounds was analyzed in human cell lysate, and all three hit compounds strongly inhibited the binding between double-stranded RNA and AGO2.Animal models are vital to the study of transfusion and development of new blood products. Post-transfusion recovery of human blood components can be studied in mice, however, there is a need to identify strains that can best tolerate xenogeneic transfusions, as well as to optimize such protocols. Specifically, the importance of using immunodeficient mice, such as NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice, to study human transfusion has been questioned. In this study, strains of wild-type and NSG mice were compared as hosts for human transfusions with outcomes quantified by flow cytometric analyses of CD235a+ erythrocytes, CD45+ leukocytes, and CD41+CD42b+ platelets. Complete blood counts were evaluated as well as serum cytokines by multiplexing methods. Circulating human blood cells were maintained better in NSG than in wild-type mice. Lethargy and hemoglobinuria were observed in the first hours in wild-type mice along with increased pro-inflammatory cytokines/chemokines such as monocyte chemoattractant protein-1, tumor necrosis factor α, keratinocyte-derived chemokine (KC or CXCL1), and interleukin-6, whereas NSG mice were less severely affected. Whole blood transfusion resulted in rapid sequestration and then release of human cells back into the circulation within several hours. This rebound effect diminished when only erythrocytes were transfused. Nonetheless, human erythrocytes were found in excess of mouse erythrocytes in the liver and lungs and had a shorter half-life in circulation. Variables affecting the outcomes of transfused erythrocytes were cell dose and mouse weight; recipient sex did not affect outcomes. The sensitivity and utility of this xenogeneic model were shown by measuring the effects of erythrocyte damage due to exposure to the oxidizer diamide on post-transfusion recovery. Overall, immunodeficient mice are superior models for xenotransfusion as they maintain improved post-transfusion recovery with negligible immune-associated side effects.Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years) undergoing ambulatory surgery between the years 2016-2018. Amredobresib The data, comprising of patient, procedure and provider factors that could influence postoperative pain and opioid requirements, was randomly split into training (80%) and validation (20%) datasets. Machine learning models of different classes were developed to predict categorized levels of postoperative opioid requirements using the training dataset and then evaluated on the validation dataset. Prediction accuracy was used to differentiate model performances. The five types of models that were developed returned the following accuracies at two different stages of surgery 1) Prior to surgery-Multinomial Logistic Regression 71%, Naïve Bayes 67%, Neural Network 30%, Random Forest 72%, Extreme Gradient Boost 71% and 2) End of surgery-Multinomial Logistic Regression 71%, Naïve Bayes 63%, Neural Network 32%, Random Forest 72%, Extreme Gradient Boost 70%.