Hyllestedvick2615

Background Umbilical cord (UC) abnormalities are related to neurological outcome and death; specific molecular factors that might be involved are, as yet, unknown; however, protein-coding genes insulin-like growth factor 2 (IGF2) and cyclin-dependent kinase inhibitor 1C (CDKN1C) have been identified as potential candidates. Methods An analytical observational study was carried out. Newborn UCs were collected, along with their clinical and morphological features. Immunohistochemical analysis was made on paraffin-embedded sections and quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed in fresh UC tissue for the assessment of gene expression. Results A total of 100 newborns were included. A significant association was found between long UC and prematurity [odds ratio (OR) 9] and long UC and respiratory distress (OR 4.04). Gestational diabetes (OR 8.55) and hypertensive disorders of pregnancy (HDP) (OR 4.71) were found to be related to short UCs. selleck The frequency for abnormal UC length was higher than expected. UC length was positively correlated with maternal, newborn and placental weight. No statistical association was found between IGF2 and CDKN1C (p57) expression and UC length; however, there was a tendency for higher CDKN1C expression in short UCs, while, on the contrary, higher IGF2 expression for long UCs. Conclusion UC length was observed to be associated with maternal and newborn complications. Protein expression, messenger RNA (mRNA) activity and the activity of said genes seem to be related to UC length.BACKGROUND Medical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition. OBJECTIVE This study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities. METHODS In this paper, the pretraining language representation model wasm previous work and performs as a new state-of-the-art method. CONCLUSIONS The multi-level representation learning model is proposed as a method to perform the Chinese EMRs entity recognition task. Experiments on two clinical datasets demonstrate the usefulness of using the multi-head attention mechanism to extract multi-level representation as part of the language model. ©Zhichang Zhang, Lin Zhu, Peilin Yu. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 04.05.2020.BACKGROUND The web-based BeUpstanding Champion Toolkit was developed to support work teams in addressing the emergent work health and safety issue of excessive sitting. It provides a step-by-step guide and associated resources that equip a workplace representative-the champion-to adopt and deliver the 8-week intervention program (BeUpstanding) to their work team. The evidence-informed program is designed to raise awareness of the benefits of sitting less and moving more, build a supportive culture for change, and encourage staff to take action to achieve this change. Work teams collectively choose the strategies they want to implement and promote to stand up, sit less, and move more, with this bespoke and participative approach ensuring the strategies are aligned with the team's needs and existing culture. BeUpstanding has been iteratively developed and optimized through a multiphase process to ensure that it is fit for purpose for wide-scale implementation. OBJECTIVE The study aimed to describe the current v7000682347; https//www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372843&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/15756. ©Genevieve Nissa Healy, Ana D Goode, Alison Abbott, Jennifer Burzic, Bronwyn K Clark, David W Dunstan, Elizabeth G Eakin, Matthew Frith, Nicholas D Gilson, Lan Gao, Lynn Gunning, Jodie Jetann, Anthony D LaMontagne, Sheleigh P Lawler, Marjory Moodie, Phuong Nguyen, Neville Owen, Leon Straker, Perri Timmins, Lisa Ulyate, Elisabeth A H Winkler. Originally published in JMIR Research Protocols (http//www.researchprotocols.org), 04.05.2020.BACKGROUND Heart failure is a chronic disease affecting patient morbidity and mortality. Current guidelines for heart failure patient treatment are focused on improving their clinical status, functional capacity, and quality of life. However, these guidelines implement numerous instructions including medical treatment adherence, physical activity, and self-care management. The complexity of the therapeutic instructions makes them difficult to follow especially by older adults. OBJECTIVE The challenge of this project is to (1) measure real-life adherence to a regular physical exercise program and (2) attempt to influence older adult patients with heart failure toward embracing a more physically active self-care lifestyle. METHODS This research consists of two studies, including a lab experiment and a pragmatic evaluation of technology at patients' homes. The lab experiment aims at exploring in an objective way (measuring neurophysiological responses to stimuli) patient engagement with different characteristicsBACKGROUND Phishing is a cybercrime in which the attackers usually impersonate a trusted source. The attackers usually send an email that contains a link that allows them to steal the receiver's personal information. In the United States, phishing is the number one cybercrime by victim count according to the Federal Bureau of Investigation's 2019 internet crime report. Several studies investigated ways to increase awareness and improve employees' resistance to phishing attacks. However, in 2019, successful phishing attacks continued to rise at a high rate. OBJECTIVE The objective of this study was to investigate the influence of personality-based antecedents on phishing susceptibility in a health care context. METHODS Survey data were collected from participants through Amazon Mechanical Turk to test a proposed conceptual model using structural equation modeling. RESULTS A total of 200 participants took part. Health concerns, disposition to trust, and risk-taking propensity yielded higher phishing susceptibility.