cols tend to make more aggressive recommendations than the ASSET consensus statement. Further research is needed to address these variations and to either validate, alter, or reject the ASSET recommendations.
 Despite an attempt by ASSET to provide standardization, this study highlights the marked variations that still exist regarding RCR rehabilitation. Additionally, online RCR rehabilitation protocols tend to make more aggressive recommendations than the ASSET consensus statement. Further research is needed to address these variations and to either validate, alter, or reject the ASSET recommendations.Knowledge acquisition facilitated by computer games, also referred to as digital game-based learning, is growing in popularity as an educational modality for healthcare disciplines. There is a dearth of research specifically focused on students' perception and lived experience of a serious game, which is a game primarily designed for educational purposes. This qualitative study aimed to evaluate the efficacy of using a serious game to teach hazard and safety assessments in community and residential healthcare settings. Using a phenomenological approach semi-structured interviews collected data about students' experience using the game 'Safe Environments'. Eight students from undergraduate healthcare programs participated. Interpretive Phenomenological Analysis was conducted. Themes and sub-themes identified nuances explaining the impact of prior knowledge, technical ability, and engagement on achievement of learning outcomes. The dynamic interrelationship and influence of themes are illustrated in the KNavEL Model, which explains the complexity of individuals' understanding and perceptions of learning through gaming. This study demonstrates that learning outcomes are directly influenced by the degree of engagement with the game. This in turn is influenced by what the student brings to the game by way of knowledge, experience navigating technology, and the subject matter. The results give voice to students' experiences and provide new insights into understanding the learning processes inherent in using serious games in health education.The association of Zika virus (ZIKV) infection with microcephaly has raised alarm worldwide. Their causal link has been confirmed in different animal models infected by ZIKV. However, the molecular mechanisms underlying ZIKV pathogenesis are far from clear. Hence, we performed global gene expression analysis of ZIKV-infected mouse brains to unveil the biological and molecular networks underpinning microcephaly. We found significant dysregulation of the sub-networks associated with brain development, immune response, cell death, microglial cell activation, and autophagy amongst others. We provided detailed analysis of the related complicated gene networks and the links between them. Additionally, we analyzed the signaling pathways that were likely to be involved. This report provides systemic insights into not only the pathogenesis, but also a path to the development of prophylactic and therapeutic strategies against ZIKV infection.Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching, and visualization through user-friendly web interfaces and web service application programming interfaces.  https://www.selleckchem.com/products/ch-223191.html  MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http//www.liwzlab.cn/microphenodb and http//lilab2.sysu.edu.cn/microphenodb.
  Most studies examining the associations between body composition and type 2 diabetes have been cross-sectional with prevalent diabetes diagnosis or they have analyzed only fat or lean body mass. Hence, the combined effect of fat and lean body mass on the risk of developing type 2 diabetes remains unclear. We investigated whether baseline lean and fat body mass taken simultaneously into account are associated with incidence of type 2 diabetes over a 15-year follow-up in older adults.
 
  We studied 704 men (n = 297) and women (n = 407) from the Helsinki Birth Cohort Study (mean age 61 years at baseline) without diabetes at baseline. Bioelectrical impedance analysis was used to derive baseline fat mass index (FMI, fat mass/height
  ) and lean mass index (LMI, lean mass/height
  ), dichotomized at sex-specific medians. Incident diabetes was defined as the composite of fasting plasma glucose (FPG) ≥ 7.0 mmol/l, haemoglobin A
  (HbA
  ) ≥ 6.5% (48 mmol/mol) or physician-based diagnosis.
 
  After a median 14.8 (range 12.5-16.8) years of follow-up, 110 incident diabetes cases occurred (15.6%). Participants with high FMI and LMI at baseline had higher composite incidence of type 2 diabetes (P < 0.001), and significantly increased risk of type 2 diabetes after adjustment for potential confounding factors (sex, physical activity, education and body mass index) compared to the other participants.
 
  Contrary to a general belief greater muscle mass is not protective against type 2 diabetes. High LMI accompanied with high FMI seem to predict subsequent development of type 2 diabetes.
 Contrary to a general belief greater muscle mass is not protective against type 2 diabetes. High LMI accompanied with high FMI seem to predict subsequent development of type 2 diabetes.