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OBJECTIVES Poor discharge communication is associated with negative health outcomes in high-income countries. However, quality of discharge communication has received little attention in India and many other low and middle-income countries. PRIMARY OBJECTIVE To investigate verbal and documented discharge communication for chronic non-communicable disease (NCD) patients. SECONDARY OBJECTIVE To explore the relationship between quality of discharge communication and health outcomes. METHODS DESIGN Prospective study. SETTING Three public hospitals in Himachal Pradesh and Kerala states, India. PARTICIPANTS 546 chronic NCD (chronic respiratory disease, cardiovascular disease or diabetes) patients. Piloted questionnaires were completed at admission, discharge and five and eighteen-week follow-up covering health status, discharge communication practices and health-seeking behaviour. Logistic regression was used to explore the relationship between quality of discharge communication and health outcomes. OUTCOME MEASURElf-reported deterioration of chronic NCD/s (AOR = 0.46; 95% CI 0.25,0.83) within 18-weeks of follow-up. CONCLUSIONS Sub-optimal discharge practices may be compromising continuity and safety of chronic NCD patient care. Structured protocols, documents and training are required to improve discharge communication, healthcare integration and NCD management.INTRODUCTION Intra-abdominal hypertension (IAH) is a well-known phenomenon in critically ill patients. Effects of a moderately elevated intra-abdominal pressure (IAP) on lung mechanics are still not fully analyzed. Moreover, the optimal positive end-expiratory pressure (PEEP) in elevated IAP is unclear. METHODS We investigated changes in lung mechanics and transformation in histological lung patterns using three different PEEP levels in eighteen deeply anesthetized pigs with an IAP of 10 mmHg. After establishing the intra-abdominal pressure, we randomized the animals into 3 groups. Each of n = 6 (Group A = PEEP 5, B = PEEP 10 and C = PEEP 15 cmH2O). End-expiratory lung volume (EELV/kg body weight (bw)), pulmonary compliance (Cstat), driving pressure (ΔP) and transpulmonary pressure (ΔPL) were measured for 6 hours. Additionally, the histological lung injury score was calculated. RESULTS Comparing hours 0 and 6 in group A, there was a decrease of EELV/kg (27±2 vs. selleck 16±1 ml/kg; p less then 0.05) and of Cstat (42±2 vs. 27±1 ml/cmH2O; p less then 0.05) and an increase of ΔP (11±0 vs. 17±1 cmH2O; p less then 0.05) and ΔPL (6±0 vs. 10±1 cmH2O; p less then 0.05). In group B, there was no significant change in EELV/kg (27±3 vs. 24±3 ml/kg), but a decrease in Cstat (42±3 vs. 32±1 ml/cmH20; p less then 0.05) and an increase in ΔP (11±1 vs. 15±1 cmH2O; p less then 0.05) and ΔPL (5±1 vs. 7±0 cmH2O; p less then 0.05). In group C, there were no significant changes in EELV/kg (27±2 vs. 29±3 ml/kg), ΔP (10±1 vs. 12±1 cmH2O) and ΔPL (5±1 vs. 7±1 cmH2O), but a significant decrease of Cstat (43±1 vs. 37±1 ml/cmH2O; p less then 0.05). Histological lung injury score was lowest in group B. CONCLUSIONS A moderate elevated IAP of 10 mmHg leads to relevant changes in lung mechanics during mechanical ventilation. In our study, a PEEP of 10 cmH2O was associated with a lower lung injury score and was able to overcome the IAP induced alterations of EELV.Genomic editing technologies are developing rapidly, promising significant developments for biomedicine, agriculture and other fields. In the present investigation, we analyzed and compared the process of innovation for six genomic technologies viral vectors, RNAi, TALENs, meganucleases, ZFNs and CRISPR/Cas including the profile of the main research institutions and their funders, to understand how innovation evolved and what institutions influenced research trajectories. A Web of Science search of papers on viral vectors RNAi, CRISPR/Cas, TALENs, ZFNs and meganucleases was used to build a citation network of 16,746 papers. An analysis of network clustering combined with text mining was performed. For viral vectors, a long-term process of incremental innovation was identified, which was largely publicly funded in the United States and the European Union. The trajectory of RNAi research included clusters related to the study of RNAi as a biological phenomenon and its use in functional genomics, biomedicine andent of applications of genomic technologies.Many studies have examined children’s understanding of playing and learning as separate concepts, but the ways that children relate playing and learning to one another remain relatively unexplored. The current study asked 5- to 8-year-olds (N = 92) to define playing and learning, and examined whether children defined them as abstract processes or merely as labels for particular types of activities. We also asked children to state whether playing and learning can occur simultaneously, and examined whether they could give examples of playing and learning with attributes either congruent or incongruent with those activities. Older children were more likely to define both playing and learning in terms of abstract processes, rather than by describing particular topics or activities. Children who defined both playing and learning in this way were able to generate more examples of situations where they were simultaneously playing and learning, and were better able to generate examples of learning with characteristics of play, and examples of playing with characteristics of learning. These data suggest that children develop an understanding that learning and playing can coincide. These results are critical to researchers and educators who seek to integrate play and learning, as children’s beliefs about these concepts can influence how they reflect on playful learning opportunities.Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission.