Positive behaviour in the early years : perceptions of staff, service providers and parents in managing and promoting positive behaviour in early years and early primary settings

The full report of research into positive behaviour in the early years: perceptions of staff, service providers and parents in managing and promoting positive behaviour in early years and early primary settings

Glucocorticoid insensitivity as a future target of therapy for chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is characterized by an abnormal and chronic inflammatory response in the lung that underlies the chronic airflow obstruction of the small airways, the inexorable decline of lung function, and the severity of the disease. The control of this inflammation remains a key strategy for treating the disease; however, there are no current anti-inflammatory treatments that are effective. Although glucocorticoids (GCs) effectively control inflammation in many diseases such as asthma, they are less effective in COPD. The molecular mechanisms that contribute to the development of this relative GC-insensitive inflammation in the lung of patients with COPD remain unclear. However, recent studies have indicated novel mechanisms and possible therapeutic strategies. One of the major mechanisms proposed is an oxidant-mediated alteration in the signaling pathways in the inflammatory cells in the lung, which may result in the impairment of repressor proteins used by the GC receptor to inhibit the transcription of proinflammatory genes. Although these studies have described mechanisms and targets by which GC function can be restored in cells from patients with COPD, more work is needed to completely elucidate these and other pathways that may be involved in order to allow for more confident therapeutic targeting. Given the relative GC-insensitive nature of the inflammation in COPD, a combination of therapies in addition to a restoration of GC function, including effective alternative anti-inflammatory targets, antioxidants, and proresolving therapeutic strategies, is likely to provide better targeting and improvement in the management of the disease

Identifying which septic patients have increased mortality risk using severity scores:a cohort study

Background: Early aggressive therapy can reduce the mortality associated with severe sepsis but this relies on prompt recognition, which is hindered by variation among published severity criteria. Our aim was to test the performance of different severity scores in predicting mortality among a cohort of hospital inpatients with sepsis. Methods: We anonymously linked routine outcome data to a cohort of prospectively identified adult hospital inpatients with sepsis, and used logistic regression to identify associations between mortality and demographic variables, clinical factors including blood culture results, and six sets of severity criteria. We calculated performance characteristics, including area under receiver operating characteristic curves (AUROC), of each set of severity criteria in predicting mortality. Results: Overall mortality was 19.4% (124/640) at 30 days after sepsis onset. In adjusted analysis, older age (odds ratio 5.79 (95% CI 2.87-11.70) for ≥80y versus <60y), having been admitted as an emergency (OR 3.91 (1.31-11.70) versus electively), and longer inpatient stay prior to sepsis onset (OR 2.90 (1.41-5.94) for >21d versus <4d), were associated with increased 30 day mortality. Being in a surgical or orthopaedic, versus medical, ward was associated with lower mortality (OR 0.47 (0.27-0.81) and 0.26 (0.11-0.63), respectively). Blood culture results (positive vs. negative) were not significantly association with mortality. All severity scores predicted mortality but performance varied. The CURB65 community-acquired pneumonia severity score had the best performance characteristics (sensitivity 81%, specificity 52%, positive predictive value 29%, negative predictive value 92%, for 30 day mortality), including having the largest AUROC curve (0.72, 95% CI 0.67-0.77). Conclusions: The CURB65 pneumonia severity score outperformed five other severity scores in predicting risk of death among a cohort of hospital inpatients with sepsis. The utility of the CURB65 score for risk-stratifying patients with sepsis in clinical practice will depend on replicating these findings in a validation cohort including patients with sepsis on admission to hospital

Metformin treatment in heart failure with preserved ejection fraction: a systematic review and meta-regression analysis

Background: Observational series suggest a mortality benefit from metformin in the heart failure (HF) population. However, the benefit of metformin in HF with preserved ejection fraction (HFpEF) has yet to be explored. We performed a systematic review and meta-analysis to identify whether variation in EF impacts mortality outcomes in HF patients treated with metformin. Methods: MEDLINE and EMBASE were searched up to October 2019. Observational studies and randomised trials reporting mortality in HF patients and the proportion of patients with an EF > 50% at baseline were included. Other baseline variables were used to assess for heterogeneity in treatment outcomes between groups. Regression models were used to determine the interaction between metformin and subgroups on mortality. Results: Four studies reported the proportion of patients with a preserved EF and were analysed. Metformin reduced mortality in both preserved or reduced EF after adjustment with HF therapies such as angiotensin converting enzyme inhibitors (ACEi) and beta-blockers (β = - 0.2 [95% CI - 0.3 to - 0.1], p = 0.02). Significantly greater protective effects were seen with EF > 50% (p = 0.003). Metformin treatment with insulin, ACEi and beta-blocker therapy were also shown to have a reduction in mortality (insulin p = 0.002; ACEi p p = 0.017), whereas female gender was associated with worse outcomes (p Conclusions: Metformin treatment is associated with a reduction in mortality in patients with HFpEF

From hypertext to hype and back again: exploring the roots of social media in the early web

Preprint of chapter from the SAGE Handbook of Social Media (Burgess, Marwick and Poell, eds., 2018). "How should we think of the relationship between social media and the early web, and what can we learn from this history?

Effects of peri-operative nonsteroidal anti-inflammatory drugs on postoperative kidney function for adults with normal kidney function

This is the protocol for a review and there is no abstract. The objectives are as follows: This review aims to look at the effect of NSAIDs used in the peri-operative period on post-operative kidney function in patients with normal kidney function.</p

Fake News Detection on Twitter Using Propagation Structures

The growth of social media has revolutionized the way people access information. Although platforms like Facebook and Twitter allow for a quicker, wider and less restricted access to information, they also consist of a breeding ground for the dissemination of fake news. Most of the existing literature on fake news detection on social media proposes user-based or content-based approaches. However, recent research revealed that real and fake news also propagate significantly differently on Twitter. Nonetheless, only a few articles so far have explored the use of propagation features in their detection. Additionally, most of them have based their analysis on a narrow tweet retrieval methodology that only considers tweets to be propagating a news piece if they explicitly contain an URL link to an online news article. By basing our analysis on a broader tweet retrieval methodology that also allows tweets without an URL link to be considered as propagating a news piece, we contribute to fill this research gap and further confirm the potential of using propagation features to detect fake news on Twitter. We firstly show that real news are significantly bigger in size, are spread by users with more followers and less followings, and are actively spread on Twitter for a longer period of time than fake news. Secondly, we achieve an 87% accuracy using a Random Forest Classifier solely trained on propagation features. Lastly, we design a Geometric Deep Learning approach to the problem by building a graph neural network that directly learns on the propagation graphs and achieve an accuracy of 73.3%