Explore the vast range of titles available.

In the current literature, many non-covalent interaction (NCI) donors have been proposed that can potentially catalyze Diels-Alder (DA) reactions. In this study, a detailed analysis of the governing factors in Lewis acid and non-covalent catalysis of three types of DA reactions was carried out, for which we selected a set of hydrogen-, halogen-, chalcogen-, and pnictogen-bond donors. We found that the more stable the NCI donor–dienophile complex, the larger the reduction in DA activation energy. We also showed that for active catalysts, a significant part of the stabilization was caused by orbital interactions, though electrostatic interactions dominated. Traditionally, DA catalysis was attributed to improved orbital interactions between the diene and dienophile. Recently, Vermeeren and co-workers applied the activation strain model (ASM) of reactivity, combined with the Ziegler-Rauk-type energy decomposition analysis (EDA), to catalyzed DA reactions in which energy contributions for the uncatalyzed and catalyzed reaction were compared at a consistent geometry. They concluded that reduced Pauli repulsion energy, and not enhanced orbital interaction energy, was responsible for the catalysis. However, when the degree of asynchronicity of the reaction is altered to a large extent, as is the case for our studied hetero-DA reactions, the ASM should be employed with caution. We therefore proposed an alternative and complementary approach, in which EDA values for the catalyzed transition-state geometry, with the catalyst present or deleted, can be compared one to one, directly measuring the effect of the catalyst on the physical factors governing the DA catalysis. We discovered that enhanced orbital interactions are often the main driver for catalysis and that Pauli repulsion plays a varying role.

There are widely recognised variations in the delivery and outcomes of healthcare but an incomplete understanding of their causes. There is a growing interest in using routinely collected 'big data' in the evaluation of healthcare. We developed a set of evidence-based 'high impact' quality indicators (QIs) for primary care and examined variations in achievement of these indicators using routinely collected data in the United Kingdom (UK). Cross-sectional analysis of routinely collected, electronic primary care data from a sample of general practices in West Yorkshire, UK (n = 89). The QIs covered aspects of care (including processes and intermediate clinical outcomes) in relation to diabetes, hypertension, atrial fibrillation, myocardial infarction, chronic kidney disease (CKD) and 'risky' prescribing combinations. Regression models explored the impact of practice and patient characteristics. Clustering within practice was accounted for by including a random intercept for practice. Median practice achievement of the QIs ranged from 43.2% (diabetes control) to 72.2% (blood pressure control in CKD). Considerable between-practice variation existed for all indicators: the difference between the highest and lowest performing practices was 26.3 percentage points for risky prescribing and 100 percentage points for anticoagulation in atrial fibrillation. Odds ratios associated with the random effects for practices emphasised this; there was a greater than ten-fold difference in the likelihood of achieving the hypertension indicator between the lowest and highest performing practices. Patient characteristics, in particular age, gender and comorbidity, were consistently but modestly associated with indicator achievement. Statistically significant practice characteristics were identified less frequently in adjusted models. Despite various policy and improvement initiatives, there are enduring inappropriate variations in the delivery of evidence-based care. Much of this variation is not explained by routinely collected patient or practice variables, and is likely to be attributable to differences in clinical and organisational behaviour.