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Since the majority of smokers were men (62.3%), the effect of male gender, smoking, together with the NHSN risk index was further investigated as predictors of SSI within a logistic regression model. All three predictors showed a significant effect on SSI.

Parotid gland surgery has a low rate of SSI. In our cohort, male gender, smoking and high NHSN risk index scores were significantly associated with SSI, whereas antibiotic prophylaxis had no protective effect.

Parotid gland surgery has a low rate of SSI. In our cohort, male gender, smoking and high NHSN risk index scores were significantly associated with SSI, whereas antibiotic prophylaxis had no protective effect.Time-based targets (TBTs) for ED stays were introduced to improve quality of care but criticised as having harmful unintended consequences. The aim of the review was to determine whether implementation of TBTs influenced quality of care. Structured searches in medical databases were undertaken (2000-2019). Studies describing a state, regional or national TBTs that reported processes or outcomes of care related to the target were included. Harvest plots were used to summarise the evidence. Thirty-three studies (n = 34 million) were included. In some settings, reductions in mortality were seen in ED, in hospital and at 30 days, while in other settings mortality was unchanged. Mortality reductions were seen in the face of increasing age and acuity of presentations, when short-stay admissions were excluded, and when pre-target temporal trends were accounted for. ED crowding, time to assessment and admission times reduced. Fewer patients left prior to completing their care and fewer patients re-presented to EDs. Short-stay admissions and re-admissions to wards within 30 days increased. There was conflicting evidence regarding hospital occupancy and ward medical emergency calls, while times to treatment for individual conditions did not change. The evidence for associations was mostly low certainty and confidence in the findings is accordingly low. Quality of care generally improved after targets were introduced and when compliance with targets was high. This depended on how targets were implemented at individual sites or within jurisdictions, with important implications for policy makers, health managers and clinicians.This paper uses the decomposition framework from the economics literature to examine the statistical structure of treatment effects estimated with observational data compared to those estimated from randomized studies. It begins with the estimation of treatment effects using a dummy variable in regression models and then presents the decomposition method from economics which estimates separate regression models for the comparison groups and recovers the treatment effect using bootstrapping methods. This method shows that the overall treatment effect is a weighted average of structural relationships of patient features with outcomes within each treatment arm and differences in the distributions of these features across the arms. In large randomized trials, it is assumed that the distribution of features across arms is very similar. Importantly, randomization not only balances observed features but also unobserved. P5091 Applying high dimensional balancing methods such as propensity score matching to the observational data causes the distributional terms of the decomposition model to be eliminated but unobserved features may still not be balanced in the observational data. Finally, a correction for non-random selection into the treatment groups is introduced via a switching regime model. Theoretically, the treatment effect estimates obtained from this model should be the same as those from a randomized trial. However, there are significant challenges in identifying instrumental variables that are necessary for estimating such models. At a minimum, decomposition models are useful tools for understanding the relationship between treatment effects estimated from observational versus randomized data.Despite sharing conserved substrate-binding residues, members of 3-hydroxyisobutyrate dehydrogenase (HIBADH) superfamily show remarkable differences in substrate preference. Cysteine residues were identified within a radius of 6 Å surrounding both the active site and the substrate entry site of HIBADH enzyme from Mycobacterium tuberculosis (MtHIBADH). Chemical modification with thiol-modifying reagents, pCMB and DTNB, abrogated the dehydrogenase activity of the enzyme. The loss in activity followed pseudo-first-order kinetics as a function of the concentration of pCMB. S-HIBA (substrate) binding provided partial protection, while NAD (cofactor) binding provided ~70% protection from thiol-modifying reagent. Site-directed mutagenesis of cysteine residues present in the MtHIBADH enzyme identified the indispensable role of Cys-210 residue, located at C-terminal domain, for its dehydrogenase activity. Cys-210 mutation to serine reduced the dehydrogenase activity by ~2-fold while mutation to alanine strikingly reduced the activity by ~140-fold. C210A mutation did not perturb the state of oligomerization of the enzyme but perturbed the secondary structure content. Structural analysis revealed the involvement of Cys-210 residue in inter-chain interaction with Gln-178, which acts as hydrogen bond donor and coordinates with Cys-210 and Gly-208 of the adjacent subunit. The data demonstrate a critical role of Cys-210 residue in maintaining the conformation and rigidity of loop composed of substrate-interacting residues involved in the entry of S-HIBA substrate in MtHIBADH.Understanding human disease, zoonoses and emergence is a global priority. A deep understanding of pathogen ecology and the complex inherent relationships at the agent-environment interface are essential to inform disease control and mitigation and to predict the next zoonotic pandemic. Here, we present the first analysis of social and environmental factors associated with human, zoonotic and emerging pathogen diversity at a global scale, controlling for research effort. Predictor-response associations were captured by generalized additive models. We used national level data to aid in policy development to inform control and mitigation. We show that human population density, land area, temperature and the human development index at the country level are associated with human, emerging and zoonotic pathogen diversity. Multiple models demonstrating society-agent-environment interactions demonstrate that social, environmental and geographical factors predict global pathogen diversity. The analyses demonstrate that weather variables (temperature and rainfall) have the potential to influence pathogen diversity.