Stochastic simulation of chemical reactions in spatially complex media

Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.

Timeliness and Clinical Impact of Hospital-Initiated Medication Reviews

Background: Medication-related problems often occur in the immediate post-discharge period. To reduce medication misadventure the Commonwealth Government funds home medicines reviews (HMRs). HMRs are initiated when general practitioners refer consenting patients to their community pharmacists, who then engage accredited pharmacists to review patients' medicines in their homes. Aim: To determine if hospital-initiated medication reviews (HIMRs) can be implemented in a more timely manner than HMRs; and to assess the impact of a bespoke referral form with comorbidity-specific questions on the quality of reports. Method: Eligible medical inpatients at risk of medication misadventure were referred by the hospital liaison pharmacist to participating accredited pharmacists post-discharge from hospital. Social, demographic and laboratory data were collected from medical records and during interviews with consenting patients. Issues raised in the HIMR reports were categorised: intervention/action, information given or recommendation, and assigned a rank of clinical significance. Results: HIMRs were conducted within 11.6 6.6 days postdischarge. 36 HIMR reports were evaluated and 1442 issues identified - information given (n = 1204), recommendations made (n = 88) and actions taken (n = 150). The majority of issues raised (89%) had a minor clinical impact. The bespoke referral form prompted approximately half of the issues raised. Conclusion: HIMRs can be facilitated in a more timely manner than post-discharge HMRs. There was an associated positive clinical impact of issues raised in the HIMR reports.

The impact of nonlinear exposure risk relationships on seasonal time series data: modelling danish neonatal birth anthropometric data

Background Birth weight and length have seasonal fluctuations. Previous analyses of birth weight by latitude effects identified seemingly contradictory results, showing both 6 and 12 monthly periodicities in weight. The aims of this paper are twofold: (a) to explore seasonal patterns in a large, Danish Medical Birth Register, and (b) to explore models based on seasonal exposures and a non-linear exposure-risk relationship. Methods Birth weight and birth lengths on over 1.5 million Danish singleton, live births were examined for seasonality. We modelled seasonal patterns based on linear, U- and J-shaped exposure-risk relationships. We then added an extra layer of complexity by modelling weighted population-based exposure patterns. Results The Danish data showed clear seasonal fluctuations for both birth weight and birth length. A bimodal model best fits the data, however the amplitude of the 6 and 12 month peaks changed over time. In the modelling exercises, U- and J-shaped exposure-risk relationships generate time series with both 6 and 12 month periodicities. Changing the weightings of the population exposure risks result in unexpected properties. A J-shaped exposure-risk relationship with a diminishing population exposure over time fitted the observed seasonal pattern in the Danish birth weight data. Conclusion In keeping with many other studies, Danish birth anthropometric data show complex and shifting seasonal patterns. We speculate that annual periodicities with non-linear exposure-risk models may underlie these findings. Understanding the nature of seasonal fluctuations can help generate candidate exposures.