Low-molecular-weight human serum proteome using ultrafiltration, isoelectric focusing, and mass spectrometry

Identification of the serum proteome is a daunting analytical task due to the complex nature of the sample which has an extremely large dynamic range of protein components. This report addresses this issue by using centrifugal ultrafiltration to enrich the low-molecular-weight (LMW) serum proteome while decreasing the amount of abundant high-molecular-weight proteins. Reduction of the complex nature of the sample was achieved by fractionation of the LMW serum proteins using solution-phase isoelectric focusing (IEF). Multiple enzyme digestions are performed and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Analysis of the tandem mass spectra resulted in the identification of 262 proteins belonging to LMW serum proteome. Our results demonstrate the effectiveness of this methodology to isolate and identify LMW proteins with improved confidence in the MS data acquired. In addition, our methodology can be combined with other multidimensional chromatography techniques performed on the peptide level to increase the number of identified proteins.     

Recent advances in the use of ionic liquids as solvents for protein-based materials and chemistry

Proteins are a diverse class of molecules that can act as catalysts and structural components. Interest in their interactions with ionic solvents is on the increase due to the tuneable possibilities for non-aqueous biocatalysis, improved thermostability of biomaterials, and possible roles in medicine, such as drug delivery and use as cell-growth scaffolds. We summarise here the recent examples of these exciting new aspects of protein-ionic solvent interactions, highlighting future directions.     

Terrein

The fungal metabolite terrein (alternative name: trans‐2,3‐di­hydroxy‐4‐propenyl­cyclo­pent‐4‐enone), C₈H₁₀O₃, forms monoclinic (P2₁) crystals. The mol­ecules form hydrogen‐bonded chains, with O?O distances of 2.7115 (16) and 2.8155 (15) Å.     

Overcoming the barriers: a qualitative study of simulation adoption in the NHS

This paper addresses a key issue in the health OR literature, namely the apparent failure of OR modelling to become embedded and widely implemented within healthcare organisations. The research presented here is a case study to evaluate the adoption of one particular simulation modelling tool, Scenario Generator (S:G), which was developed by the SIMUL8 Corporation in a PPI partnership with the UK's National Health Service (NHS) Institute for Innovation and Improvement. The study involved semi-structured interviews with employees of 28 Primary Care Trusts who had all been engaged in some way with the initiative, with participants classified as ‘Not Started’, ‘Given Up’ and ‘Actively Using’. This paper presents a brief summary of barriers and facilitators to the successful use of the S:G software, but its main purpose is to focus more broadly on factors influencing the successful adoption of simulation tools in general within healthcare organisations. The insights gained in this study are relevant to improving the uptake of OR modelling in general within the NHS.     

Small world network models of the dynamics of HIV infection

It has long been recognised that the structure of social networks plays an important role in the dynamics of disease propagation. The spread of HIV results from a complex network of social interactions and other factors related to culture, sexual behaviour, demography, geography and disease characteristics, as well as the availability, accessibility and delivery of healthcare. The small world phenomenon has recently been used for representing social network interactions. It states that, given some random connections, the degrees of separation between any two individuals within a population can be very small. In this paper we present a discrete event simulation model which uses a variant of the small world network model to represent social interactions and the sexual transmission of HIV within a population. We use the model to demonstrate the importance of the choice of topology and initial distribution of infection, and capture the direct and non-linear relationship between the probability of a casual partnership (small world randomness parameter) and the spread of HIV. Finally, we illustrate the use of our model for the evaluation of interventions such as the promotion of safer sex and introduction of a vaccine.