The oxygen saturation of red blood cell concentrates: The basis for a novel index of red cell oxidative stress.

BACKGROUND: Oxidative stress is a major driving force in the development of storage lesions in red cell concentrates (RCCs). Unlike manufactured pharmaceuticals, differences in component preparation methods and genetic/physiological status of donors result in nonuniform biochemical characteristics of RCCs. Various characteristics of donated blood on oxygen saturation (SO2 ) distribution were investigated, and a model to estimate potential oxidative stress burden of stored RCC at transfusion is proposed. STUDY DESIGN AND METHODS: The oxygen content of freshly prepared RCCs (770) was quantified noninvasively as fractional hemoglobin saturation (SO2 ) with visible reflectance spectrometry. Using separate RCCs and mimicking typical handling of RCCs during routine storage, evolution of SO2 was followed for construction of an empirical model. Based on this model, the oxygen exposure index (OEI) was formulated to estimate the accumulated oxygen exposure burden of RCC at the time of transfusion. RESULTS: The SO2 of RCCs varied widely at donation (mean 43% ± 1.3%; range 20%-93%). Multivariate regression model showed that sex and processing method had small effects on SO2 (R2  = 0.12), indicating that variability was mainly attributed to other individual donor characteristics. Storage simulation model indicated that median SO2 increased gradually over 6 weeks (approx. 1.3 fold), while OEI increased at a faster rate (approx. eight-fold). CONCLUSION: In addition to storage age, the OEI provides a potential new metric to assess the quality of RCCs at the time of transfusion in terms of their oxidative stress. In future studies, a single noninvasive measurement during storage could link OEI to clinical outcomes in transfusion recipients.

A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution.

This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

Spatial analyses of wildlife contact networks.

Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely determined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles (Microtus agrestis) inferred from mark-recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increasingly similar to spatial proximity graphs as vole density increases. The average number of contacts, 〈k〉, was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density 〈k〉 is low but hosts move more freely, and at high density 〈k〉 is high but transmission is hampered by local build-up of infected or recovered animals.