Disulfiram-loaded immediate and extended release vaginal tablets for the localised treatment of cervical cancer

Objectives: To develop and manufacture both immediate and sustained release vaginal tablets containing the anticancer drug disulfiram, which has the potential to be used as a non-invasive treatment for cervical cancer. 
Methods: Disulfiram-loaded vaginal tablets were manufactured at pilot scale using the direct compression method. These tablets were tested in accordance with the European Pharmacopeia testing of solid dosage form guidelines. They were also tested using a biorelevant dissolution method as well as a dual-chambered release model designed to better mimic the dynamic nature of the vaginal vault.
Key findings: We have developed both immediate and sustained release vaginal tablets, which when manufactured at pilot scale are within the limits set by the European Pharmacopeia for the testing of solid dosage forms. Furthermore, these tablets are capable of releasing disulfiram in vitro using the dual-chambered release model at levels 25 000 times and 35 000 times greater than its IC50 concentration for the HeLa cervical cancer cell line. 
Conclusions: The successful pilot manufacture and testing of both the immediate and sustained release disulfiram-loaded vaginal tablets warrant further investigation, using an in-vivo model, to assess their potential for use as a non-invasive treatment option for cervical cancer.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

Making space: English women, letter-writing, and the life of the mind, c.1650-1750

This article uses women's letter-writing from the late seventeenth and early eighteenth centuries to explore the home as a site of female intellectual endeavour. Far from representing a static backdrop to the action of domestic life, the home played a dynamic role in women's experiences of the life of the mind and shaped the ways in which women thought and wrote. Letters were penned in dining rooms, parlours and closets, by firesides, and on desks and laps. In their letters, women projected images of themselves scribbling epistles to friends in order to maintain their mental intimacy. Space was both real and imagined and the physical realities of a hand-written and hand-delivered letter gave way to the imaginative possibilities brought by networks of epistolary exchange and the alternative spaces of creative thought. By reinstating the home more fully in the history of female intellectual experience, a more nuanced view of the domestic arena can be developed: one that sees the home not as a site of exclusion and confinement, but as a space for scholarship and exchange. 

Women, letter-writing and the life of the mind in England, c.1650-1750

Drawing on national and regional letter collections dating from the late seventeenth and early eighteenth centuries, this article explores women's experiences of the life of the mind through an analysis of their letter-writing. This study also highlights the shortcomings of the compartmentalised nature of scholarship on women's writing and intellectual lives and proposes the letter both as a beneficial historical source and methodological tool for research on women's mental worlds. By employing an inclusive definition of intellectual and creative life, and eschewing traditional benchmarks of achievement, this article contends that women took a full part in the cultures of knowledge of their time. 

The extended phenotype of Scots pine Pinus sylvestris structures the understorey assemblage

Heritable variation in plant secondary compounds in dominant species has been hypothesised to effect ecosystem function and the structure of associated assemblages of plants, microbes and animals. The functioning of this extended phenotype in relation to the understorey vegetation composition was tested within a boreal forest system dominated by Pinus sylvestris which contains a range of monoterpenes, the composition of which is largely under genetic control. A variance partitioning approach was adopted to identify the relative importance of tree chemistry, environment, spatial location and tree architecture in controlling the distribution of species in the ground flora under individual trees. The monoterpene composition of the pine needles appeared to contribute significantly to controlling understorey vegetation composition, but was less important than environmental factors, though similar to spatial factors. Thus there appears to be a link between variation in the chemical composition of the single, dominant tree species within this system and the pattern of occurrence and abundance in other species at the same trophic level.

A techno-economic evaluation of changing from conventional vehicles to range extended electric vehicles

Transportation accounts for 22% of greenhouse gas emissions in the UK, and increases to 25% in Northern Ireland. Surface transport carbon dioxide emissions, consisting of road and rail, are dominated by cars. Demand for mobility is rising rapidly and vehicle numbers are expected to more than double by 2050. Car manufacturers are working towards reducing their carbon footprint through improving fuel efficiency and controlling exhaust emissions. Fuel efficiency is now a key consideration of consumers purchasing a new vehicle. While measures have been taken to help to reduce pollutants, in the future, alternative technologies will have to be used in the transportation industry to achieve sustainability. There are currently many alternatives to the market leader, the internal combustion engine. These alternatives include hydrogen fuel cell vehicles and electric vehicles, a term which is widely used to cover battery electric vehicles, plug-in hybrid electric vehicles and extended-range electric vehicles. This study draws direct comparisons measuring the differing performance in terms of fuel consumption, carbon emissions and range of a typical family saloon car using different fuel types. These comparisons will then be analysed to see what effect switching from a conventionally fuelled vehicle to a range extended electric vehicle would have not only on the end user, but also the UK government.

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.