What can local authorities do to improve the social care-related quality of life of older adults living at home? Evidence from the Adult Social Care Survey

Local authorities spend considerable resources on social care at home for older adults. Given the expected growth in the population of older adults and budget cuts on local government, it is important to find efficient ways of maintaining and improving the quality of life of older adults. The ageing in place literature suggests that policies in other functions of local authorities may have a significant role to play. This study aims to examine the associations between social care-related quality of life (SCRQoL) in older adults and three potential policy targets for local authorities: (i) accessibility of information and advice, (ii) design of the home and (iii) accessibility of the local area. We used cross-sectional data from the English national Adult Social Care Survey (ASCS) 2010/2011 on service users aged 65 years and older and living at home (N=29,935). To examine the association between SCRQoL, as measured by the ASCOT, and three single-item questions about accessibility of information, design of the home and accessibility of the local area, we estimate linear and quantile regression models. After adjusting for physical and mental health factors and other confounders our findings indicate that SCRQoL is significantly lower for older adults who find it more difficult to find information and advice, for those who report that their home design is inappropriate for their needs and for those who find it more difficult to get around their local area. In addition, these three variables are as strongly associated with SCRQoL as physical and mental health factors. We conclude that in seeking to find ways to maintain and improve the quality of life of social care users living at home, local authorities could look more broadly across their responsibilities. Further research is required to explore the cost-effectiveness of these options compared to standard social care services

An Assay to Monitor HIV-1 Protease Activity for the Identification of Novel Inhibitors in T-Cells

The emergence of resistant HIV strains, together with the severe side-effects of existing drugs and lack of development of effective anti-HIV vaccines highlight the need for novel antivirals, as well as innovative methods to facilitate their discovery. Here, we have developed an assay in T-cells to monitor the proteolytic activity of the HIV-1 protease (PR). The assay is based on the inducible expression of HIV-1 PR fused within the Gal4 DNA-binding and transactivation domains. The fusion protein binds to the Gal4 responsive element and activates the downstream reporter, enhanced green fluorescent protein (eGFP) gene only in the presence of an effective PR Inhibitor (PI). Thus, in this assay, eGFP acts as a biosensor of PR activity, making it ideal for flow cytometry based screening. Furthermore, the assay was developed using retroviral technology in T-cells, thus providing an ideal environment for the screening of potential novel PIs in a cell-type that represents the natural milieu of HIV infection. Clones with the highest sensitivity, and robust, reliable and reproducible reporter activity, were selected. The assay is easily adaptable to other PR variants, a multiplex platform, as well as to high-throughput plate reader based assays and will greatly facilitate the search for novel peptide and chemical compound based PIs in T-cells

Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways

Oligodendrogliomas are diffusely infiltrative gliomas defined by IDH-mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression. Our multi-faceted computational approach uncovered key genetic alterations associated with disease progression and shorter survival in oligodendroglioma and specifically identified Notch pathway inactivation and PI3K pathway activation as the most strongly associated with MRI and pathology findings of advanced disease and poor clinical outcome. Our findings that Notch pathway inactivation and PI3K pathway activation are associated with advanced disease and survival risk will pave the way for clinically relevant markers of disease progression and therapeutic targets to improve clinical outcomes. Furthermore, our approach demonstrates the strength of machine learning and computational methods for identifying genetic events critical to disease progression in the era of big data and precision medicine