A stakeholder co-design approach for developing a community pharmacy service to enhance screening and management of atrial fibrillation

The authors would like to thank all participants in this research for their valuable input into the co-design process.Background: Community pharmacies provide a suitable setting to promote self-screening programs aimed at enhancing the early detection of atrial fibrillation (AF). Developing and implementing novel community pharmacy services (CPSs) is a complex and acknowledged challenge, which requires comprehensive planning and the participation of relevant stakeholders. Co-design processes are participatory research approaches that can enhance the development, evaluation and implementation of health services. The aim of this study was to co-design a pharmacist-led CPS aimed at enhancing self-monitoring/screening of AF. Methods: A 3-step co-design process was conducted using qualitative methods: (1) interviews and focus group with potential service users (n = 8) to identify key needs and concerns; (2) focus group with a mixed group of stakeholders (n = 8) to generate a preliminary model of the service; and (3) focus group with community pharmacy owners and managers (n = 4) to explore the feasibility and appropriateness of the model. Data were analysed qualitatively to identify themes and intersections between themes. The JeMa2 model to conceptualize pharmacybased health programs was used to build a theoretical model of the service. Results: Stakeholders delineated: a clear target population (i.e., individuals ≥65 years old, with hypertension, with or without previous AF or stroke); the components of the service (i.e., patient education; self-monitoring at home; results evaluation, referral and follow-up); and a set of circumstances that may influence the implementation of the service (e.g., quality of the service, competency of the pharmacist, inter-professional relationships, etc.). A number of strategies were recommended to enable implementation (e.g.,. endorsement by leading cardiovascular organizations, appropriate communication methods and channels between the pharmacy and the general medical practice settings, etc.). Conclusion: A novel and preliminary model of a CPS aimed at enhancing the management of AF was generated from this participatory process. This model can be used to inform decision making processes aimed at adopting and piloting of the service. It is expected the co-designed service has been adapted to suit existing needs of patients and current care practices, which, in turn, may increase the feasibility and acceptance of the service when it is implemented into a real setting.This work was funded by Covidien Pty Ltd. (Medtronic Australasia Pty Ltd) [UTS Project code: PRO16–0688], which is the company that has the rights to distribute the device Microlife BP A200 AFIB in Australia. Also, funding for this research has been provided by a UTS Chancellor’s postdoctoral fellowship awarded to the first author of this article (ID number: 2013001605)

A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

Two Pathways Recruit Telomerase to Saccharomyces cerevisiae Telomeres

The catalytic subunit of yeast telomerase, Est2p, is a telomere associated throughout most of the cell cycle, while the Est1p subunit binds only in late S/G2 phase, the time of telomerase action. Est2p binding in G1/early S phase requires a specific interaction between telomerase RNA (TLC1) and Ku80p. Here, we show that in four telomerase-deficient strains (cdc13-2, est1Ä, tlc1-SD, and tlc1-BD), Est2p telomere binding was normal in G1/early S phase but reduced to about 40–50% of wild type levels in late S/G2 phase. Est1p telomere association was low in all four strains. Wild type levels of Est2p telomere binding in late S/G2 phase was Est1p-dependent and required that Est1p be both telomere-bound and associated with a stem-bulge region in TLC1 RNA. In three telomerase-deficient strains in which Est1p is not Est2p-associated (tlc1-SD, tlc1-BD, and est2Ä), Est1p was present at normal levels but its telomere binding was very low. When the G1/early S phase and the late S/G2 phase telomerase recruitment pathways were both disrupted, neither Est2p nor Est1p was telomere-associated. We conclude that reduced levels of Est2p and low Est1p telomere binding in late S/G2 phase correlated with an est phenotype, while a WT level of Est2p binding in G1 was not sufficient to maintain telomeres. In addition, even though Cdc13p and Est1p interact by two hybrid, biochemical and genetic criteria, this interaction did not occur unless Est1p was Est2p-associated, suggesting that Est1p comes to the telomere only as part of the holoenzyme. Finally, the G1 and late S/G2 phase pathways for telomerase recruitment are distinct and are likely the only ones that bring telomerase to telomeres in wild-type cells

Cytokine Levels Correlate with Immune Cell Infiltration after Anti-VEGF Therapy in Preclinical Mouse Models of Breast Cancer

The effect of blocking VEGF activity in solid tumors extends beyond inhibition of angiogenesis. However, no studies have compared the effectiveness of mechanistically different anti-VEGF inhibitors with respect to changes in tumor growth and alterations in the tumor microenvironment. In this study we use three distinct breast cancer models, a MDA-MB-231 xenograft model, a 4T1 syngenic model, and a transgenic model using MMTV-PyMT mice, to explore the effects of various anti-VEGF therapies on tumor vasculature, immune cell infiltration, and cytokine levels. Tumor vasculature and immune cell infiltration were evaluated using immunohistochemistry. Cytokine levels were evaluated using ELISA and electrochemiluminescence. We found that blocking the activation of VEGF receptor resulted in changes in intra-tumoral cytokine levels, specifically IL-1β, IL-6 and CXCL1. Modulation of the level these cytokines is important for controlling immune cell infiltration and ultimately tumor growth. Furthermore, we demonstrate that selective inhibition of VEGF binding to VEGFR2 with r84 is more effective at controlling tumor growth and inhibiting the infiltration of suppressive immune cells (MDSC, Treg, macrophages) while increasing the mature dendritic cell fraction than other anti-VEGF strategies. In addition, we found that changes in serum IL-1β and IL-6 levels correlated with response to therapy, identifying two possible biomarkers for assessing the effectiveness of anti-VEGF therapy in breast cancer patients

Hybrid Time-series Representation for the Classification of Driving Behaviour

The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for providing a personalized view, or 'fingertip', of each driver, useful in driving assistance and car insurance industry. Intuitively, an aggressive driving style manifests itself in the long run, with distinct frequencies of occurrence for time-series patterns and critical events, such as accelerations, brakings and turnings. In this work, we consider a hybrid classification method, which employs both RNN-guided time-series encoding and rule-guided event detection. Histograms derived from the output of these two components are merged, normalized and used to train a standard perceptron to classify overall driving behavior as normal or aggressive. Experimental evaluation on a publicly available dataset of sensor measurements obtained for various drivers and routes, lead to the conclusion that both RNN-guided and rule-guided components contribute to the obtained classification accuracy. © 2020 IEEE