Publications by authors named "J W Blaney"

Gβγ subunits mediate many different signaling processes in various compartments of the cell, including the nucleus. To gain insight into the functions of nuclear Gβγ signaling, we investigated the functional role of Gβγ signaling in the regulation of GPCR-mediated gene expression in primary rat neonatal cardiac fibroblasts. We identified a novel, negative, regulatory role for the Gβγ dimer in the fibrotic response.

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Background: Following concerns regarding the emergence of infection in 2010, we changed antibiotic prophylaxis in patients undergoing primary hip and knee arthroplasty from cefuroxime to flucloxacillin and single-dose (SD) gentamicin. A subsequent perceived increase in the incidence of post-operative acute kidney injury (AKI) led us to evaluate the AKI incidence between different prophylactic antibiotic regimes used at our centre.

Methods: We examined the incidence of AKI as defined by Kidney Disease: Improving Global Outcomes criteria in 1588 patients undergoing primary hip or knee arthroplasty from January 2010 to January 2015.

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Developing research self-efficacy is an important part of doctoral student preparation. Despite the documented importance of research self-efficacy, little is known about the progression of doctoral students' research self-efficacy over time in general and for students from minoritized groups. This study examined both within- and between-person stability of research self-efficacy from semester to semester over 4 years, focusing on doctoral students in biological sciences ( = 336).

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Background: Total knee arthroplasty (TKA) aims to relieve pain and improve physical functioning of the knee, however, some patients continue to experience pain and impaired function following TKA which cannot be explained by surgical and implant factors. Psychological factors may influence the outcomes of TKA. The aim of this prospective study was to examine the psychosocial factors that predicted pain, stiffness and physical functioning up to one year following TKA.

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Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines.

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